d1.3 second periodic report - europa

D1.3 Second Periodic Report

Project acronym : INSIGHT

Project title : Innovative Policy Modelling and Governance Tools for Sustainable Post-Crisis Urban Development

Grant Agreement number : 611307

Funding Scheme : Collaborative project

Project start date / Duration: 01 Oct 2013 / 36 months

Call Topic : FP7.ICT.2013-10

Project web-site : http://www.insight-fp7.eu/

Deliverable : D1.3 Second Periodic Report

Issue 1

Date 09/11/2015

Status Approved

Dissemination Level : Confidential, only for members of the consortium

http://www.insight-fp7.eu/


November 2015

© INSIGHT Consortium Page i

Authoring and Approval

Prepared by

Name & Affiliation Position Date

Ricardo Herranz (Nommon) Scientific/Technical Coordinator 30/10/2015

Asunción Santamaría (UPM-CeDInt) Project Coordinator 30/10/2015

Iris Galloso (UPM-CeDInt) Management Coordinator 30/10/2015

Reviewed by


Asunción Santamaría (UPM-CeDInt) Project Coordinator 02/11/2015


Approved for submission to the European Commission by


Andrés Monzón (UPM-TRANSyT) Member of the General Assembly 04/11/2015

Michael Batty (CASA-UCL) Member of the General Assembly 04/11/2015

Harry Timmermans (TU/e) Member of the General Assembly 04/11/2015

José Javier Ramasco (IFISC-UIB) Member of the General Assembly 04/11/2015

Txema Nouvilas (IMI-BCN) Member of the General Assembly 04/11/2015

Iris Galloso (UPM-CeDInt) Management Coordinator 04/11/2015


Record of Revisions

Edition Date Description/Changes

Issue 1 Draft 1 30/10/2015 First full draft

Issue 1 Draft 2 04/11/2015 Addition of Publishable Summary

Minor editorial corrections

Issue 1 09/11/15 Addition of explanation of the use of resources

Approval for submission to the EC


November 2015

© INSIGHT Consortium Page ii

Second Periodic Report

Grant Agreement number: 611307

Project acronym: INSIGHT

Project title: Innovative Policy Modelling and Governance Tools for Sustainable Post-Crisis Urban

Development

Funding Scheme: Collaborative project

Date of latest version of Annex I against which the assessment will be made: 11/07/2013

Periodic report: 1st 2nd X 3rd Final

Period covered: from 1st October 2014 to 30th September 2015

Name, title and organisation of the scientific representative of the project's coordinator:

Asunción Santamaría Galdón, Director of CeDInt-UPM

CeDInt-UPM, Technical University of Madrid

Campus de Montegancedo s/n

28223 Pozuelo de Alarcón

Tel: +34 91 336 4502

Fax: +34 91 336 4501

E-mail: [email protected]

Project website address: http://www.insight-fp7.eu/

mailto:[email protected]



November 2015

© INSIGHT Consortium Page iii

Declaration by the scientific representative of the project

coordinator

I, as scientific representative of the coordinator of this project and in line with the obligations as stated in Article II.2.3 of the Grant Agreement, hereby confirm that:

The attached periodic report represents an accurate description of the work carried out in this project for this reporting period;

The project (tick as appropriate):

has fully achieved its objectives and technical goals for the period;

has achieved most of its objectives and technical goals for the period with relatively minor deviations;

has failed to achieve critical objectives and/or is not at all on schedule.

The project Website, if applicable

is up to date

is not up to date

To my best knowledge, the financial statements which are being submitted as part of this report are in line with the actual work carried out and are consistent with the report on the resources used for the project (section 5) and if applicable with the certificate on financial statement.

All beneficiaries, in particular non-profit public bodies, secondary and higher education establishments, research organisations and SMEs, have declared to have verified their legal status. Any changes have been reported under section 3.2.3 (Project Management) in accordance with Article II.3.f of the Grant agreement.

Name of scientific representative of the Coordinator: Asunción Santamaría Galdón

Date: 04/11/2015 Signature of Project coordinator

For most of the projects, the signature of this declaration could be done directly via the IT reporting tool through an adapted IT mechanism and in that case, no signed paper form needs to be sent


November 2015

© INSIGHT Consortium Page iv

Table of Contents

PUBLISHABLE SUMMARY .............................................................................................................................. 1

1. PROJECT OBJECTIVES FOR THE PERIOD ................................................................................................. 5

1.1 OBJECTIVES OF WP1 MANAGEMENT ................................................................................................................ 6

1.2 OBJECTIVES OF WP3 DATA INTEGRATION AND ANALYSIS ...................................................................................... 6

1.3 OBJECTIVES OF WP4 THEORETICAL MODELLING .................................................................................................. 6

1.4 OBJECTIVES OF WP5 MODEL INTEGRATION AND SOFTWARE IMPLEMENTATION ........................................................ 7

1.5 OBJECTIVES OF WP6 VISUALISATION TOOLS ....................................................................................................... 7

1.6 OBJECTIVES OF WP7 POLICY ASSESSMENT AND MODEL EVALUATION ...................................................................... 7

1.7 OBJECTIVES OF WP8 COMMUNICATION, DISSEMINATION AND EXPLOITATION .......................................................... 8

2. WORK PROGRESS AND ACHIEVEMENTS DURING THE PERIOD ............................................................... 9

2.1. OVERALL PROGRESS ........................................................................................................................................ 9

2.2. PROGRESS AND ACHIEVEMENTS OF WP3 'DATA INTEGRATION AND ANALYSIS' .......................................................... 9

2.2.1 WP3 Objectives ..................................................................................................................................... 9

2.2.2 WP3 Tasks ........................................................................................................................................... 10

2.2.3 Work done and main achievements of WP3 ...................................................................................... 11

2.2.4 WP3 Deliverables ................................................................................................................................ 16

2.2.5 Partners' contribution to WP3 ............................................................................................................ 16

2.2.5 Deviations from WP3 planning and corrective actions ....................................................................... 16

2.3. PROGRESS AND ACHIEVEMENTS OF WP4 'THEORETICAL MODELLING' ................................................................... 16

2.3.1 WP4 Objectives ................................................................................................................................... 16

2.3.2 WP4 Tasks ........................................................................................................................................... 17


2.3.3 WP4 Deliverables ................................................................................................................................ 23

2.3.4 Partners’ contribution to WP4 ............................................................................................................ 23


2.4. PROGRESS AND ACHIEVEMENTS OF WP5 'MODEL INTEGRATION AND SOFTWARE IMPLEMENTATION' ......................... 23

2.4.1 WP5 Objectives ................................................................................................................................... 23

2.4.2 WP5 Tasks ........................................................................................................................................... 24


2.4.4 WP5 Deliverables ................................................................................................................................ 29




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2.5. PROGRESS AND ACHIEVEMENTS OF WP6 ' VISUALISATION TOOLS' ........................................................................ 30

2.5.1 WP6 Objectives ................................................................................................................................... 30

2.5.2 WP6 Tasks ........................................................................................................................................... 30


2.5.4 WP6 Deliverables ................................................................................................................................ 31



2.6. PROGRESS AND ACHIEVEMENTS OF WP7 'POLICY ASSESSMENT AND MODEL EVALUATION' ....................................... 32

2.6.1 WP7 Objectives ................................................................................................................................... 32

2.6.2 WP7 Tasks ........................................................................................................................................... 32


2.6.4 WP7 Deliverables ................................................................................................................................ 33



2.7. PROGRESS AND ACHIEVEMENTS OF WP8 'COMMUNICATION, DISSEMINATION AND EXPLOITATION' ........................... 33

2.7.1 WP8 Objectives ................................................................................................................................... 33

2.7.2 WP8 Tasks ........................................................................................................................................... 34


2.7.4 WP8 Deliverables ................................................................................................................................ 35



2.8. SUMMARY TABLE ......................................................................................................................................... 36

3. DELIVERABLES AND MILESTONES TABLES ............................................................................................ 39

3.1. DELIVERABLES.............................................................................................................................................. 39

3.2. MILESTONES ............................................................................................................................................... 42

4. PROJECT MANAGEMENT ..................................................................................................................... 44

4.1. MANAGEMENT STRUCTURE AND RESPONSIBILITIES ............................................................................................. 44

4.2. PROJECT PLANNING, MONITORING AND CONTROL .............................................................................................. 47

4.2.1 Project planning .................................................................................................................................. 47

4.2.2 Progress monitoring and control ........................................................................................................ 48

4.2.3 Meetings ............................................................................................................................................. 49

4.2.1 Deviation management ....................................................................................................................... 51


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4.3 QUALITY MANAGEMENT ................................................................................................................................ 52

4.4 RISK MANAGEMENT ...................................................................................................................................... 52

4.5 INFORMATION AND COMMUNICATION MANAGEMENT ........................................................................................ 56

4.5.1 Internal communication within the Consortium ................................................................................ 56

4.5.2 External communication ..................................................................................................................... 56

4.5.3 Handling of sensitive/confidential data .............................................................................................. 57

4.6 USE OF FOREGROUND AND DISSEMINATION ACTIVITIES ....................................................................................... 57

5. EXPLANATION OF THE USE OF RESOURCES .......................................................................................... 58

5.1. GENERAL OVERVIEW OF PROJECT EXPENSES ...................................................................................................... 58

5.2. TOTAL EFFORT DISTRIBUTION PER WP, EFFORT DURING 1P AND 2P AND AND CUMULATIVE EFFORT DISTRIBUTION ....... 60

5.3. EFFORT DISTRIBUTION PER BENEFICIARY AND WP IN 1P AND 2P AND OVERALL EFFORT PROGRESS ............................. 60

5.4. BENEFICIARIES’ COST BREAKDOWN PER COST CATEGORY DURING 2P ..................................................................... 61

5.5. DETAILED EXPLANATION OF THE USE OF RESOURCES PER BENEFICIARY DURING 2P ................................................... 71


November 2015

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Publishable Summary

Summary of project context and objectives

Cities embody the twofold challenge currently facing the European Union: how to improve competitiveness

while achieving social cohesion and environmental sustainability. They are fertile ground for science and

technology, innovation and cultural activity, but also places where problems such as environmental pollution,

unemployment, segregation and poverty are concentrated. The overall goal of INSIGHT is to investigate how

ICT, with particular focus on data science and complexity theory, can help European cities formulate and

evaluate policies to stimulate a balanced economic recovery and a sustainable urban development. The

objectives of the project are the following:

1. to investigate how data from multiple distributed sources available in the context of the open data, the

big data and the smart city movements, can be managed, analysed and visualised to understand urban

development patterns;

2. to apply these data mining functionalities to characterise the drivers of the spatial distribution of

activities in European cities, focusing on the retail, housing, and public services sectors, and paying special

attention to the impact of the current economic crisis;

3. to develop enhanced spatial interaction and location models for retail, housing, and public services;

4. to integrate the new theoretical models into state-of-the-art simulation tools, in order to develop

enhanced decision support systems able to provide scientific evidence in support of policy options for

post-crisis urban development;

5. to develop innovative visualisation tools to enable stakeholder interaction with the new urban simulation

and decision support tools and facilitate the analysis and interpretation of the simulation outcomes;

6. to develop methodological procedures for the use of the tools in policy design processes, and evaluate

and demonstrate the capabilities of the tools through four case studies carried out in cooperation with

the cities of Barcelona, Madrid, London, and Rotterdam.

Project progress and main results achieved during year 2

The project started on 1st October 2013 and will run for 36 months. The work performed during the second year

of the project and the main results achieved are summarised as follows:

Data acquisition. The data repository built during the first project year has been enriched by integrating two

new datasets provided by the Barcelona City Council tax office: (i) prices of the real estate transactions in the

city paid in housing and small businesses locations purchases in the last five years; and (ii) level of “activity

taxes” from businesses in the city.

Visually-aided data exploration. A variety of information visualisation techniques have been used to support

the exploration of the datasets collected by the project in search of patterns on urban land usage, public

services coverage and usage, daily activity and mobility patterns, and demographic trends. After the First

Periodic Review, the five visualisations developed during year 1 were improved and enriched to increase their

usability, clarity and performance. Also, an experimental study has been conducted to analyse how the

visualisations are used and perceived by the users and to what extent they are effective in supporting the

specific tasks they were set up for. Detailed design, implementation and usability conclusions were drawn.


November 2015


Analysis of urban location patterns. INSIGHT has applied different spatial analysis techniques to identify spatial

patterns of land use, activities and public services in combination with the socio-demographic characteristics at

different parts of the city. Two approaches have been explored: spatial statistics and spatial networks.

Under the spatial statistics approach, five analyses can be differentiated. First, a cross-variable analysis including

highly thematic and spatially disaggregated variables was conducted to unveil spatial relationships that help

identify different social and economic patterns within the city. Second, the former variables were combined

with new data from the Cadastre and with a sample of georeferenced evictions to unveil general trends

concerning this phenomenon. Third, Panoramio data were explored to identify tourist behaviour patterns and

how different they are from those of the resident population. Fourth, the combination of over 350 variables

from traditional and new data-sources available for the municipality of Madrid allowed the identification of key

relationships between population groups and economic activities, as well as factors influencing the spatial

distribution of GDP in different parts of the city. Finally, a cross-city analysis was developed using Panoramio

data. Geotagged photographs taken by tourists enabled the identification and analysis of the main visual tourist

attraction areas in a city and the comparison across cities.

The spatial networks approach has explored the possibility of detecting land use directly from ICT data, by using

complex networks techniques and mobile phone records collected in Spain for two months. The method

developed has allowed the comparison of the land use organisation at different geographical scales in the most

populated urban areas of the country (Barcelona, Bilbao, Madrid, Seville and Valencia). The spatial distribution

of the average activity profile along time (hour by hour) for every cell was analysed to infer land use patterns. In

the five cities, between 98 and 100% of the cells were found to be covered by only 4 clusters, namely:

residential, business, logistics and nightlife. These results have been validated with cadastral information from

Barcelona and Madrid. The land use organisation has then been compared over the five Spanish urban areas.

Residential cells are found to be well distributed across the cities, with a maximum not very far from the centre.

Business cells appear at a similar distance as Residential, but peaking a little further. Logistics and Industry are

preferentially located in the periphery, while Nightlife cells are well distributed along the urban areas but

slightly more concentrated in the central areas.

Finally, both approaches have been combined to analyse language distribution in the cities using Twitter

geolocated data and language detection. A metric inspired by Shannon entropy has been introduced to get

quantitative insights on the spatial distributions of different number of users/languages in the cities. Relying on

this metric, the concept of “Power of Integration” has been defined to measure the ability of a city to integrate

different cultures within its boundaries.

Modelling of housing-retail-public services interaction. A stylised agent-based model has been developed with

the aim of capturing at a coarse level the interaction dynamics between housing, retail, and public services

responsible for the patterns observed in the data analysis work. The main purpose of the model is to work as a

test bed to observe the direct and lag effects of changes in one sector on the dynamics of the two other sectors,

looking at cause and effect relations between short term and long term dynamics, and helping understand the

importance of interaction in emergent phenomena that cannot be directly obtained by independently looking

at each sector. Phenomena observed in the toy model can help decide whether or not is worth including some

interactions in a more sophisticated microsimulation model. We have also analysed how the non-conventional

data sources gathered by INSIGHT, in particular mobile phone data and credit card usage records, could be used

for characterising different phenomena occurring at different scales for calibration purposes.


November 2015


Housing model. Activity-based models of travel demand require a socio-demographic profile of individuals as

input. The location of the house is a key variable as it is one of the pegs influencing the action space of

individuals. INSIGHT has developed an approach to creating a dynamic synthetic population that takes both

demographic processes and residential choice behaviour into account. Residential choice behaviour is based on

a housing model which first predicts the intention to move house as a function of socio-demographics, housing

attributes, key characteristics of activity-travel patterns, and the elapsed time since the previous move,

adjusted according to empirical information about the relationship between the stated intention to move and

actual moves. Monte Carlo simulation is then used to simulate actual housing choice, which is calibrated on

empirical distributions. The approach has been illustrated for the city of Rotterdam.

Retail model. Modelling retail location choice processes has traditionally been one of the pillars of urban

modelling. Recent work has shed empirical light to pricing strategy and location preference of retail activity.

Since the late 90s, advances in economic geography have reinvigorated the field and reorganised it, by

considering economies of scale, cross-dependencies of markets (e.g., labour, retail, housing) and forms of

imperfect competition between firms. However, apart from notable exceptions, fresh approaches have been

slow in informing state-of-the- art modelling and engaging with mainstream location choice modelling based on

discrete choice and random utility theory. INSIGHT has developed a model of consumer location choice based

on random utility theory and designed to capture internal and external economies of scale at the individual

retailer level, taking advantage of unconventional data sources that became recently available (e.g., detailed

economic activity, digital social media footprints, etc.) for calibration and validation. The novelty of the

proposed model is that it models retailers at the individual level, which opens up exciting opportunities towards

integrating the consumer location choice component with explicit retail location microsimulation models able

to get full advantage of the emerging availability of detailed data sources and incorporate complex behaviour

on price-setting, network dynamics and risk management.

Public services models. In the landscape left by the economic crisis, the delivery of efficient public services is

fundamental. INSIGHT has explored the application of different prescriptive location models to (i) analyse the

existing public service coverage in the cities of Barcelona, London, Madrid and Rotterdam, with particular focus

on the detection of social inequalities and spatial imbalances; (ii) define a set of possible measures to tackle the

identified deficiencies; and (iii) evaluate the impact of the proposed measures. We have decided to focus our

analysis on primary education. First, we have analysed the current public service coverage, considering public

and private services independently and as a whole. We have then evaluated the degree of optimisation of

current public service coverage in relation to an ideal coverage derived from the optimal location of the existing

public facilities, revealing to what extent location models may play an important or a modest role in the

improvement of the existing service. Finally, we have defined a set of possible measures to be adopted

according to the results obtained from the analyses. The application of these measures has been modelled in

the four case studies, considering the hypothetical scenario of having to reduce public services and the possible

adoption of alternative or complementary measures, such as promoting Public-Private Partnerships (PPPs) or

relocating facilities.

Improvement of urban simulation frameworks. The results of the empirical and theoretical work previously

described are currently being integrated into various simulation frameworks based on different modelling

approaches, including the agent-based models Albatross and MATSim, the spatial interaction LUTI model

SIMULACRA, and the system dynamics model MARS.


November 2015


Visualisation tools. Ongoing work on visualisation encompasses three main activities: a literature review on

visual analytics techniques applied to the comparative assessment of multi-dimensional scenarios; the

specification of the main functionalities and user interactions to be supported by the INSIGHT visual ecosystem;

and the definition of its overall architecture and main components. The usability and performance

requirements identified at this step, as well as the lessons learned from the exploratory data analysis task, are

being integrated with the features of the simulation tools used in the project and with the challenges imposed

by the common policy measures to be implemented in INSIGHT to define the workflow, functionalities and

interactions that should be enabled by the visual ecosystem. The system architecture will rely on two main

cornerstones: (i) the use of web interfaces (e.g., dashboards) to support a flexible and accessible front-end

representation of the visual analytics environment, and (ii) the use of no-SQL databases in the back-end to store

heterogeneous datasets and enable flexible, scalable and efficient data retrieval and manipulation operations.

Case studies and stakeholder workshop. At the end of its second year, INSIGHT organised a stakeholder

workshop with representatives of the four case study cities (Madrid, Barcelona, London and Rotterdam). The

workshop served to gather feedback from policy makers and practitioners about the results obtained by

INSIGHT and discuss the policy questions to be evaluated within the project case studies. Three policy measures

have been proposed to be analysed across the four cities to allow model comparison: cordon tolls, fostering

teleworking, and redensification policies. Additionally, policy questions of particular interest for each city have

been identified. Examples of these questions are the pedestrianisation of areas of the Madrid city centre;

housing capacity policies in London; the relationship between the Rotterdam harbour and the city, in particular

the new occupation for people losing their job at the harbour; and tourism demand management in Barcelona.

Prospects for year 3

The prospects for the last year comprise the completion of the enhancement of the simulation tools Albatross,

SIMULACRA, MARS and MATSim and of the visualisation tools, as well as the assessment of the newly

developed tools on the basis of their ability to support the implementation of a variety of policy measures. The

main challenge will be bringing together consistently the results achieved during the first two years through the

case studies and demonstrating the usefulness of such results on urban planning and policy assessment.

Expected results and potential impact and use

The results of INSIGHT will have an impact at different levels. At the scientific level, INSIGHT is developing new

methods of analysing spatio-temporal data for the purpose of understanding urban development patterns, as

well as improved simulation models. These outcomes have led to a significant number of peer-reviewed papers

in journals and conference proceedings. At the policy level, the results of INSIGHT are of value for urban

planning and policy assessment. The impact in this area will be evaluated through the case studies that will be

conducted in collaboration with the cities of Barcelona, Madrid, London and Rotterdam. At the innovation level,

INSIGHT is also expected to open the door for the development of innovative products and services, e.g.

decision support tools for retail based on the exploitation of non-conventional data sources. The strategy to

bring about these impacts is based on a communication and dissemination plan targeting the actors that can

benefit from, implement, or further develop the project results, including the scientific community, urban

planning practitioners, and policy makers. The INSIGHT dissemination material can be consulted at the project

website: http://www.insight-fp7.eu/



November 2015


1. Project objectives for the period

The overall goal of INSIGHT is to investigate how ICT, with particular focus on data science and complexity

theory, can help European cities formulate and evaluate policies to stimulate a balanced economic recovery and

a sustainable urban development. The objectives of the project are the following:

1. to investigate how data from multiple distributed sources available in the context of the open data, the

big data and the smart city movements, can be managed, analysed and visualised to understand urban

development patterns;

2. to apply these data mining functionalities to characterise the drivers of the spatial distribution of

activities in European cities, focusing on the retail, housing, and public services sectors, and paying special

attention to the impact of the current economic crisis;

3. to develop enhanced spatial interaction and location models for retail, housing, and public services;

4. to integrate the new theoretical models into state-of-the-art simulation tools, in order to develop

enhanced decision support systems able to provide scientific evidence in support of policy options for

post-crisis urban development;

5. to develop innovative visualisation tools to enable stakeholder interaction with the new urban simulation

and decision support tools and facilitate the analysis and interpretation of the simulation outcomes;

6. to develop methodological procedures for the use of the tools in policy design processes, and evaluate

and demonstrate the capabilities of the tools through four case studies carried out in cooperation with

the cities of Barcelona, Madrid, London, and Rotterdam.

These project-level objectives are then translated into a number of lower level objectives for the different work

packages. The INSIGHT work packages and their interdependencies are shown in Figure 1 below:

Figure 1. Work flow and interdependencies between work packages

The objectives of the different work packages for the second project reporting period (1st October 2014 - 30th

September 2015) are detailed hereafter.


November 2015


1.1 Objectives of WP1 Management

The objective of WP1 is to manage and coordinate the INSIGHT project so as to ensure the achievement of the

project goals within agreed time, cost, and quality.

1.2 Objectives of WP3 Data integration and analysis

The general purpose of WP3 is to gather, integrate and analyse the real-world data that will serve as input for

the modelling and simulation tasks to be carried out in WP4 and WP5, and for the case studies in WP7.

The specific objectives of WP3 are:

O3.1 to build a database on land use and activity patterns integrating geographical data and citizens

activity descriptors taken from both institutional (e.g. public institution open data initiatives) and non-

conventional sources;

O3.2 to uncover organisational patterns in the spatial arrangement of different economic sectors and

services, with particular focus on housing, retail, and public services;

O3.3 to study the evolution of these patterns during the unfolding of the current economic and financial

crisis;

O3.4 to develop spatial sustainability indicators linked to the mix of activities and services present in

different areas of the city;

O3.5 to compare the patterns observed in the different cities and to ponder the existence of certain

universal behaviours as well as some components specific to each city.

1.3 Objectives of WP4 Theoretical modelling

The general purpose of WP4 is to adapt urban location models to the current European socio-economic context,

in order to account for the effects of the financial crisis and render them more suitable for the assessment of

the policy questions related to economic recovery and sustainable urban development.


O4.1 to develop improved theoretical models of the interaction between housing, retail, and public

services location and its impact on land use patterns;

O4.2 to develop improved theoretical models of household location choice;

O4.3 to develop improved theoretical models of retail location choice;

O4.4 to develop improved theoretical models of public services location choice;

O4.5 to model the interaction between household, retail and public services location decisions and daily

activity-travel patterns.


November 2015


1.4 Objectives of WP5 Model integration and software implementation

The general purpose of WP5 is to develop new versions of various LUTI simulation frameworks based on

different modelling approaches to integrate the new sub models developed in WP4 and demonstrate how new

interactions within such models can be handled.


O5.1* to embed the new models of housing, retail, and public services location into the agent-based LUTI

models Albatross and UrbanSim/MATSim;

O5.2* to embed the new models of housing, retail, and public services location into the spatial

interaction LUTI model SIMULACRA;

O5.3* to embed the new models of housing, retail, and public services location into a LUTI model built by

integrating the system dynamics model MARS and the cellular automata model Metronamica.

1.5 Objectives of WP6 Visualisation tools

The general purpose of WP6 is to create an integrated visual ecosystem supporting intuitive user interaction

with the new urban simulation and decision support tools and facilitating analytical reasoning, interpretation

and communication of simulation results.

The specific objectives of WP6 within the Second Reporting Period are:

O6.1* to define the visual strategy underlying the functionalities and interactions of the visual ecosystem

and of its components;

O6.2* to design and develop a suitable and intuitive interface supporting user interaction with the

simulation tools;

O6.3* to apply suitable information visualisation and visual analytics techniques to facilitate the analysis

and interpretation of the outcomes of the simulations;

O6.4* to build up a comprehensive and coherent visual ecosystem supporting the integrated urban

planning process.

1.6 Objectives of WP7 Policy assessment and model evaluation

The general purpose of WP7 is to bring the models and tools developed within the project to the point where

stakeholders can employ them in various policy contexts to produce different tests of topical scenarios for

sustainable post-crisis urban development, as well as to evaluate and compare the suitability of the different

modelling approaches implemented by INSIGHT when tackling different types of urban policy questions.

The specific objectives of WP7 that are being addressed in year 2 are:

O7.2 to define a series of typical urban development problems which pertain to different or similar

policies for each of the four cities.

* Objectives encompassing a time period that spans beyond the Second Reporting Period.


November 2015


1.7 Objectives of WP8 Communication, dissemination and exploitation

The general purpose of WP8 is to facilitate a fruitful and efficient exchange of information with different

stakeholders, and prepare for the exploitation of the project results.

The specific objectives of WP8 for the Second Reporting Period are:

O8.1* to establish efficient communication channels with external partners, such as policy makers,

industrial actors or academic researchers working on related fields so as to gather their inputs and

feedback;

O8.2* to disseminate the findings of the project to encourage exploitation of the results.


November 2015


2. Work progress and achievements during the period

2.1. Overall progress

The INSIGHT project was launched on 1st October 2013. During the second year, the project has progressed

according to the work plan. Some minor deviations with respect to the original work plan have required some

adjustments in the planning of certain tasks; these minor deviations have been successfully recovered without

any significant impact on the project plan.

WP2 Challenges for European urban development and governance: the role of ICT, which was completed and

reviewed during the First Reporting Period, analysed urban policies, indicators, and methods and tools for urban

policy assessment based on an extensive literature review and a consultation with different stakeholders

throughout Europe. This process allowed us to identify the most relevant policy questions for European cities in

the post-crisis scenario, and helped us refine the identification of the current gaps in terms of data, theory, and

policy assessment methodologies.

The outcomes of WP2 were used to refine the research questions tackled in WP3 and WP4. WP3 Data

integration and analysis has gathered different data sets, has put in place the infrastructure to integrate,

manage, process and analyse such data, and has conducted different pieces of work oriented to analysing the

collected data and identifying patterns and trends related to the spatial organisation of the cities under study.

In parallel to the empirical work conducted in WP3, and in some cases building on it, WP4 Theoretical

modelling has proposed, developed and evaluated a set of location models for retail, housing, and public

services aiming to improve the existing models in two main directions: (i) adapting them to the current

European urban reality, especially the effects of the economic crisis, and (ii) taking advantage of some of the

new data sources collected and analysed in WP3. WP3 and WP4 constitute the core of the work conducted

during the Second Reporting Period.

The Third Reporting period will bring together the work done in WP3 and WP4 and will translate it into practical

applications: WP5 Model integration and software implementation will integrate the findings (both in terms of

new data sources and of theoretical modelling approaches) into a range of state-of-the-art urban models based

on different modelling paradigms; WP6 Visualisation tools will develop visual analytics tools to facilitate the

interpretation, analysis and communication of the simulation results; finally, WP7 Policy assessment and model

evaluation will test and evaluate the newly developed tools through a set of policy assessment case studies.

The main achievements of the different work packages are summarised in the following sections.

2.2. Progress and achievements of WP3 'Data integration and analysis'

2.2.1 WP3 Objectives

The general purpose of WP3 is to gather, integrate and analyse the real-world data that will serve as input for

the modelling and simulation tasks to be carried out in WP4 and WP5, and for the case studies in WP7.


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O3.1 to build a database on land use and activity patterns integrating geographical data and citizens

activity descriptors taken from both institutional (e.g. public institution open data initiatives) and non-

conventional sources;

O3.2 to uncover organisational patterns in the spatial arrangement of different economic sectors and

services, with particular focus on housing, retail, and public services;

O3.3 to study the evolution of these patterns during the unfolding of the current economic and financial

crisis;

O3.4 to develop spatial sustainability indicators linked to the mix of activities and services present in

different areas of the city;

O3.5 to compare the patterns observed in the different cities and to ponder the existence of certain

universal behaviours as well as some components specific to each city.

2.2.2 WP3 Tasks

In order to achieve the work package objectives, a number of specific tasks were defined. The description of

tasks and sub-tasks that have been active during the second year of the project is shown below:

WP3.1 Data acquisition

This task has as main aim the collection of land use information from three different main sources:

Open data repositories such as census, cadastral offices, etc.

Information coming from city authorities

Data coming from ICTs, including online social networks such as Twitter, cell phone records, etc.

WP3.3 Visually-aided data exploration

WP3.3 aims to identify non trivial patterns and infer behavioural and interaction rules expressed by raw data

through the use of Information Visualisation and Visual Analytics techniques. In particular, the task focuses on

applying such techniques to gain insight into:

urban land usage and its evolution during the crisis;

public services coverage and usage;

daily (mobility) patterns and their evolution during the crisis;

demographic evolution/trends.

WP3.4 Spatial statistics

The main objective of WP3.4 is to use the large datasets collected in WP3.1 to unveil some of the threats and

challenges that European cities are currently facing due to the economic crisis. The role of this task is to provide

some useful information on spatial patterns that will feed the models to be developed in subsequent work

packages. For this, we first need to understand the potential of the data collected in WP3.1, in order to design

the methodology of analysis and select the tools to perform it. WP3.4 includes the following activities:

Data exploration, quality check and data treatment

Designing the methodology for the spatial analysis


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Performing the analysis

Extracting particular and general conclusions about the spatial patterns of activities and population

WP3.5 Spatial networks analysis

The purpose of this task is to analyse the spatial organisation of land use in cities by using a network approach.

The nature of the activity developed by the citizens in different city areas is identified and its intensity

quantified. This allows us to estimate a correlation matrix or network with the land use intensities between all

the city subdivisions, study it as a network and analyse with it several aspects such as centrality, clusters, etc.

2.2.3 Work done and main achievements of WP3

WP3 has advanced in the four tasks listed above, which are strongly related to one another. Data acquisition

and infrastructure (and in particular the new datasets integrated along this year) have fed the analysis and

visualisation tasks. Furthermore, a strong collaboration between the visualisation and analysis tasks has

contributed to focus the analysis and to a better understanding of the interactions revealed by it.

WP3.1 Data acquisition

Data acquisition has continued during the second year of INSIGHT. This includes the continuation of the

collection of the datasets listed in D3.1 and the search for further information sources. In particular, two

datasets not included in D3.1 have been received from the Barcelona City Hall tax office:

Prices of the real estate transmissions in the city. These are the prices paid in housing and small

businesses locations purchases in the last five years. The information is disaggregated to the physical

address of the real estate event and reflects the last years of the crisis and the beginning of a possible

recovery in the sector. The dataset comprehends almost 2000 purchases/transfers every years in the

urban area around Barcelona.

Level of "activity taxes" by "Illa", year and type of activity. These taxes are collected by the municipality

tax offices from businesses in the city, while the "illa" is a spatial division enough to grant anonymity

but with a resolution that allows a high resolution geographical analysis.

These datasets have been integrated into INSIGHT data infrastructure and the associated security and access

measures have been implemented. The data repository is accessible from the project

website http://www.insight-fp7.eu under the intranet button.

WP3.3 Visually-aided data exploration

WP3.3 has applied information visualisation techniques to aid the exploration of the datasets collected by the

project in search of patterns (and their evolution) on urban land usage, public services coverage and usage,

daily activity and mobility patterns, and demographic trends. Five specific visualisations were developed,

concerning: population location dynamics in Barcelona; socio-demographic and economic activity evolution in

Madrid over the economic crisis; observed vs. forecasted mobility patterns in Rotterdam; mobility flows in

Barcelona; and mobility flows on London’s tube network. After the First Periodic Review these visualisations

have been improved and enriched to increase their usability, clarity and performance and to further reveal and

highlight the relations between the different socio-economic variables and the spatial location of activities.



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An experimental study was conducted to analyse how the visualisations implemented are used and perceived

by the user and to what extent they are effective in supporting the specific tasks they were set up for. On

average, the best valuated components were the data loading window and the bar chart, probably because

they were the most familiar to the users. The scores assigned to the slope chart and to the heat map varied

significantly across individuals / experimental conditions, which suggests that these valuations could be heavily

influenced by implementation-specific and/or user-related features. In our visualisations (and in particular in

VA1), interaction outperforms information presentation. Additionally, the design/implementation approach of

the data loading panel and the tooltips adopted in VA1 received higher quality ratings than those adopted in the

other visualisations, being the worst rated the implemented in VA3. Tooltips was found to be the most suitable

component to support the extraction of a specific value from the visualisation in a timely manner. Similarly, PCP

Charts were found to support the timely comparison of different values (corresponding to different times or

variables) for two or more ROIs in a more efficient manner than Heat Maps, Bar Charts or tooltips. Detailed

design and usability conclusions were drawn as well. Furthermore, the post-experiment interviews conducted

provided worthy qualitative feedback.

The outcomes of WP3.3 will support the design of more intuitive and user-friendly visual tools, both in WP6 and

beyond the project life.

WP3.4 Spatial statistics

The analysis undertaken in WP 3.4 aimed to respond a collection of research questions related to: a) the

research methodology (what and how can we use new data sources to answer INSIGHT research questions); b)

the identification of spatial relationships between different population groups and economic activities; and c)

the identification of factors that influence economic performance, which could be introduced in models used by

subsequent work-packages.

One of the objectives of this part of the research was to test the suitability of new and traditional data sources

to be combined in order to maximise their potential to unveil spatial relationships that help identify different

social and economic patterns within the city. In the first place, we have undertaken a cross-variable analysis

including highly thematic and spatially disaggregated variables. Most of them belong to traditional data-sources

that now provide much more detailed data, like census-like yearly tables (i.e. Padrón), statistics on the

residence and workplace of workers, or the census of economic activities. The combination of these data-

sources required the agreement of common spatial units, which was done with the use of a common regular

grid. All the datasets where accordingly aggregated or disaggregated (weighted by the area) to this common

spatial division, thus enabling the cross-variable analysis while minimising the Modifiable Areal Unit Problem

(MAUP). The analyses performed with these variables allowed the understanding of the socio economic

structures in different parts of the city. Still, some time allowances needed to be made in order to compare

multiple variables. For this reason the analysis is dated c. 2010 (with variables dated from 2009 to 2012).

These variables were combined with new data from the Cadastre and also with a sample of georeferenced

evictions. While cadastral information deemed significant, it was clear that a larger sample of evictions is

needed to introduce this variable in the models. Still, data on evictions allowed for the identification of general

trends. In addition, we explored Panoramio data to investigate the patterns of tourists and how different they

are from those of the resident population.


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The combination of over 350 variables from traditional and new data-sources available for the municipality of

Madrid allowed the identification of some relationships between population groups and economic activities, as

well as some factors influencing the spatial distribution of GDP, and how the influence of these factors has

different intensities in different parts of the city. The most relevant variables are those linked to the

characteristics of the working population of an area, like age and nationality. Also the presence of bars and

restaurants is an indicator of GDP values in some parts of the city. The model fits very well is some specific areas

like Tetuán and the core of the city centre to a lesser extent.

Activities related to tourism, like accommodation or travel agencies, show lower elasticity values (0.2) in

relation to GDP. Precisely, the analysis of the Panoramio dataset evidences different spatial patterns of tourists

and resident population. The lowest relation to GDP values is found in social care and education services, whose

location patterns are related to residential areas rather than economic activities.

The results obtained for the city of Madrid were useful for defining a model suitable for the whole metropolitan

area, thus potentially feeding the MARS model in WP4, taking into account the lack of information of some of

the variables. The same methodology was replicated confirming the unsuitability of global Pearson correlations

for heterogeneous areas like a metropolitan region. While in the case of the central area we were able to

identify some significant variables with the use of a correlation matrix, the exploration of variables that are

spatially correlated needs the use of the methodology proposed based on the spatial co-location of high-value

clusters. Consequently, the GWR provides a better fit model than OLS. It is interesting to observe how different

variables of the model are positively or negatively correlated with GDP values in different parts of the

metropolitan area, and how residence based variables tend to have opposite signs than activity based variables.

Finally, we developed a cross-city analysis with the use of a widespread data source as Panoramio. Geotagged

photographs taken by tourists make it possible to identify and analyse the main visual tourist attraction areas in

a city and to perform comparisons between cities. The advantage of geotagged photographs is that they can be

used as a proxy for the spatial distribution of tourists, allowing us to measure, map and make comparisons

within cities and between cities anywhere in the world, providing useful information for tourist researchers and

managers. In the present case, they made it possible to identify different patterns in the cities analysed,

reflecting a different spatial distribution of tourist resources. Areas of interest can be defined by studying the

density of tourists' photographs and the operations done with GIS. Currently, many of them are not considered

tourist attractions by the government nor by businesses (visitor planning trips editors, product and service

creators, hotel professionals, etc.). This way, all of them can know which areas are the most attractive to visitors

and what this area's characteristics are (sports or monumental venues, places with modern architecture, etc.),

fundamental aspects in order to market these destinations (Hays et al, 2013).

The data from geotagged photographs posted on social networks can be used to achieve progress in geographic

research on tourism, but present some limitations. First, the information is biased in so far as not all tourists

make use of these networks, and those who do, do so with varying degrees of intensity. Secondly, it is not

allowed to take pictures inside of some buildings, especially museums, so that this data source is more reliable

for open spaces than for indoors. Additionally the information refers exclusively to aesthetically attractive

places visited by most tourists, and does not fully reflect the attraction of other, less "photogenic" sites. This

particularly affects visually undistinguished places of business, study and shopping, which may be under-


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represented. For shopping tourism, for example, some shopping areas (street markets) do appear in these data

sources. One possible means to correct this bias might be to compare geolocated data with credit card data.

WP3.5 Spatial networks analysis

The possibility of detecting land use directly from ICT has been systematically explored in this task 3.5 using

complex networks techniques and mobile phone records collected in Spain for two months. The method

developed here allows the comparison of the land use organisation at different geographical scales in the most

populated urban areas of the country (Barcelona, Bilbao, Madrid, Seville and Valencia). To do this, the urban

areas have been divided using a spatially uniform square grid composed by small cells of 500 x 500 meters.

Each geo-referenced phone event (making or receiving calls by the users) is identified and assigned to a cell. The

activity is then defined as the number of users active in each cell per unit of time (normally one hour). Since

there are users that can make or receive more than one call per hour in different cells, their presence in the

activity has been counted in a fractional way (one over the number of events in each cell) to approximate the

time spend in each geographical unit. This process produces an average activity profile along time hour by hour

for every cell. These temporal profiles can be cross-analysed using simple Pearson correlation metrics. The

product is a matrix with the correlation for every pair of cells. Note that high correlations imply similar activity

users between the two cells and, therefore, a close land use type. The spurious correlations can be neglected

and this generates a sparse matrix that can be represented as a weighted network per city. The network

representation is especially indicated since it permits the employment of clustering techniques developed in the

context of complex networks that do not require to specify a given number of clusters.

Analysing the spatial distributions with such clustering techniques, it is found that in the five cities, between 98

and 100% of the cells are covered with only 4 clusters. In particular, each of the clusters can be associated with

a main land use: i) Residential, which is characterised by low activities from 8am to 5-6pm. For the cells

composing this group, the activity peaks around 7-8am and during the evening. In the weekend, the activity is

almost constant; ii) Business, where the activity is significantly higher during the weekdays than during the

weekends. Furthermore, it concentrates from 9am to 6-7pm; iii) Logistics/Industry, where, as for Business, the

activity is higher during the weekdays. A large peak is observed between 5am and 7am followed by a smaller

peak around 3pm; iv) Nightlife, which is characterised by high activity during the night hours (1am-4am),

especially during the weekends.

The results of this method regarding the assignation of Residential, Logistics and Business cells have been

validated with cadastral information from Barcelona and Madrid. It should be noted that the nature of the land

use information is both sources is different: while the cadastral data contains the surface devoted to each

activity (regardless of the number of users performing it), the ICT based data is produced by users directly so it

is more prone to reflect majority uses in terms of people participating. Despite the differences, both sources

agree in the main land use type of the cells a relatively high number of times (almost 60% in both cities).

The land use organisation is then compared in the city's space over the five Spanish urban areas. Normalising

the distance between the City Hall of each city by the maximum distance, Residential cells are found to be well

distributed across the cities but with a maximum not very far from the centre. Business cells appear at a similar

distance as Residential but peaking a little further. Logistics and Industry are preferentially located in the

periphery, while the Nightlife cells are well distributed along the urban areas but slightly more concentrated in

the central areas. In order to quantify land use distribution patterns, two indicators of spatial heterogeneity are


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introduced: the normalised Ripley’s K and a spatial entropy measure. The Ripley’s K indicator grows with the

geographical scale r. Similar patterns are detected throughout the whole set of cities, where most of the cities

are following a general scaling-like curve for all the land use types. The presence of a scaling is confirmed using

the spatial entropy measures E(D) in function of the lateral number of divisions over the cities (D) with a similar

behaviour of the curves across cities. The average entropy tends to zero if the land use within the divisions

becomes uniform, as occurs for instance at smallest spatial scales. On the other extreme, when the number of

divisions tends to 1, the entropy converges to a fixed value describing the full city. Finally, in order to explain

the shape of the average entropy, a model has been developed inspired by Schelling’s segregation. A land use

type is initially assigned to each of the cells of a lattice, respecting the distribution of the four types found in

each city in such a way that E(D=1) coincides with observations. A satisfaction index is defined for each cell

considering its own type and that of its neighbours. Essentially, the satisfaction increases when a cell is

surrounded by cells of the same type. This process is performed until the satisfaction reaches a stationary state.

Calibrating a single parameter γ, which measures the tolerance of Residential and Business cells to stay as

neighbouring cells, the observed K(r) and E(D) in the real urban areas can be reproduced. This work is accepted

for publication at the Royal Society Open Science journal.

After this analysis, a collaborative effort between IFISC-UIB and TRANSyT-UPM has started with the aim of

exploring the capability of ICT data to study immigrant communities in cities. This work is related to the analysis

of the language distribution in the cities by means of Twitter geo-localised data and language detection. This

common effort should provide new information about where and how the people are living, working and

enjoying the cities, in function of their spoken language. We analysed nearly eight months of geo-localised

tweets recorded over 58 out of the most populated cities in the world in order to identify residency pattern of

users according to their communication activity. The dataset analysed includes geolocated tweets since 2012

until the present. The language of each tweet is automatically detected, and paired each “resident” active user

in each city with its most probable native language. A a filtering analysis is performed as well to avoid the

detection of “fake” tweets, generated by automatic bots and/or no-human entities. The dataset of detected

users and languages has been then intersected with uniform spatial grids all over the cities, considering an

“influential area” of 50 km from the city barycentre. A metric inspired by Shannon entropy is introduced in

order to get quantitative insights on the spatial distributions of different number of users/language in the cities.

In this way, it is found that after a threshold of nearly 30 users, the metric can be considered as independent

from the number of users.

This metric has been organised to build a matrix having the cities as rows, the languages detected as columns

and the corresponding level of entropy as value. This metric can be definitely considered as a measure of the

level of integration of communities of people speaking the same language in the cities. Sorting the rows of the

matrix in descending order per city, the concept of “Power of Integration” can be introduced measuring of how

friendly a city is in integrating different cultures within its boundaries. The cities can then be classified according

to their “Power of Integration”. Three groups are found with the first group comprehending cities as London,

New York and Toronto (among others metropolis) in which several communities of immigrants live together,

although with different level of integration. Finally, a network analysis over the side of the languages alone has

been developed connecting languages with local cultures (i.e. the local language of the city) if they are

integrated in the same city (or in cities having the same cultures, e.g. urban areas in the US or UK). In this way,

the resulting network is a full directed weighted network, where languages are pointing to cultures where they

are integrated in, and the weight of the edge is directly proportional to the mean values of entropy, i.e. of


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integration. A clustering analysis is now being performed over the network to find out communities of language

and cultures that behave in similar way in both sides of the integration process (integrating and hosting) of

immigrant communities in urban area.

2.2.4 WP3 Deliverables

The deliverables of WP3 for the Second Reporting Period are:

D3.2 Analysis of Urban Location Patterns

2.2.5 Partners' contribution to WP3

UIB: coordinator of WP3, main contributor to WP3.1, WP3.2 and WP3.5

UPM: main contributor to WP3.3 and WP3.4, contributor to WP3.1

UCL: contributor to WP3.1

TU/e: contributor to WP3.1

IMI-BCN: contributor to WP3.1

2.2.5 Deviations from WP3 planning and corrective actions

The timeline of WP3.1 was extended to continue integrating new datasets into the INSIGHT data infrastructure.

Similarly, the work in WP3.3 continued during the second year to further improve and evaluate the

visualisations developed during the First Reporting Period. No other deviations from the general plan of the WP

have taken place during the second year of the project.

2.3. Progress and achievements of WP4 'Theoretical modelling'


The general purpose of WP4 is to adapt urban location models to the current European socio-economic context,

in order to account for the effects of the financial crisis and render them more suitable for the assessment of

the policy questions related to economic recovery and sustainable urban development.


O4.1: to develop improved theoretical models of the interaction between housing, retail, and public

services location and its impact on land use patterns;

O4.2: to develop improved theoretical models of household location choice;

O4.3: to develop improved theoretical models of retail location choice;

O4.4: to develop improved theoretical models of public services location choice;

O4.5: to model the interaction between household, retail and public services location decisions and daily

activity-travel patterns.


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2.3.2 WP4 Tasks

In order to achieve the work package objectives, a number of specific tasks were defined. A short description of

the ongoing tasks in WP4 is provided below:

WP4.1 Modelling of housing-retail-public services interaction

Study the interaction dynamics between housing, retail, and public services responsible for the patterns

observed in WP3.

WP4.2 Housing models

Develop improved models for household location decisions to make them more sensitive to the impact of the

financial crisis.

WP4.3 Retail models

Develop improved retail models to account for interactions with residential and workplace location and with

public services, including those interactions that depend on ICT rather than physical travel.

WP4.4 Public services models

Develop improved location-allocation models for public services, such as health care and education, to account

for the impact of the financial crisis and other structural and long term trends.


WP4.1 Modelling of housing-retail-public services interaction

WP4.1 has developed a toy model aiming to reproduce the relationships between the dynamics of the

interaction between housing, retail, and public services. The model is an agent-based model comprising three

main layers:

Environment layer. The physical environment in which the simulation takes place consists of a set of

square cells of a certain size with some properties intending to represent a diversity of socio-

demographic characteristics.

Agents’ layer. Three types of agents are considered, representing the three sectors whose interactions

are analysed: household, retail, and public service agents.

Exogenous variables. The only exogenous variable considered by the model is the number of job positions

available in industry and services other than retail, which intends to represent the overall evolution of the

economy.

Three types of agents are considered:

Household agents. The members of the household perform a series of daily/monthly activities according

to the family type and budget each household has. The election of the centres where shopping and

leisure activities take place depends on the activity centres’ attractiveness and accessibility. Every month

households may consider the option of dwelling and/or household members’ job relocation to improve


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their current situation, either by getting better jobs, by moving to a better house, or by reducing the total

travel costs. Areas with higher availability of retail or job options result in an indirect way (reducing

estimated travelled distance) more attractive in the dwelling election process. In the current version of

the model, population and households are fixed (i.e., no births, deaths, marriages or divorces are

considered).

Retail agents. Retailers evaluate their performance on a periodic basis as a function of the average

number of clients per month. If they are not profitable enough, they close. The election of retail centres

by households will determine the number of clients visiting each retail centre, thus leading to the closure

of existing options or to the appearance of new ones. The closure of a retail centre has a direct impact on

the budget of the household to which the former retail employees belong, leading to a change of the

household members’ activity diary and, in some cases, to dwelling and job relocation. When a retail

business closes, it leaves free space for new business to come. Every month new businesses appear in the

areas where there is sufficient demand for that type of business. As for business closure, business

opening has a direct impact on the budget of the households of the new employees.

Public services agents. The only public service considered by the model is public transport, modelled in

the form of an accessibility matrix. Accessibility is improved or worsened twice a year according to an

origin destination matrix produced by citizens travelled patterns and to the unemployment rate,

intending to mimic the impact of tax payment on transport infrastructure and service offer.

The model can be used to simulate different scenarios (including different spatial planning policies) by changing

the parameters of the environment layer and/or the agents’ attributes and/or the economic evolution, showing

in a coarse grained manner the coupling of the different sub-sectors (housing, retail, public transport). Being a

toy model with the purpose of exploring new approaches for the modelling of these interrelationships in a fast

and intuitive manner, the model lacks a rigorous calibration. Notwithstanding, the work performed has

explored how some of the non-conventional data sources gathered by INSIGHT could be used for characterising

different phenomena occurring at different scales and calibrating a model along the proposed principles. In

particular, an approach is proposed to use the money expenditure flows estimated from credit card records to

calibrate the retail choice model and the mobile phone records to characterise the distribution of home-work

distances and help calibrate the home and work choice models.

WP4.2 Housing models

Activity-based models of travel demand require a socio-demographic profile of individuals as input. The location

of the house is a key variable as it is one of the pegs influencing the action space of individuals. These variables

are constructed by creating a synthetic population. Current modelling approaches repeat this process on an

annual basis, jeopardising the integrity of the data.

WP4.2 has developed an approach to creating a dynamic synthetic population that takes both demographic

processes and residential choice behaviour into account. Residential choice behaviour is based on a housing

model which first predicts the intention to move house as a function of socio-demographics, housing attributes,

key characteristics of activity-travel patterns, and the elapsed time since the previous move, adjusted according

to empirical information about the relationship between the stated intention to move and actual moves. Monte

Carlo simulation is then used to simulate actual housing choice of those intending to move, calibrated on


November 2015


empirical distributions taking their housing history into account. The approach is illustrated for the City of

Rotterdam, The Netherlands.

The model specification is limited due to publicly available information but can be extended if more data would

be available.

WP4.3 Retail models

Modelling retail location choice processes has traditionally been one of the pillars of urban modelling. Recent

work has shed empirical light to pricing strategy and location preference of retail activity. However, retail

location and consumer location choice theory continue to draw heavily from fundamental ideas from von

Thunen and Hotelling, from central place theory and rent-bid theory. Since the late 90s, advances in economic

geography have reinvigorated the field and reorganised it, by considering economies of scale, cross-

dependencies of markets (e.g. labour, retail, housing) and forms of imperfect competition between firms.

However, apart from notable exceptions, fresh approaches have been slow in informing state-of-the-art

modelling and engaging with mainstream location choice modelling based on discrete choice and random utility

theory.

In D4.3 we present a model of consumer location choice, based on random utility theory and designed to

capture internal and external economies of scale at the individual retailer level. We discuss its theoretical

foundations, propose an implementation strategy, take advantage of unconventional data sources that became

recently available (e.g. detailed economic activity, digital social media footprints etc.) for calibration and

validation and review its outputs.

We have mainly used three datasets for the calibration and validation of this model. These are a combination of

formal proprietary datasets of travel behaviour and economic activity, and passively collected data sources of

digital social media footprints:

London Travel Demand Survey (LTDS). LTDS is a continuous household survey of the London area,

covering the London boroughs as well as the area outside Greater London but within the M25 motorway.

Results in this report relate to residents of the Greater London area, comprising the 32 London boroughs

and the City of London. The LTDS annual sample size is around 8,000 households in a typical year, a sum

of 65,000 households for the 2005-2013 period. LTDS captures information on households, people, trips

and vehicles. Details captured include trip purposes, modes used, trip start and end times, and the

locations of trip origins and destinations.

Valuation Office Business Rates. VOA compiles and maintains lists of rateable values of the 1.7 million

non-domestic properties in England, and the 100,000 in Wales, to support the collection of around 25

billion in business rates. The rateable value represents the agency’s estimate of the open market annual

rental value of a business/ non-domestic property; i.e. the rent the property would let for on the

valuation date, if it were being offered on the open market. The agency publishes a detailed set of

information for each property; this includes the classification of its main use (detailed breakdown into

more than 100 classes), the full address and postcode, the total area of the premise, the total rateable

value and breakdown into zones with different rateable value per square metre, and the weighted

average rateable value per square metre. This makes it possible to create a detailed map of rateable

value for any use.


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Social media spatiotemporal profiles (Twitter and Foursquare). We use two passively generated datasets.

One contains 25 million geo-located tweets collected over a period of 7 months (10/2013 to 05/2014),

and covering an area that extends beyond London and covers most of the Greater South East of England.

Each record contains the tweet coordinates, timestamp, text, language, tags etc. For this report we use

only location and time. A second one contains all Foursquare venues within the M25 motorway (300

thousand venues). Each record contains location coordinates, number of check-ins and unique visitors

since venue was registered and detailed venue category (activity type). The Foursquare venue data was

collected in December 2014.

The location choice model we present in D4.3 is based on random utility and is following the, growing in

popularity, cross-nested choice structure. The novelty of the proposed model is that it models retailers at the

individual level. This opens up exciting opportunities towards integrating the consumer location choice

component with explicit retail location microsimulation models able to get full advantage of emerging

availability of detailed data sources and incorporate complex behaviour on price-setting, network dynamics and

risk management.

The proposed model in its current form has been simplified (with no loss of generality) into assuming that all

retailers offer unique varieties of the same product. Moreover, it has been assumed that (i) all consumers have

equal disposable retail budgets regardless of their location, (ii) all trips are uni-purpose (only shopping is

considered), (iii) VOA rateable values are good indicators of floorspace rents and (iv) product prices do not vary

in space. These assumptions mean that a considerable part of the complexity of the decision making mechanism

is not represented by the model. The VOA and LTDS datasets that we are currently using have the potential to

increase the complexity of the model significantly towards removing some of the existing simplifying

assumptions, and when combined with passively collected social media datasets and formal datasets on

economic activity (e.g. Business Structure Dataset from ONS) offer sufficient detail to capture all the main

dimensions of behavioural variation.

Having said that, the basic model that we present in D4.3 remains very useful, both as the baseline example of

the proposed approach and as a benchmark; despite its simplicity, it translates a substantial amount of the

discrepancy between the modelled flows of the unconstrained location choice model and the observed rents

into estimates of internal and external economies of scale.

Looking at the — not too distant — future, passively generated datasets of human presence promise to offer

deeper insights on the dynamics of urban activities, including spatio-temporal patterns of shopping behaviour,

as long as biases associated with the temporal variation of social-media usage (i.e. preference of users to tweet

at specific times) is controlled to ensure the efficacy of passively generated datasets in generating valid

representations of travel demand and activity dynamics. If we succeed in this, the abundance of existing social

media data sources and the relentless pace in which new data are introduced, sustain the promise of accessible

and highly disaggregated spatio-temporal information for anyone who manages to overcome lack of

specification, representational biases and possibly absence of context.

WP4.4 Public services models

In the landscape left by the economic crisis, public services policies are extremely important but at the same

time are dramatically limited. On the one hand, negative socio-demographic changes and imbalances, such as


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the increasing social segregation or the rising economic inequality, must be tackled through policies oriented to

fostering public services or improving the existing ones. On the other hand, the economic decline has reduced

public resources, limiting the launching of these new services and affecting the delivery of the existing ones. In

this scenario, the delivery of efficient public services is fundamental and the location of public facilities becomes

crucial.

The work developed in the WP4.4 Public services models is described in the deliverable D4.4 Public services

models. The research explores the application of different location models, creating new approaches and

methodologies oriented to assisting policy makers and urban planners at some of the Policy Cycle stages

defined in INSIGHT D2.2 Urban Planning and governance: current practices and new challenges, such as the

analysis of the current scenario, the definition of policies and measures and the evaluation of future scenarios.

Accordingly, the main objectives have been:

To explore the use of diverse location models in order to analyse the existing public service coverage in

the INSIGHT four case studies (Barcelona, London, Madrid and Rotterdam). The analyses have a special

focus on the detection of potential social inequalities and spatial imbalances, responding to some of the

most important threats over European cities.

To perform a diagnosis, drawing conclusions from the previous analysis and leading to the adoption of

measures.

To define a set of possible measures oriented to tackle the public service deficiencies or imbalances

identified according to the previous diagnosis, and framed in the current European context of austerity

policies.

To model the application of the defined measures by applying diverse location models.

To evaluate the impact that the application of these measures would have, estimating the public service

coverage of future scenarios.

The methodology followed is based on the comparative analysis of the four case studies, which have served as

vehicles for exploring new applications of location models in the analysis and modelling of public services and

measures. We have decided to focus our analysis on primary education. First, we have analysed the current

public service coverage in a comprehensive way, considering public and private services independently and as a

whole, contemplating the analysis of different urban areas and also the diverse groups of demand. We have

then evaluated the degree of optimisation of current public service coverage in relation to an ideal coverage

derived from the optimal location of the existing public facilities, revealing to what extent location models may

play an important or a modest role in the improvement of the existing service. Finally, we have defined a set of

possible measures to be adopted according to the results obtained from the analyses. The application of these

measures has been modelled in the four case studies, considering the hypothetical scenario of having to reduce

public services and the possible adoption of alternative or complementary measures, such as promoting Public-

Private Partnerships (PPPs) or relocating facilities.

Some considerations and findings may be highlighted:

The analysis of public service should be performed considering the existence of the private one. It is not

possible to understand each of them separately, since they are complementary. Analysing just the public

system may lead us to consider that a service is poorly covered while it can be simply mostly covered by

the private sector, revealing possible inequalities. Furthermore, detecting these public-private imbalances


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is also important in the current context of the European Union, as one of the main goals is reducing the

growing social polarisation that threats many of its cities. The analysis of the four case studies reveals

significant differences in the public service coverage as well as in the overall coverage including the

private sector. These differences become more significant when considering the amount of resources

displayed per capita, evidencing the importance of different urban variables, especially the population

density.

Considering different urban areas is important in order to identify possible spatial imbalances that can

contribute to the manifestation of spatial segregation. The analysis of the central urban areas and the

peripheral one, or the spatial analysis of the whole metropolitan area in a more disaggregated way, may

reveal the existence of significantly different service coverages. This kind of analysis should be taken into

account when considering possible measures oriented to reduce public services, so that the impact in the

areas that are currently poorly covered is minimised.

Taking into account different groups of demand is also essential when analysing public services, since

most of them are mainly oriented to a specific segment of the population and also because it is relevant

to analyse specifically those groups that are more dependant of public services, as they are the ones that

could suffer more the consequences of public service cuts. The analysis of the three considered groups of

demand (Total population, Target population and Vulnerable population) reveals that, fortunately, there

are not significant imbalances in the four case studies. Furthermore, the estimated public coverage of the

Vulnerable population group was greater than the obtained for the Total population one in the four cities.

The comparison of the existing public service coverage to the ideal one, derived from the optimal location

of the existing facilities is an important analysis in order to evaluate the level of optimisation in the

distribution of facilities. The analysis also evidences the limits established by the “nature” of the city

(defined by basic characteristics such as its population density or its urban morphology) and to what

extent location models may play an important or a limited role in the improvement of the service

coverage.

With respect to modelling the potential scenarios that may result from the application of specific measures, we

would like to underline that:

The application of different location models is really useful in order to evaluate measures and propose

alternative or complementary ones. Furthermore, this is crucial in the current European context, since

these measures can minimise the effect of the application of austerity policies. Dealing with Public-

Private Partnerships (PPPs) or relocating facilities may not only minimise this effect but can even improve

the global service.

The different approaches based on the application of location models allowed us to estimate the future

scenarios that may result from the application of a specific measure and evidenced the different

consequences that this measure could have in different locations. This fact reinforces the idea that

policies can be globally launched but they must be applied through specific measures that are sensitive to

the local characteristics and particularities of each city or urban area.

Gravity-location models may be used to simulate the impact of choice by calibrating distance function.

The use of such gravity models is fundamental when estimating the real average distance to school

instead of average distances to closest school. This real distance is also crucial when estimating the

transportation mode share, particularly the walk mode share. And we must highlight that increasing the

walk mode share in trips to school is one of the common objectives in many countries.


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Although this study is focussed on the analysis of public primary education, the different approaches and

methodologies developed here can be applied to other public services, helping policy makers and urban

planners to think about, define, analyse, simulate and evaluate more sustainable and effective policies and

measures.


The deliverables of WP4 for the second reporting period are:

D4.1 Housing-Retail-Public Services Interaction Models

D4.2 Housing Location Models

D4.3 Retail Location Models

D4.4 Public Services Location Models

2.3.4 Partners’ contribution to WP4

Nommon: main Contributor to WP4.1

UIB: contributor to WP4.1

TU/e: main Contributor to WP4.2

CASA-UCL: main Contributor to WP4.3

UPM: main Contributor to WP4.4


Although it was originally planned to finish on month 21, WP4 was extended until the end of the Second

Reporting Period to complete the testing of the models and improve the quality of the deliverables. Other than

that, no significant problems or deviations from the original plan occurred in WP4.

2.4. Progress and achievements of WP5 'Model integration and software implementation'


The general purpose of WP5 is to develop new versions of various LUTI simulation frameworks based on

different modelling approaches to integrate the new sub models developed in WP4 and demonstrate how new

interactions within such models can be handled.


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O5.1† to embed the new models of housing, retail, and public services location into the agent-based LUTI

models Albatross and UrbanSim/MATSim;

O5.2* to embed the new models of housing, retail, and public services location into the spatial

interaction LUTI model SIMULACRA;

O5.3* to embed the new models of housing, retail, and public services location into a LUTI model built by

integrating the system dynamics model MARS and the cellular automata model Metronamica;

2.4.2 WP5 Tasks



WP5.1 Embedding new location sub-models into Albatross

The goal of WP5.1 is to evolve the Albatross model by plugging in the theoretical sub-models developed in WP4.

The improvements include a link with housing choice to allow the simulation of the consequences of changes in

residential location on activity-travel patterns and vice versa.

WP5.2 Embedding new location sub-models into SIMULACRA

The goal of WP5.2 is to evolve the large scale LUTI model SIMULACRA by plugging in the theoretical sub-models

developed in WP4. The improvements include the development of a more detailed representation of town

centres and their spatial distribution of land uses focused on retailing, commercial activities and

government/public services, introducing a direct feedback link from the site specific level to the aggregate

demand and supply. The improved model shall also implement sustainability indicators based on the level and

variety of retail activities.

WP5.3 Embedding new location sub-models into MARS / Metronamica

WP5.3 aims to implement the theoretical sub-models developed in WP4 into a system dynamics-cellular

automata integrated model based on MARS and Metronamica, respectively. MARS causal loop diagrams and

the associated stock and flow diagrams, as well as the local interaction rules of the Metronamica models, will be

reformulated and/or extended to implement the new interactions defined in WP4.

WP5.4 Embedding new location sub-models into UrbanSim/MATSim

The aim of WP5.4 is to evolve the large scale open source agent-based models UrbanSim and MATSim by

plugging in the theoretical models developed in WP4. The improvements include the development of interfaces

between the existing code and the new sub-models for the UrbanSim utility function of location choices and the

MATSim utility function that describe the satisfaction of the traveller. The integrated tool shall be able to

support the evaluation of the impact of an iterative short time behaviour on long term changes in land use.

† Objectives encompassing a time period that spans beyond the Second Reporting Period.


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WP5.1 Embedding new location sub-models into Albatross

To address a fundamental problem of activity-based models, the static synthetic population has been replaced

with a dynamic population synthesizer. Embedded in this synthesizer is a housing choice model. It first

estimates the relationship between housing satisfaction and the intention to move. Those households who are

predicted to move house are then involved in a search process and are simulated to move, subject to capacity

constraints in the market. Housing moves are conditional upon housing type and class (owner vs. renter). The

housing choice model allows the simulation of the consequences of changes in residential location on activity-

travel patterns and vice versa.

A binary logit model has been estimated to predict the intention to move house for each owner and housing

type class. The explanatory variables of this model include socio-demographic variables, satisfaction and

distance to work for all adult working household members. These variables represent a stronger link with

activity-based models than the usual accessibility variables. Transition probabilities pick up housing careers.

The housing clearance process is assumed to be non-competitive. Household predicted to have an intention to

move house, adjusted to empirical data, are simulated to decide whether or not to move to a house. Candidate

houses are drawn proportionally to distance decay effects and market availability. Monte Carlo simulation is

used decide whether the candidate will be selected. Simulated moves create the dynamics in the housing

market and jointly with demographic process a dynamic synthetic population.

Dynamics in the spatial distribution of housing occupancy and in personal and household characteristics trigger

changes in activity-travel patterns that are predicted by the Albatross model system of travel demand.

WP5.2 Embedding new location sub-models into SIMULACRA

CASA, UCL is in the process of evolving the strategic London LUTI model SIMULACRA by plugging in the

theoretical retail location choice sub-model developed in WP4 and described in D4.3. The improvements

include the development of a more detailed model of consumer location choice, based on random utility theory

and designed to capture internal and external economies of scale at the individual retailer level. This translates

into enhanced representation of town centres and the spatial distribution of retailing activities, introducing a

direct feedback link from the site specific level to the aggregate demand and supply, since retailers are

modelled at the individual level.

The incorporated structure opens up exciting future opportunities towards integrating the consumer location

choice component with explicit retail location microsimulation models able to get full advantage of emerging

availability of detailed data sources and incorporate complex behaviour on price-setting, network dynamics and

risk management. The improved model implements sustainability indicators based on the level and variety of

retail activities.


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WP5.3 Embedding new location sub-models into MARS / Metronamica

To achieve the general objective of this task, which is embedding new location sub-models (i.e., housing, retail

and public service) into MARS Model for Madrid, the following steps shall be completed:

1. Update of MARS model with the recent public data. The update includes the land use sub-model,

residential sub-model, workplace sub-model and transport sub-model.

2. Re-calibration of MARS using the newest mobility survey that was conducted in 2014.

3. Integration of new performance indicators that were defined in WP2 for MARS, in order to support policy

evaluation in WP7.

4. Re-develop the workplace sub-model of MARS by distinguishing three categories of land use functions:

production and two types of services, "retail" and "offices".

5. Embed the public service model into MARS.

6. Preparation of the new MARS model for simulating the policies designed for WP7, and coordination of

works with WP6 that will allow the visualisation of the outcomes.

During the second year of the project we completed the first three steps, which are described as follows.

Step 1 Model update. The previous version of MARS model has the data collected from 2004 and was

calibrated using data from 1996 and 2004. From three sources (i.e. Instituto Nacional de Estadística,

Comunidad de Madrid y Ciudad de Madrid), we have updated MARS data concerning land use and private

transport from 2004 to 2014. Moreover, we have acquired the transport survey 2014 from CRTM

(Consorcio Regional de Transportes de Madrid) on month 24; therefore we will be able to update the

whole MARS model for both private transport and public transport.

Step 2 Model re-calibration. The re-calibration of MARS model aims to adjust two coefficients that are

used in the MARS model as external variables. First, MARS considers the journey from home to work as a

shift and calculated based on the number of jobs in each area. Thus, the first data to calibrate the rate of

travel per person with reason work. The second calibration is to adjust the friction (also called

impedance) of the four modes of transport considered in the model, its costs and operating time (in the

case of motorized transport), availability of car as well as factors rush hour mode. The base year of the

calibration is 2012. It has chosen this year because of the availability of information regarding the

mobility of the Madrid thanks to the mobility survey conducted in that year.

Step 3 Indicators integration. The performance indicators were described in D2.2. These were identified

in the survey about the capacity of each indicator to measure a stated objective as well as the importance

of the objective itself. Most of the performance indicators were added in MARS (see Table 1). However,

some of them could not be included due to model limitations.

Table 1 Indicators to assess the policy measure - MARS for Madrid case study

Performance Indicators Related Objective Capability to model the indicator

Comments

Unemployment Economic growth Yes

Land prices Economic growth Yes


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Comments

Job creation Economic growth Yes

Unoccupied flats and buildings Economic efficiency Yes

Time spent travelling Economic efficiency Yes

Congestion levels Economic efficiency Yes It simply considers the current speed is less than 50% of free flow speed as congestion.

Vulnerable users injured by traffic accidents

Liveable streets and neighbourhoods

Yes

Share of the budget devoted to fundamental needs

Equity and social inclusion

Yes

Accessibility to main services in each zone

Equity and social inclusion

Yes Only the accessibility to work is available in MARS.

PT supply Equity and social inclusion

Yes As an input indicator

Traffic accidents with casualties Safety and security Yes

Fatalities occurred in traffic accidents

Safety and security Yes

% active population Stop demographic decline

Yes

% population > 60 years Stop demographic decline

No Could be added as an input indicator

% population < 25 years Stop demographic decline

No Could be added as an input indicator

Energy consumption Reduce energy consumption

Yes Only for transport section

Share of energy consumption by sector

Reduce energy consumption

No

Greenhouse Gases Emissions Reduce contribution to climate change

Yes Only for transport section

Share of GGE by sector Reduce contribution to climate change

No

Emissions of NOx and particles generated by transport modes

Reduce air pollution Yes Only for transport section


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Comments

Trends in air concentration of NOx and particles

Reduce air pollution Yes Could be calculated externally.

Noise intensity levels Reduce noise pollution Yes Only for transport section

Proportion of population living in households considering that they suffer from noise

Reduce noise pollution No

Urban density Reduce urban sprawl Yes As an input indicator

Share of the metropolitan area living in the central city

Reduce urban sprawl No

Land occupied by transport infrastructures

Reduce urban sprawl Yes As an input indicator

Share of jobs in the CBD Reduce urban sprawl Yes

Unemployment Economic growth Yes

The following steps mainly include embedding the new retail and public service location model into MARS, and

making the model ready for WP6 and WP7. The results of this part are expected to be finished in the Month 27.

While this task was originally planned to embed the new location sub-model into MARS and Metronamica,

instead of Metronamica, GIS applicant will be used for this part of work, for two main reasons: (i) we have tried

to create a "bridge" between Metronamica and MARS, however the theory behind the two models are totally

different, for example space is represented as a regular gird in Metronamica, and as a municipal zones in MARS.

There is not a way to both embed the new location model into the two models; and (ii) this GIS application is

capable of providing a platform to embed the new location sub-model, as well as express the results that

obtained by MARS model.

WP5.4 Embedding new location sub-models into UrbanSim/MATSim

Unlike in the case of the three other cities, at the beginning of the project there was not a fully operational

model of UrbanSim or MATSim for Barcelona. The work during the Second Reporting Period has therefore been

focused on the implementation of the MATSim model for Barcelona, building on the preliminary

implementation developed in the frame of the FP7 project EUNOIA. A second difference with the other models

is that, in this case, being MATSim a transport model more suitable for the evaluation of policies in the short-

term, we have decided to re-orient the work towards the integration of some of the new data sources analysed

in WP3. In particular, we have designed a method to produce realistic activity-based travel demand from mobile

phone records, and are currently building agents' activity-travel diaries based on mobile phone data recently

collected from one of the three main mobile network operators in Spain. The use of such type of data opens

promising opportunities for the calibration and validation of transport models:


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Compared to traditional household travel surveys, we can work with larger samples capturing a wider

variety of travel behaviours;

Data can be gathered and analysed in days, avoiding the problem of working with outdated information;

Unlike in the case of household travel surveys, we are able to work with data for the whole of Spain as

well as roaming data, which opens the possibility of including visitors and tourists mobility in travel

demand models in a realistic manner.

WP5.4 was originally planned to work also on an extension of UrbanSim and its implementation for Barcelona.

The complexity of the implementation and the delays in collecting the required data create a high risk that this

implementation cannot be finalised in time. For this reason, the possibility of replacing the work planned with

UrbanSim by some additional applications of MATSim is currently under study. In this respect, the opportunities

opened by new data sources to study tourism behaviour have been identified as particularly interesting in the

case of Barcelona, for which the tourism sector is especially important. The discussions held with the city

authorities during the Second Reporting Period have highlighted their interest on better understanding tourist

activity. This has led us to consider the option of extending the Barcelona case study that will be conducted in

the frame of WP7 during the Third Reporting Period by including a specific study of the use of public spaces and

public transport by tourists. This will in turn require some additional adaptation of the model to include tourists

activity-travel diaries and calibrate the MATSim utility function that describes the satisfaction of the traveller.


There are not deliverables of WP5 planned for the Second Reporting Period.


TU/e: main Contributor to WP5.1

CASA-UCL: main Contributor to WP5.2, contributor to WP 5.4

UPM: main Contributor to WP5.3

Nommon: main Contributor to WP5.4


No significant problems or deviations from the original plan occurred in WP5, except for the proposed

redefinition of the tasks WP5.3 and WP5.4 regarding Metronamica and UrbanSim, respectively. Apart from this,

the implementation work has started before the planned date and significant progress has been made during

Year 2.


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2.5. Progress and achievements of WP6 ' Visualisation tools'


The general purpose of WP6 is to create an integrated visual ecosystem supporting intuitive user interaction

with the new urban simulation and decision support tools and facilitating analytical reasoning, interpretation

and communication of simulation results.

The specific objectives of WP6 within the Second Reporting Period are:

O6.1* to define the visual strategy underlying the functionalities and interactions of the visual ecosystem

and of its components;

O6.2* to design and develop a suitable and intuitive interface supporting user interaction with the

simulation tools;

O6.3* to apply suitable information visualisation and visual analytics techniques to facilitate the analysis

and interpretation of the outcomes of the simulations;

O6.4* to build up a comprehensive and coherent visual ecosystem supporting the integrated urban

planning process.

2.5.2 WP6 Tasks



WP6.1 Visual ecosystem

WP6.1 deals with the functional specification and design of the visualisation platform, including: inputs from

and/or interfaces with the new urban simulation and decision support tools, general architecture and visual

analysis tools supporting the multi-dimensional evaluation of a policy option, the comparison among policy

alternatives at a given location and the comparative assessment of the impact of a given policy across locations.


WP6.1 Visual ecosystem

The work under development in WP6.1 encompasses three main steps: a literature review on visual analytics

techniques applied to the comparative assessment of multi-dimensional scenarios; the specification of the main

functionalities and user interactions to be supported by the visual ecosystem; and the definition of its overall

architecture and main components.

The literature review focused on gaining insight into previous works dealing with the application of visual

analytics techniques to multi-dimensional scenario analysis. The aim was to identify (in particular, from

empirical outcomes) those features recommended in previous work to be used for a proper visual inspection of

a multi-dimensional space. At the same time, the literature review helped to further define the main challenges

to address the problem of a meaningful comparison among different policy alternatives and how well these

meet the policy targets. In this sense, a visual analytics tool has to explicitly deal with a proper representation of


November 2015


uncertainty and objective trade-offs; to allow the exploration of alternative policy options and link them to the

corresponding simulation parameter spaces; to provide filters and ranking options; and to clearly display how

good an alternative is with respect to other options and its effectiveness in achieving the pursued objectives.

The usability and task performance requirements identified in the literature review, as well as the lessons learnt

from the exploratory data analysis task, are integrated with the features of the simulation tools used in the

project and with the challenges imposed by the common policy measures to be implemented in INSIGHT to

define the workflow, functionalities and interactions that should be implemented by the visual ecosystem.

Additionally, some high-level functionalities and interactions to be supported by the visual ecosystem along the

policy analysis process are: loading data (including the capability to choose a specific scenario and city);

choosing the analysis mode (namely, policy analysis at a single location or cross-assessment through different

locations); data filtering and querying; switching across different representations; ranking; highlighting single or

a group of elements; brushing and linking across different data views; and displaying extra textual information.

As regards the general architecture of the visual ecosystem, two main cornerstones have been identified. These

deal with the use of web interfaces (for instance, designed as dashboards) to support the front-end

representation of the visual analytics environment; and with the use of no-SQL databases in the back-end to

store heterogeneous data. The first choice is motivated by the need to provide an interactive and widespread

environment, where users can easily interplay with the visual components representing the relevant concepts

and relationships in the scenario analysis. Likewise, by relying on the no-SQL paradigm, a proper storage of

heterogeneous datasets (as those coming from different simulators) will be ensured without losing flexibility,

scalability and maintainability. Furthermore, this approach is expected to enable a high performance data

retrieving and manipulation.




UPM-CeDInt: main Contributor to WP6.1



No significant problems or deviations from the original plan have occurred in WP6.


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2.6. Progress and achievements of WP7 'Policy assessment and model evaluation'


The general purpose of WP7 is to bring the models and tools developed within the project to the point where

stakeholders can employ them in various policy contexts to produce different tests of topical scenarios for

sustainable post-crisis urban development, as well as to evaluate and compare the suitability of the different

modelling approaches implemented by INSIGHT when tackling different types of urban policy questions.

Although WP7 was originally planned to start on Month 24, it has been kick-started several months before in

order to ensure full consistency of the model developments conducted in WP5.

The specific objectives of WP7 that have been addressed during the Second Reporting Period are:

O7.2 to define a series of typical urban development problems which pertain to different or similar

policies for each of the four cities.

2.6.2 WP7 Tasks

WP7 includes the following tasks:

WP7.1 Case study 1: Madrid

WP7.2 Case study 2: London

WP7.3 Case study 3: Rotterdam

WP7.4 Case study 4: Barcelona

WP7.5 Models and tools comparative evaluation and impact assessment

A short description of the ongoing work is provided below.


During the Second Reporting Period, three policy measures have been proposed to be analysed across the four

cities in order to allow model comparison:

Cordon tolls

Teleworking

Redensification

The three policies have been selected on the basis of the challenges and policy trends identified in WP2, and a

preliminary assessment of the capabilities of each model. A deeper analysis is now ongoing in order to assess

the detailed modelling approach for each policy and the capabilities and limitations of each simulation tool

regarding the indicators that can be evaluated.

During the Stakeholder Meeting held in Madrid on 6th October 2015, the proposed policy measures were

discussed with representatives from the different case study cities. Additionally, it was proposed to

complement the common policies included in all the case studies with some additional policy questions of


November 2015


particular interest for each city. Examples of the questions identified as relevant for each city are: (i)

pedestrianisation of areas of the Madrid city centre; (ii) housing capacity policies in London; (iii) the relationship

between the Rotterdam harbour and the city, in particular the new occupation for people losing their job at the

harbour; and (iv) tourism demand management in Barcelona.




UPM-TRANSyT: coordinator of WP7 and main contributor to WP7.1

CASA-UCL: main contributor to WP7.2

TU/e: main contributor to WP7.3

Nommon: main contributor to WP7.4


UPM-CeDInt: main contributor to WP7.5


WP7 has been kick-started before the date originally planned.

2.7. Progress and achievements of WP8 'Communication, dissemination and exploitation'


The general purpose of WP8 is to facilitate a fruitful and efficient exchange of information with different

stakeholders, and prepare for the exploitation of the project results.


O.8.1 to establish efficient communication channels with external partners, such as policy makers,

industrial actors or academic researchers working on related fields so as to gather their inputs and

feedback;

O.8.2 to disseminate the findings of the project to encourage exploitation of the results;

O.8.3 to guarantee the continuation of the research avenues opened by INSIGHT after the end of the

project.


November 2015


2.7.2 WP8 Tasks

In order to achieve the work package objectives, a number of specific tasks were defined. The description of

tasks and sub-tasks is shown below:

WP8.1 Direct dissemination

Carry out a set of communication and dissemination actions, such as bilateral meetings or presentations,

specifically targeted at individuals with a known interest and in a position to use the information

WP8.2 Indirect dissemination

Perform a set of communication and dissemination actions targeted at a broader audience, including:

the INSIGHT website;

presence in web 2.0 and social networking tools, e.g. professional networks such as LinkedIn;

publications;

presentation of key project findings at conferences and industry/academic meetings and workshops;

the INSIGHT Summer School;

the INSIGHT Final Event.

WP8.3 Coordination with Global Systems Science

Explore the connection between the research embodied by INSIGHT and Global System Science (GSS).

GSS is an initiative of DG CONNECT - Digital Science Unit for the study of global systems, aiming at

overcoming the shortcomings of existing models in support of decision making and improving the

interaction between scientific modellers and societal actors through the use of ICT tools. Both objectives

are fully relevant for INSIGHT. This task will explore the link between INSIGHT and GSS, in particular

regarding simulation.


WP8.1 Direct dissemination

A number of meetings with individuals with a known interest and in a position to use the project results have

been held during Year 2, including several bilateral meetings with representatives from the four INSIGHT cities

as well as a specific Stakeholder Workshop held in Madrid on 6 October 2015 with the aim of discussing and

selecting the policy questions to be investigated in WP7 case studies.

WP8.2 Indirect dissemination

A project website (www.insight-fp7.eu) was created at the beginning of the project to provide a unique point of

entry to the activities and results of the INSIGHT project to the research community, practitioners, policy

makers and general public. As of 30 September 2015, the website has had 5864 visits and 5698 different visitors

from 114 different countries.



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So far, the project has produced 10 scientific papers sent to peer-reviewed journals, 2 book chapters, 14

conference papers and numerous conference talks. All the publications produced by the project can be

consulted at the project website (http://www.insight-fp7.eu/#!publications/cgh8).

Additionally, INSIGHT has co-organised several dissemination events, such as CitiNet 2014 (a satellite workshop

at the European Conference on Complex Systems 2014 in Lucca, Italy), UrbanNet 2015 (a satellite workshop at

NetSci 2015 conference in Zaragoza, Spain), the International Conference on City Sciences (Shanghai, China, 4-5

June 2015) and the Symposium on Urban Systems at the Conference on Complex Systems CCS'15 held in

Tempe, Arizona in October 2015.

Finally, dissemination activities have also included an active presence presence of the project in the media as

well as in social networks, which has helped reach a wider audience and raise awareness among key

stakeholders. A LinkedIn group is in place since the beginning of the project that is periodically updated,

providing general information about the project, deliverables, news, publications, etc. The LinkedIn group has

80 members, mainly from academia and industry, and to a lesser extent from public administration. News about

the project are also regularly tweeted from the partners’ Twitter accounts.

WP8.3 Coordination with Global Systems Science

The project has also contributed to other events organised in the framework of the Global Systems Science

initiative (http://global-systems-science.eu/), to which INSIGHT is attached.

During the First Reporting Period, INSIGHT contributed to the organisation of the III GSS Conference, which took

place in Brussels on 8th-9th October 2014, by co-organising a session on urban systems.

During the Second Reporting Period, we participated in the GSS Cluster Meeting held in Brussels on 19th

November 2014, which consisted in a GSS networking and coordination event, where presentation of progress

and discussion on topics of common interest took place. More recently, at the IV GSS Conference (Genoa, 28-30

October 2015), INSIGHT organised the session ‘Shaping the future of urban systems – towards a new science of

cities’, with the purpose of exploring how data science and systems thinking can be put at work to tackle the

challenges associated to urban development.


The deliverables of WP8 for this period are:

D8.2 Second Dissemination Package


Nommon: coordination of WP8, contributor to WP8.1, WP8.2 and WP8.3

UPM, UCL, TU/e, UIB and IMI-BCN: contributors to WP8.1, WP8.2 and WP8.3


No significant problems or deviations from the original plan occurred in WP8.

http://www.insight-fp7.eu/#!publications/cgh8

http://global-systems-science.eu/


November 2015


2.8. Summary table

Table 2 summarises the level of achievement of INSIGHT objectives during the Second Reporting Period.

Table 2. Achievement of project objectives – Summary table

Work Package

Objectives for the First Reporting Period Achieved? Means of compliance

Additional information

WP3 O3.1 to build a database on land use and activity patterns integrating geographical data and citizens activity descriptors taken from both institutional (e.g. public institution open data initiatives) and non-conventional sources

Yes D3.1

O3.2 to uncover organisational patterns in the spatial arrangement of different economic sectors and services, with particular focus on housing, retail, and public services

Yes D3.2

O3.3 to study the evolution of these patterns during the unfolding of the current economic and financial crisis

Yes D3.2

O3.4 to develop spatial sustainability indicators linked to the mix of activities and services present in different areas of the city

Yes D3.2

O3.5 to compare the patterns observed in the different cities and to ponder the existence of certain universal behaviours as well as some components specific to each city

Yes D3.2

WP4 O4.1: to develop improved theoretical models of the interaction between housing, retail, and public services location and its impact on land use patterns

Yes D4.1

O4.2: to develop improved theoretical models of household location choice

Yes D4.2

O4.3: to develop improved theoretical models of retail location choice

Yes D4.3

O4.4: to develop improved theoretical models of public services location choice

Yes D4.4

O4.5: to model the interaction between household, retail and public services location decisions and daily activity-travel patterns

Yes D4.1


November 2015


Work Package



WP5 O5.1* to embed the new models housing, retail, and public services location into the agent-based LUTI models Albatross and UrbanSim/MATSim

In progress D5.1, D5.4 Work in progress in WP5.1 and WP5.4

O5.2* to embed the new models housing, retail, and public services location into the spatial interaction LUTI model SIMULACRA

In progress D5.2 Work in progress in WP5.2

O5.3* to embed the new models housing, retail, and public services location into a LUTI model built by integrating the system dynamics model MARS and the cellular automata model Metronamica;


WP6 O6.1* to define the visual strategy underlying the functionalities and interactions of the visual ecosystem and of its components


O6.2* to design and develop a suitable and intuitive interface supporting user interaction with the simulation tools

In progress D6.1, D6.2 Work in progress in WP6.1, WP6.2

O6.3* to apply suitable information visualisation and visual analytics techniques to facilitate the analysis and interpretation of the outcomes of the simulations

In progress D6.1, D6.3 Work in progress in WP6.1, WP6.3

O6.4* to build up a comprehensive and coherent visual ecosystem supporting the integrated urban planning process

In progress D6.1, D6.2, D6.3

Work in progress in WP6.1, WP6.2, WP6.3

WP7 O7.2 to define a series of typical urban development problems which pertain to different or similar policies for each of the four cities

In progress D7.1, D7.2, D7.3, D7.4

Work in progress in WP7

WP8 O8.1* to establish efficient communication channels with external partners, such as policy makers, industrial actors or academic researchers working on related fields so as to gather their inputs and feedback

Yes D8.2 O8.1 spans along the entire project life. The dissemination actions planned for the Second Reporting Period in relation to O8.1 were successfully accomplished (see D8.2).


November 2015


Work Package



O8.2* to disseminate the findings of the project to encourage exploitation of the results

Yes D8.2 O8.2 spans along the entire project life. A wide dissemination of INSIGHT outcomes within the Second Reporting Period was carried out, as detailed in D8.2.


November 2015


3. Deliverables and milestones tables

3.1. Deliverables

Table 3 presents all the deliverables due in this reporting period, as indicated in Annex I of the Grant

Agreement. Deliverables that are of a nature other than written reports, such as the database or the

dissemination material (website, workshop organisation, etc.), have been delivered accompanied by a short

report explaining their contents.


November 2015


Table 3. Deliverables due in the Second Reporting Period

Del. no.

Deliverable name

WP no.

Lead beneficiary

Nature Dissemination level

Delivery date from Annex I

Delivered (Yes/No)

Actual delivery date

Comments


1 UPM-CeDInt R CO Month 24 (1 October 2015)

Yes 4 November 2015

D3.2 Analysis of Urban Location Patterns

3 UIB R PU Month 18 (1 April 2015)

Yes 3 November 2015

Internal draft distributed in April 2015. Final version released before Second Year Review to incorporate the latest developments.

D4.1 Housing-Retail-Public Services Interaction Models

4 Nommon R PU Month 18 (1 April 2015)

Yes 3 November 2015

Internal draft distributed in September 2015. Final version released before Second Year Review to incorporate the latest developments.

D4.2 Housing Location Models

4 TU/e R PU Month 21 (1 July 2015)

Yes 30 October 2015


D4.3 Retail Location Models

4 UCL R PU Month 21 (1 July 2015)

Yes 28 October 2015



November 2015


Del. no.

Deliverable name

WP no.

Lead beneficiary

Nature Dissemination level

Delivery date from Annex I

Delivered (Yes/No)

Actual delivery date

Comments

D4.4 Public Services Location Models

4 UPM R PU Month 21 (1 July 2015)

Yes 3 November 2015


D8.2 Second dissemination package

8 Nommon O PU Month 23 (1 September

2015)

Yes 2 November 2015

Nature of the Deliverables: R = Report, P = Prototype, D = Demonstrator, O = Other

Dissemination Level: PU = Public, PP = Restricted to other programme participants (including the Commission Services), RE = Restricted to a group specified by

the Consortium (including the Commission Services), CO = Confidential, only for members of the Consortium (including the Commission Services).


November 2014


3.2. Milestones

Table 3 presents the project milestones as specified in Annex I of the Grant Agreement and their assessment

against the specific criteria defined in Annex I.


November 2015


Table 4. Milestones in the Second Reporting Period

Milestone no.

Milestone name Means of verification (from Annex I)

Delivery date from

Annex I

Achieved (Yes/No)

Actual delivery

date

Comments

MS4 Review of Data Analysis and Preliminary Review of Theoretical Models

Approval of D3.2 and D4.1 by WP3 and WP4 leaders and Project Coordinator.

Availability of D4.1, D4.2, D4.3 and D4.4 in draft form, with a level of maturity accepted by WP4 Leader and Project Coordinator.

Month 18 (1 April 2015)

Yes 29 April 2014

Draft versions of D3.2, D4.1, D4.2 and D4.4 were presented and reviewed by the whole Consortium during the INSIGHT Progress Meeting held in Eindhoven on 28-29 April 2015.

MS5 Second Interim Review (incl. Final Review of Theoretical Models and Preliminary Review of Simulation Toolset)

Approval of D4.2, D4.3 and D4.4 by WP4 Leader and Project Coordinator.

Successful user test of the initial release of the extended simulation models (D5.1, D5.2, D5.3 and D5.4).

Month 24 (30

September 2015)

Yes 11 November

2015

The pre-final versions of D3.2, D4.1, D4.2 and D4.4 were reviewed and discussed by the whole Consortium and the Advisory Board during the INSIGHT Progress Meeting held in Madrid on 5 October 2015. The progress of WP5 was also reviewed and considered as satisfactory in order to meet the deadlines established for the Third Reporting Period.

D3.2, D4.1, D4.2 and D4.4 were subsequently updated according to the comments received and their final versions are now released and approved.


November 2015


4. Project management

Project management tasks are included in WP1 Management. The objective of WP1 is to manage and

coordinate the INSIGHT project so as to ensure the achievement of the project goals within agreed time, cost,

and quality.

The partners in charge of the project management tasks are UPM and Nommon, who have implemented the

project management structure and procedures as defined in section B2.1 of the DoW.

4.1. Management structure and responsibilities

The INSIGHT project has identified specific challenges, in terms of technical, operational (day-to-day)

management, decision-making and project organisation. To accomplish these activities and aims, the

management structure shown in Figure 2 was proposed. It comprises: Project Coordinator (supported by the

Management coordinator), Deputy S/T Coordinator, Administrative/Financial Management Office, General

Assembly, Work Package Leaders and the External Experts Advisory Board.

The members of the Consortium formalised their internal work arrangements, including the agreed project

management structure and internal decision making procedures, by means of a written Consortium Agreement

that was signed in July 2013.

Figure 2. INSIGHT management structure


November 2015


UPM, as Project Coordinator, is administratively in charge of the project and is the main intermediary for any

communication between the EC and the Consortium members. The Coordinator administers the payments

received from the EC regarding its allocation between Consortium members, in accordance with the Grant

Agreement and the decisions taken by the General Assembly. The Project Coordinator shall perform, in addition

to its responsibilities as a Beneficiary, all tasks assigned to it in the Grant Agreement (GA) and the Consortium

Agreement (CA), which include the responsibility of monitoring the compliance by Consortium members with

their obligations.

The Project Coordinator also chairs the General Assembly and is involved in the Administrative and Financial

Management Office. For the fulfilment of these obligations the coordinator will be assisted by the Management

Coordinator, as well as by the Deputy Scientific/Technical Coordinator and the Administrative and Financial

Management Office for scientific/technical and administrative/financial issues, respectively.

The Deputy Scientific/Technical Coordinator performs day-to-day scientific/technical management of the

Project activities, including scientific/technical reporting and reviews and is, together with the coordinator, the

main interface with other institutions and projects.

The Administrative and Financial Management Office (AFMO) provides assistance to the Coordinator for

executing the decisions of the General Assembly. It is responsible for the day-to-day administrative and financial

management of the Project.

The General Assembly is the primary supervising and decision-making body. All beneficiaries are represented in

the General Assembly and have a voting right. The General Assembly meets twice a year with additional

conference calls every 3 months. The General Assembly is free to act on its own initiative to formulate

proposals and take decisions in accordance with the procedures set out in D1.1.

The Work Package Leaders (WPLs) coordinate the execution of the work package activities in accordance with

the present Project Management Plan (D1.1). The WPL is involved in the detailed coordination, planning,

monitoring and reporting of the WP and in the interactions with other work packages.

The External Experts Advisory Board (EEAB) is a panel of top level experts from domains such as urban

planning, social sciences, policy making, urban modeling and simulation, and data management, analysis and

visualisation. It provides, upon request, scientific / technical advice on project strategy and approach to assist

and facilitate the decisions of the General Assembly. The General Assembly may consult the EEAB in its strategic

decision-making process and in specific issues that rise in its day-to-day work. Questions can be posed to the

whole group or to individual members of the EEAB as required.

The names of the people occupying the different management positions/bodies are presented in Table 4.


November 2015


Table 5. Persons occupying the different management positions/bodies

Role Name and Contact Details

Project Coordinator / Management Coordinator Asunción Santamaría (UPM) / Iris Galloso (UPM)

Deputy Scientific/Technical Coordinator Ricardo Herranz (Nommon)

Administrative and Financial Management Office Beatriz Navarro (UPM)

Paloma Espín (UPM)

Carmen Lastres (UPM)

Mercedes Pastor (UPM)

General Assembly Iris Galloso (UPM) (Chair)

Ricardo Herranz (Nommon) (Co-chair)

Andrés Monzón (UPM)

Michael Batty (UCL)

Harry Timmermans (TU/e)

José Javier Ramasco (UIB)

Txema Nouvilas (BCN)

Work Package Leader WP1: Iris Galloso (UPM)

WP2: Ricardo Herranz (Nommon)

WP3: José Javier Ramasco (UIB)

WP4: Michael Batty (UCL)

WP5: Harry Timmermans (TU/e)

WP6: Iris Galloso (UPM)

WP7: Andrés Monzón (UPM)

WP8: Miguel Picornell (Nommon)

External Experts Advisory Board (EEAB) Mercè de Miguel i Capdevila (City of Rotterdam)

Alberto Leboreiro (Madrid Regional Government)

Gianluca Misuraca (European Commission JRC-IPTS)

Michael Wegener (Spiekermann & Wegener)

Gennady & Natalia Andrienko (Fraunhofer)

Peter Nijkamp (Vrije University)

Ajit Jaokar (Futuretext – Feynlabs, Oxford University)

Denise Pumain (CNRS)

Vittorio Loreto (Sapienza)

There have been no changes in the INSIGHT Consortium during the Second Reporting Period.


November 2015


4.2. Project planning, monitoring and control

4.2.1 Project planning

Planning activities have been carried out in in accordance with the schedule and milestones defined in the DoW

(INSIGHT Grant Agreement No 611307).

INSIGHT work plan is structured into eight work packages (WPs): six of them related to RTD activities and two

concerning management activities. The following table shows the type of activity, leading institution and

person, global budgeted effort and starting/ending dates corresponding to each Work Package.

Table 6. List of INSIGHT Work Packages

WP Nº WP Title Type of Activity

(1)

Leading Institution

WP Leader Person-months

Start End

WP1 Management MGT UPM-CeDInt

Iris Galloso 18 01 October

2013

30 September

2016

WP2

Challenges for European urban development and governance: the role of ICT

RTD Nommon Ricardo Herranz

15 01 October

2013 01 July 2014

WP3 Data integration and analysis

RTD IFISC-UIB José Javier Ramasco

39 01

November 2013

01 April 2015

WP4 Theoretical modelling

RTD CASA-UCL Michael Batty

41 01 July 2014

01 July 2015

WP5 Model integration and software implementation

RTD TU/e Harry Timmermans

42 01 April

2015 01 January

2016

WP6 Visualisation tools RTD UPM-CeDInt

Iris Galloso 26 01 July 2015

01 July 2016

WP7 Policy assessment and model evaluation

RTD UPM-

TRANSyT Andrés Monzón

53 01 October

2015

30 September

2016

WP8 Communication, dissemination and exploitation

MGT Nommon Ricardo Herranz

24 01 October

2013

30 September

2016

TOTAL 258

(1) RTD = Research and technological development; DEM = Demonstration; MGT = Management of the

Consortium


November 2015


4.2.2 Progress monitoring and control

INSIGHT progress monitoring is based on cost, time and technical criteria. Main management items are linked to

the Periodic Reports set by the EC in the Grant Agreement as shown in the List of Milestones.

Cost management

Cost management includes all the processes required to control and report the project costs. Each Consortium

member is responsible for controlling its costs and for reporting on them to the Administrative and Financial

Management Office (AFMO), in accordance with the reporting procedures defined in the Project Management

Plan (D1.1).

The AFMO has prepared specific time and financial reporting sheets for each beneficiary in order to facilitate

both its continuous budget follow up and the reporting process. It has also elaborated a “costs guide” that was

distributed among the beneficiaries in November 2013. The guide advices about common errors to be avoided

(e.g. how to calculate personnel costs, timesheets, etc.) and documentation to be kept (e.g. original boarding

cards for justifying all travels), etc.

Interim Financial Reports are required every 6 months for a continuous follow up and control of project costs and expenses.

Schedule management

Schedule management includes all the processes required to achieve timely completion of the project tasks.

The Project Coordinator, together with the Administrative and Financial Management Office (AFMO), are

responsible for maintaining the project schedule. WP Leaders shall report to the Project Coordinator any

significant deviation as soon as it is detected.

Considering the small size of INSIGHT consortium, the progress monitoring of ongoing activities is ensured by a

continuous interaction of the Project Coordinator and the Scientific/Technical Coordinator with WP leaders and

task leaders. An updated Action List is maintained accessible through the Project Information System.

During the First Reporting Period, no major modifications of the schedule have been carried out. Minor

adjustments of the schedule during project execution, as described in section 2, have been made upon

agreement between the relevant WP Leader, the Project Coordinator and the S/T Coordinator.

Reporting

INSIGHT is structured into three Periodic Reports and one Final Report as shown in Table 6.

Table 7. Tentative schedule of Project Reviews

Review number Tentative planning Venue Comments

RV1 M12 London First Project Review

RV2 M24 Brussels Second Project Review

RV3 M36 Brussels Final Project Review

https://nommon.atlassian.net/wiki/display/FP7PolicyModelling/INSIGHT+Action+List


November 2015


INSIGHT monitoring and reporting mechanisms include:

on-line Reporting from WP Leaders: on a 3-monthly basis, the WP Leaders update the status of their tasks through the Project Information System (section "Progress reporting"), including the progress achieved, the status of risks and open actions, and the key issues for the next period;

Interim Financial Report: every 6 months, all INSIGHT partners (and associated third parties) shall submit an Interim Financial Progress Report to the Administrative and Financial Management Office for a continuous follow up and control of project financial execution. Specific templates and guidelines have been provided by the AFMO to each participant to that effect;

Periodic Reports: at T0+12, T0+24 and T0+36, the Consortium shall submit a Progress Report including a publishable summary of the work progress; an explanation of the use of the resources; and a Financial Statement (Form C) from each beneficiary (and associated third parties), together with a summary financial report consolidating the claimed Community contribution;

Final Report: within one month of the end of the Contract, the Consortium shall submit a Final Report, which will comprise a final publishable summary report covering the results, impact, and conclusions of the project; a plan for the use and dissemination of foreground; and a report covering the wider societal implications of the project.

The Periodic Reports and the Final Report are prepared by the Coordinator and approved by the General

Assembly before being formally submitted to the EC.

4.2.3 Meetings

A meeting or a conference call gathering the General Assembly is held every 3 months, in order to review all

details of project execution included in the Progress Reports. Technical/scientific meetings and working sessions

are also scheduled as required to ensure a smooth and coherent development of the project. Meetings are

grouped, whenever possible, to minimise travel time and make the best possible use of travel budget.

Information about meetings (date, place, attendees, agenda and minutes) is kept available in the section

Meetings of the Project Information System. The list of the main meetings held during the Second Reporting

Period is provided as follows, including their dates, venues, attendees and a short summary of the main issues

discussed.

Table 8. List of project meetings held during the Second Reporting Period

Meeting Date Place Attendees Summary

Three-monthly progress meeting

29 January

2015

Conference call

General Assembly +

WP2, WP3 and WP4 teams

Overall status of the project + Progress reporting WP1;

Review of the EC Review report; Delivery and review schedule for D3.2 and

D4.1; Progress reporting WP3, WP4 and WP8 and

final progress reporting WP2; Objectives for the next progress meeting +

date confirmation.

https://nommon.atlassian.net/wiki/display/FP7PolicyModelling/Progress+reporting

https://nommon.atlassian.net/wiki/display/FP7PolicyModelling/Meetings


November 2015



WP7 coordination meeting

23 March 2015

Madrid (UPM)

WP7 team + Project

coordinator and S/T

coordinator

Identification of potentially interesting common policy measures to be implemented in the models, to be discussed during the Eindhoven progress meeting.

6-monthly Progress Meeting (incl. working session WP3, WP4, WP5 and WP7)

28-29 April 2015

Eindhoven (TU/e)

General Assembly + WP3, WP4,

WP5, WP6 and WP7 teams

WP1: reminder of planning for Year 2, overall progress / main issues, review of action list, second interim financial report;

Progress reporting, discussion and planning of next steps for WP3, WP4, WP5, WP6, WP7 and WP8;

Review of D3.2 and WP4 deliverables in draft form;

Preliminary discussion of potentially interesting common policy measures to be implemented in the models (cross-assessment).

Three-monthly progress meeting

27 July

2015

Conference

call

General

Assembly +

WP3, WP4,

WP5, WP6 and

WP7 teams

Progress reporting, discussion and planning of next steps for WP3, WP4, WP5, WP6, WP7 and WP8;

Preparation of the INSIGHT Stakeholder Workshop (Madrid, 6 October 2015).

INSIGHT Progress Meeting and Stakeholder Workshop

5-6 October

2015

Madrid (UPM)

General Assembly +

representatives from all INSIGHT

partners + External Experts

Advisory Board +

representatives from the case

study cities

Overall status of the project: main achievements of Year 2 and overall progress towards project objectives;

Review and discussion of WP4, WP5, WP6, WP7 and WP8 progress and achievements during year 2;

WP5, WP6 and WP7: prospects for year 3; Review and discussion of INSIGHT case

studies (draft proposal): common and city-specific policy measures;

Feedback from stakeholders and from the EEAB.


November 2015



Second Project Review

11 November

2015

Brussels (EC)

General Assembly + EC

+ Reviewers

Overall status of the project - progress objectives for Year 2 and overall progress towards project objectives: main scientific/technical achievements of the project during Year 2;

Final reports WP3 and WP4: main scientific/technical achievements, contribution to the state-of-the-art and impact;

Progress report WP5, WP6 and WP7: main scientific/technical achievements, contribution to the state-of-the-art, impact and prospects for Year 3;

Progress report WP8: use and dissemination of foreground - main achievements during Year 2;

Financial Reporting: beneficiaries’ contributions and integration within the project; difficulties found, solutions proposed and objectives for Year 3; use of resources / summary of financial statement;

Preliminary feedback from reviewers.

*The INSIGHT Stakeholder Workshop and the Second Project Review meeting (i.e. the last progress monitoring

action of Year 2) are included as part of the list of meetings corresponding to the Second Reporting Period (1

October 2014 - 30 September 2015) despite the fact that they actually take place out of this period. Thus, the

costs corresponding to the attendance to these meetings will be declared in the Financial Statement

corresponding to the Third Reporting Period.

4.2.1 Deviation management

During the project execution both technical and administrative deviations may be encountered. All members of

the Consortium are encouraged to identify deviations and make the information available as soon as a deviation

is detected. If deviations from the project planning are encountered, they are first discussed between the WP

Leader, the Project Coordinator and the Deputy Scientific/Technical Coordinator in order to implement the

corresponding recovery plan and corrective actions. If a deviation is not corrected by the involved partners

without interfering with the development of other INSIGHT tasks, an extraordinary meeting of the General

Assembly will be called and extraordinary measures will be proposed by the Coordinator and the Deputy S/T

Coordinator in order to overcome such deviation and minimise its effects.

During the Second Reporting period, no significant deviations with respect to the work plan have occurred.

Minor, day-to-day issues have been corrected successfully at WP level and have not required the intervention of

the General Assembly.


November 2015


4.3 Quality management

Quality management includes all the processes required to achieve the completion of the tasks in accordance

with the required level of quality.

The review and approval of project deliverables is an essential step to achieve high quality results. All INSIGHT

deliverables are reviewed by at least two participants not involved in its preparation and shall be approved by

the WP Leader, the Deputy Scientific/Technical Coordinator and the Coordinator except for the Project Plan, the

Periodic Reports and the Final Report that shall be approved by the General Assembly

Each INSIGHT deliverable has a Deliverable Responsible that is designated by the corresponding WP leader. The

Deliverable Responsible shall upload a complete draft of the deliverable to the corresponding Documentation

Management Section not later than three weeks prior the delivery date. The Deputy Scientific/Technical

Coordinator in agreement with the WP leader and the Deliverable Responsible shall designate two reviewers.

Within the two weeks following the availability of the draft, the reviewers should send to the Deliverable

Responsible their comments and suggestions. The Deliverable Responsible shall update the deliverable

according to the comments and modifications received and make available the reviewed version via the Project

Information System one week in advance to the delivery date. The WP Leader, Deputy Scientific/Technical

Coordinator and Coordinator shall send to the Deliverable Responsible their comments and suggestions, if any,

not later than 2 working days before the due date. After completing the review procedure the document is

approved and the definitive version shall be uploaded to the INSIGHT portal.

4.4 Risk management

Risk Management includes the processes concerned with increasing the probability and impact of positive

events and decreasing the probability and impact of adverse events. The risk management procedure

implemented in the project comprises four steps: risk identification; risk assessment; risk response; and risk

monitoring and control. Risks are identified at any moment at the initiative of any member of INSIGHT

Consortium, are reported by WP Leaders to the Project Coordinator and the S/T Coordinator and are recorded

in a Risk Register available in the Project Information System. Each WP leader shall provide a risk report at least

every three months, as part of the 3-monthly progress report. For each risk, a complete description, its cause

and the impact or consequences that it can bring to the project objectives shall be described. The risk

assessment, the mitigation actions and the contingency plan are validated by the Project Coordinator and the

S/T Coordinator. The Risk Register is continuously updated throughout the Project and is accessible to all

INSIGHT partners through the Project Information System. The Risk Register at the moment of preparing this

report is shown in Table 9. The following risk rating criteria and scale apply:

Risk rating criteria:

Likelihood and severity scales: 4-point Likert scale from 1: low to 4: high


November 2015


Table 9. Risk register

Risk ID

Risk identification Risk analysis Risk response Tracking identified risks

Risk Description (incl. cause)

Impact / Consequence

Likelihood

(from 1 to 4)

(1)

Severity (1 to 4)

(1)

Risk

Rating(2)

Risk Owner

Actions to implement / contingency plans

Actions tracking Status

INS-RI-1

Insufficient number of responses to the Stakeholder Consultation

Research questions not correctly aligned with policy needs

2 3 Medium-High

Nommon Define a list of possible respondents.

Maximise the distribution of the questionnaire.

Second round of personalised messages asking for responses

All actions were implemented as part of WP2.1 and are now completed. The number of responses was below the initial target, but still high enough to derive useful conclusions that have been documented in D2.1.

Closed

INS-RI-2

Inability / delays to collect the required data.

Delay in data analysis (WP3) and modelling (WP4) tasks

3 3 Medium-High

IFISC Working session scheduled on 19th December to identify data needs and launch data collection tasks.

Bilateral contacts and meetings with data providers

Most required data are now available to the Consortium,

Closed


November 2015


Risk ID




Likelihood

(from 1 to 4)

(1)

Severity (1 to 4)

(1)

Risk

Rating(2)

Risk Owner



INS-RI-3

The results of the data analysis in WP3 are not relevant for WP4.

Lack of consistency between WP3 and WP4.

Collected data not sufficiently exploited for the subsequent project tasks.

2 3 Medium-High

IFISC, Nommon,

UPM-TRANSyT,

CASA, TU/e

Close coordination, including specific working sessions, between the partners involved in WP3 and WP4.

Close coordination between WP3 and WP4 has taken place. Although WP3 includes some self-contained pieces of work, it has also provided several results that are being actively used in subsequent work packages.

Closed

INS-RI-4

Inability to implement all the theoretical improvements of WP4 into all the tools of WP5.

Lack of applicability of the new models in an operational context.

3 3 Medium-High

Nommon, UPM-

TRANSyT, CASA, TU/e

INSIGHT will start working from existing tools, some of them developed by members of the Consortium. Such tools will be improved in an iterative manner by adding the new modelling results.

The specificities of each simulation framework will make it almost impossible to implement all the findings of WP3 and WP4 into all the models. Notwithstanding, all the frameworks will incorporate at least some of these findings, which will lead to an improvement of the existing models and thus will still deliver valuable results.

Open


November 2015


Risk ID




Likelihood

(from 1 to 4)

(1)

Severity (1 to 4)

(1)

Risk

Rating(2)

Risk Owner



INS-RI-5

Inability to conduct a sound comparison of model capabilities across the four cities

Incomplete assessment of the capabilities of the different simulation tools

2 3 Medium-High

Nommon, UPM-

TRANSyT, CASA, TU/e

Define a set of common policy questions that can be modelled with the different simulation tools used in the project.

The definition of the policy questions has been kick-started before the originally planned data in order to mitigate this risk. Three policy questions have been pre-selected: urban cordon tolls, teleworking, and cities redensification. While it is anticipated that, due to the capabilities of each tool, a systematic comparison of the four tools across the three case studies will be very difficult, the selected policies are expected to provide a sufficiently good basis for a comprehensive assessment of the capabilities and limitations of the different tools.

Open


November 2015


4.5 Information and communication management

Information and communication management includes all the processes required to ensure timely and

appropriate collection, storage, and distribution of project information to ensure an efficient work flow, while

maintaining the required confidentiality and security procedures, as well as to achieve an efficient and

fruitful communication with other stakeholders external to the project.

4.5.1 Internal communication within the Consortium

The Project Information System is a web-based, wiki-like tool which is used to store and manage all the

information related to the project (except for sensitive data requiring a special treatment), including: Progress

Reports, Documentation Management, Meetings, INSIGHT Action List, Opportunities and Risk Management

among other.

The Project Information System provides all Consortium members with on-line, real-time visibility on all areas of

the project. The contents are updated and consulted on a regular basis by the project partners. The system has

proven effective in ensuring the storage and exchange of project information among the Consortium members.

The communication channels mirror the project management structure:

communication within a WP is the responsibility of the WP Team;

communication from a WP to the Coordinator is the responsibility of the WP Leader;

communication with the EC is the responsibility of the Project Coordinator.

Internal communication as well as knowledge sharing among partners is strongly promoted through frequent

and close interaction between teams at WP level and through working sessions and workshops at project level.

4.5.2 External communication

The Coordinator is responsible for communication with the EC. The Coordinator and the Deputy

Scientific/Technical Coordinator represent the project externally, acting as interface with other institutions and

projects. This does not exclude the possibility of bilateral meetings between other Consortium members and

stakeholders, if necessary/beneficial for the project.

The strategy for external communication and dissemination is designed around WP8.

The INSIGHT Consortium has fostered a continuous and fluent exchange of information with the research

community, industry, policy makers, and civil society, with particular attention to potential users of the project

results. External dissemination activities involve all the project partners and started from the beginning of the

project and will continue until M36.

The project website (www.insight-fp7.eu) and the LinkedIn Group EUNOIA - INSIGHT are two permanent

dissemination channels at the disposal of INSIGHT Consortium.

Any dissemination action carried out by a partner is registered in the List of Communication and Dissemination

Actions and notified to the Project Coordinator and the Deputy Scientific/Technical Coordinator in order to

include a note or reference to the action in the project website.


http://www.linkedin.com/groups?gid=4861322&trk=my_groups-b-grp-v

https://nommon.atlassian.net/wiki/display/FP7PolicyModelling/List+of+Communication+and+Dissemination+Actions

https://nommon.atlassian.net/wiki/display/FP7PolicyModelling/List+of+Communication+and+Dissemination+Actions


November 2015


4.5.3 Handling of sensitive/confidential data

The INSIGHT project is dealing with confidential / commercially sensitive information, such as questionnaires.

Confidential or commercially sensitive data is exclusively used for the purpose of the project, and can’t be

shared with any other third party without the explicit permission of the data owner, who will also specify the

security precautions to be observed.

In compliance with Article 9 of Law 15/1999 on Protection of Personal Data and consistent measures of the

Rules of Safety, INSIGHT consortium implements all the necessary technical and organisational measures to

ensure data security personal character and prevent tampering, loss, or unauthorised access.

The ethical issues namely a) informed consent b) data protection and right of privacy are taken into account and

respected. The legal framework of the three countries involved in the project is considered, ensuring it is

respected.

4.6 Use of foreground and dissemination activities

The results of INSIGHT will have an impact at different levels. The strategy to bring about these impacts is based

on a comprehensive communication and dissemination plan, targeting the actors that can benefit from,

implement, or further develop the project results, including the scientific community, urban planning

practitioners, and policy makers. The dissemination material produced by the project can be consulted at the

project website.

At the scientific level, INSIGHT is contributing to the development of new methods of integrating and mining

spatio-temporal databases for the purpose of understanding urban development patterns, as well as to the

development of improved simulation models. These scientific outcomes have already led to a significant

number of peer-reviewed papers in journals and conference proceedings.

At the policy level, the results of INSIGHT are of value for urban planning and policy assessment. While the

outcomes of WP2 include a detailed review of current practices which can be of use for urban planners and

policy makers, the main impact in this area is expected to be linked to the cases studies that will be conducted

by WP7 (in collaboration with the cities of Barcelona, Madrid, London and Rotterdam) during the third

Reporting Period.

At the innovation level, the work currently ongoing is also expected to open the door for the development of

innovative products and services, e.g. decision support tools for retail based on the exploitation of non-

conventional data sources. Nommon is already feeding some of the mobile phone data analysis algorithms

developed for WP5 and WP7 into the products and services of its subsidiary company Kineo (www.kineo-

analytics.com). Finally, other project results, such as the improved versions of Albatross, Simulacra, MARS and

MATSim and the visualisation tools developed in WP5 and WP6, respectively, can also lead to innovative

solutions.

http://www.kineo-analytics.com/

http://www.kineo-analytics.com/


November 2015


5. Explanation of the use of resources

5.1. General overview of project expenses

The costs claimed by each Beneficiary and associated Third Parties during the Second Reporting Period - 2P (M12-M24) are shown in Table 10 and Figure 3 respectively. By the end of the 2P, INSIGHT Beneficiaries have claimed costs accounting for 64% of the total budgeted costs (DoW).

Table 10. Submitted Costs 2P and cumulative budgeted costs expenditure (1P&2P)

ADJUSTMENT 1P COSTS 2P

1 UNIVERSIDAD POLITECNICA DE MADRID UPM 744.394 € 195.995 € 0 € 200.367 € 348.032 € 53%

UNIVERSIDAD COMPLUTENSE DE MADRID UCM 180.360 € 32.459 € 0 € 65.678 € 82.223 € 54%

2 NOMMON SOLUTIONS AND TECHNOLOGIES SL NOMMON 592.640 € 151.319 € 12.230 € 319.111 € 109.980 € 81%

3UNIVERSITY COLLLEGE LONDON

UNIVERSITY

COLLEGE L542.000 € 113.220 € 0 € 224.250 € 204.530 € 62%

4 TECHNISCHE UNIVERSITEIT EINDHOVEN TU/e 443.880 € 88.941 € 0 € 185.936 € 169.003 € 62%

5UNIVERSIDAD DE LES ILLES BALEARS UIB 274.821 € 63.954 € 1.062 € 149.096 € 60.709 € 78%

AGENCIA ESTATAL CONSEJO SUPERIOR

INVESTIGACIONES CIENTIFICAS CSIC 80.530 € 45.568 € 0 € 24.788 € 10.174 € 87%

6INSTITUT MUNICIPAL D'INFORMATICA DE

BARCELONA IMI 96.300 € 11.642 € 0 € 15.590 € 69.068 € 28%

2.954.925 € 703.098 € 13.292 € 1.184.815 € 1.053.719 € 64%

ACCEPTED COSTS

1P

SUBMITTED COST

2P REMAINING

BUDGET

3P

BUDGETED COSTS

EXPENDITURE

(M1-M24)

TOTAL

BENEFICIARYBUDGETED

COSTS


November 2015


Figure 3. Submitted Cost 2P vs budgeted costs per Beneficiary and Third Parties

D1.3 Second Periodic Report November 2015


5.2. Total effort distribution per WP, effort during 1P and 2P and cumulative effort distribution

The status of effort per WP after 1P & 2P is summarised in Table 11 and Figure 4.

Table 11. Effort distribution (PM) per WP

Figure 4. Effort distribution (1P & 2P) and cumulative effort vs effort planned (DoW)

5.3. Effort distribution per beneficiary and WP in 1P and 2P and overall effort progress

Each Beneficiary has dedicated a specific effort to constitute a research team adequate to the needs of the

Work Plan. The researchers incorporated to each group have allocated their effort in accordance with their

entities’ commitment to the different Work Packages. The effort has been adjusted to ensure a timely

WP

Effort (PM)

Project Plan

(DoW)

WP Duration

(months)

Effort (PM)

1P

Effort (PM)

2P

Cumulative

Effort (PM)

1P & 2PWP 1 18,0 1-36 10,87 7,99 18,86

WP 2 15,0 1-9 19,85 0,00 19,85

WP 3 39,0 2-18 29,45 32,48 61,93

WP 4 41,0 10-21 16,17 38,73 54,90

WP 5 42,0 19-27 0,00 38,50 38,50

WP 6 26,0 21-33 0,00 6,64 6,64

WP 7 53,0 25-36 0,00 6,16 6,16

WP 8 24,0 1-36 7,77 10,86 18,64

TOTAL 258 1-36 84,12 141,36 225,48



completion of the project tasks with the required level of quality and according to the project budget. Table 12

summarises the total estimated effort (in PM) per WP and Beneficiary, the actual effort spent per WP and

Beneficiary during the 2P and the cumulative effort distribution per Beneficiary by WPs in 1P and 2P.

Table 12. Total effort per WP and Beneficiary and effort during 1P & 2P

*Beneficiary with linked Third Party

5.4. Beneficiaries’ cost breakdown per cost category during 2P

Table 13 shows the cost breakdown per Beneficiary during 2P. Detailed cost breakdown of Third Parties has

been included in a separate file under their linked beneficiaries.

WP UPM* NOMMONUNIVERSITY

COLLEGE LTU/e UIB* IMI

TOTAL

CONSORTIUM

WP1 (DoW) 13 5 0 0 0 0 18

1P Effort 8,63 2,24 0,00 0,00 0,00 0,00 10,87

2P Effort 5,69 2,31 0,00 0,00 0,00 0,00 7,99

WP2 (DoW) 7 7 0 0 0 1 15

1P Effort 9,78 9,45 0,00 0,00 0,00 0,62 19,85

2P Effort 0,00 0,00 0,00 0,00 0,00 0,00 0,00

WP3 (DoW) 11 0 1 1 24 2 39

1P Effort 10,75 0,00 4,88 1,30 11,64 0,89 29,45

2P Effort 6,69 0,00 0,00 0,00 24,65 1,13 32,48

WP4 (DoW) 10 9 12 10 0 0 41

1P Effort 0,43 2,31 7,35 6,07 0,00 0,00 16,17

2P Effort 9,44 8,28 16,74 4,28 0,00 0,00 38,73

WP5 (DoW) 10 10 10 12 0 0 42

1P Effort 0,00 0,00 0,00 0,00 0,00 0,00 0,00

2P Effort 5,11 13,75 7,85 11,78 0,00 0,00 38,50

WP6 (DoW) 18 0 0 0 6 2 26

1P Effort 0,00 0,00 0,00 0,00 1,60 0,00 1,60

2P Effort 4,20 0,00 0,00 0,00 1,60 0,84 6,64

WP7 (DoW) 18 10 10 10 0 5 53

1P Effort 0,00 0,00 0,00 0,00 0,00 0,00 0,00

2P Effort 0,00 6,16 0,00 0,00 0,00 0,00 6,16

WP8 (DoW) 5 9 3 3 2 2 24

1P Effort 2,41 3,52 0,46 0,61 0,62 0,15 7,77

2P Effort 3,07 3,84 0,00 1,29 2,41 0,25 10,86

Project Effort

Planned (DoW)92 50 36 36 32 12 258

1P Effort (TOTAL) 32,00 17,52 12,70 7,98 13,86 1,66 85,71

2P Effort (TOTAL) 34,20 34,33 24,59 17,35 28,66 2,23 141,36

Project Effort progress

(%)72,0% 103,7% 103,6% 70,4% 132,9% 32,4% 88,0%



Table 13. Cost breakdown per Beneficiary and Third Parties during 2P (adjustment to FORM C of 1P included)

The distribution of budget expenditure in 2P (%) by the main Direct Cost categories is shown in Figure 5.

Figure 5. Distribution of budget expenditure in 2P by main direct cost categories

PM Total costs Adjustment Total direct costs Total indirect costs Personnel Other Travels Subcontracting

1 UPM 25,59 200.367 € - € 124.548 € 75.819 € 106.725 € 3.618 € 13.522 € 683 €

UCM 8,61 65.678 € - € 41.049 € 24.629 € 38.455 € - € 2.594 € - €

2 NOMMON 34,33 331.341 € 12.230 € 199.444 € 119.666 € 192.281 € 2.890 € 4.273 € - €

3 UCL 24,59 224.250 € 140.156 € 84.094 € 137.841 € 1.973 € 342 € - €

4 TU/e 17,35 185.936 € - € 109.398 € 76.538 € 108.126 € 140 € 1.133 € - €

5 UIB 25,66 150.158 € 1.062 € 89.633 € 59.463 € 79.094 € 1.674 € 8.866 € - €

CSIC 3,00 24.788 € - € 18.188 € 6.600 € 18.188 € - € - € - €

6 IMI 2,23 15.590 € - € 12.992 € 2.598 € 11.379 € - € 1.613 € - €

141,36 1.198.108 € 13.292 € 735.408 € 449.407 € 692.088 € 10.295 € 32.342 € 683 €

INSIGHT TOTAL COSTS - 2P

TOTAL



The following table summarizes the total effort and cost breakdown by each Beneficiary and associated Third Parties during the first 24 months of project

execution (1P+2P).

Table 14. Total effort and costs breakdown during the project execution 1P+2P

The specific distribution of Beneficiaries’ costs is shown below according to the information uploaded in NEF front-office (Project participant portal) by each

Beneficiary.

PM Total costs Adjustment Total direct costs Total indirect costs Personnel Other Travels Subcontracting

1 UPM 53,53 396.362 € - € 246.783 € 149.579 € 221.401 € 6.378 € 18.321 € 683 €

UCM 12,67 98.137 € - € 61.335 € 36.801 € 58.113 € 0 € 3.223 € - €

2 NOMMON 51,86 482.660 € 12.230 € 294.019 € 176.411 € 285.237 € 3.358 € 5.425 € - €

3 UCL 37,29 337.470 € - € 210.918 € 126.551 € 205.806 € 2.192 € 2.920 € - €

4 TU/e 25,33 274.877 € - € 166.891 € 107.986 € 164.266 € 231 € 2.395 € - €

5 UIB 31,92 214.112 € 1.062 € 127.863 € 85.187 € 113.310 € 2.672 € 11.880 € - €

CSIC 9,00 70.356 € - € 51.623 € 18.734 € 51.623 € 0 € 0 € - €

6 IMI 3,89 27.232 € - € 22.695 € 4.539 € 20.566 € 0 € 2.129 € - €

225,48 1.901.206 € 13.292 € 1.182.127 € 705.787 € 1.120.321 € 14.830 € 46.292 € 683 €

INSIGHT TOTAL COSTS - 1P + 2P

TOTAL



Beneficiary 1 – UPM

RTD ( A )

Demonstration ( B )

Management ( C )

Other ( D )

Total ( A+ B+ C + D )

Personnel costs 77.962 € 0 € 28.763 € 0 € 106.725 €

Subcontracting 0 € 0 € 683 € 0 € 683 €

Other direct costs 3.531 € 0 € 13.609 € 0 € 17.140 €

Indirect costs 54.895 € 0 € 20.924 € 0 € 75.819 €

Lump sums/flat-rate/scale of unit declared

0 €

Total 136.387 € 0 € 63.979 € 0 € 200.367 €

Maximum EC contribution 102.291 € 0 € 63.979 € 0 € 166.270 €

Requested EC contribution 166.270 €

Third Party linked to Beneficiary 1 – UCM

RTD ( A )

Demonstration ( B )

Management ( C )

Other ( D )


Personnel costs 38.455 € 0 € 0 € 0 € 38.455 €

Subcontracting 0 € 0 € 0 € 0 € 0 €

Other direct costs 2.102 € 0 € 492 € 0 € 2.594 €

Indirect costs 24.334 € 0 € 295 € 0 € 24.629 €

Lump sums/flat-rate/scale of unit declared 0 €

Total 64.891 € 0 € 788 € 0 € 65.678 €

Maximum EC contribution 48.668 € 0 € 788 € 0 € 49.456 €




Beneficiary 2 - NOMMON

RTD ( A )

Demonstration ( B )

Management ( C )

Other ( D )


Personnel costs 152.400 € 0 € 39.881 € 0 € 192.281 €



Indirect costs 92.561 € 0 € 27.105 € 0 € 119.666 €


Total 246.830 € 0 € 72.281 € 0 € 319.111 €



Adjustment to Form C 1P

RTD ( A )

Demonstration ( B )

Management ( C )

Other ( D )


Personnel costs 6.177 € 0 € 1.748 € 0 € 7.925 €


Other direct costs 0 € 0 € -281 € 0 € -281 €

Indirect costs 3.706 € 0 € 880 € 0 € 4.586 €


Total 9.883 € 0 € 2.347 € 0 € 12.230 €





Beneficiary 3 – UCL

RTD ( A )

Demonstration ( B )

Management ( C )

Other ( D )


Personnel costs 137.841 € 0 € 0 € 0 € 137.841 €



Indirect costs 84.094 € 0 € 0 € 0 € 84.094 €


Total 224.250 € 0 € 0 € 0 € 224.250 €





Beneficiary 4 – TU/e

RTD ( A )

Demonstration ( B )

Management ( C )

Other ( D )


Personnel costs 91.787 € 0 € 16.338 € 0 € 108.126 €



Indirect costs 64.972 € 0 € 11.565 € 0 € 76.538 €


Total 157.892 € 0 € 28.043 € 0 € 185.936 €





Beneficiary 5 – UIB

RTD ( A )

Demonstration ( B )

Management ( C )

Other ( D )


Personnel costs 71.488 € 0 € 7.605 € 0 € 79.094 €



Indirect costs 53.745 € 0 € 5.718 € 0 € 59.463 €


Total 127.293 € 0 € 21.802 € 0 € 149.096 €



Adjustment to Form C 1P

RTD ( A )

Demonstration ( B )

Management ( C )

Other ( D )


Personnel costs 0 €

Subcontracting 0 €

Other direct costs 1.062 € 1.062 €

Indirect costs 0 €


Total 0 € 0 € 0 € 1.062 € 1.062 €

Maximum EC contribution 0 € 0 € 0 € 1.062 € 1.062 €




Third Party linked to Beneficiary 5 – CSIC

RTD ( A )

Demonstration ( B )

Management ( C )

Other ( D )


Personnel costs 18.188 € 0 € 0 € 0 € 18.188 €


Other direct costs 0 € 0 € 0 € 0 € 0 €

Indirect costs 6.600 € 0 € 0 € 0 € 6.600 €


Total 24.788 € 0 € 0 € 0 € 24.788 €





Beneficiary 6 - IMI

RTD ( A )

Demonstration ( B )

Management ( C )

Other ( D )


Personnel costs 9.705 € 0 € 1.674 € 0 € 11.379 €



Indirect costs 2.264 € 0 € 335 € 0 € 2.598 €


Total 13.582 € 0 € 2.009 € 0 € 15.590 €





5.5. Detailed explanation of the use of resources per beneficiary during 2P

The following pages provide a detailed explanation of the Use of Resources (personnel costs, subcontracting

and other direct costs) incurred by each beneficiary, according to the information uploaded in NEF front-office

(Project participant portal) by each beneficiary.

d1.3 second periodic report - europa

Documents