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City of Jerez Urban Core Densification proposal; An Agent Based Spatio-temporal model
Modelling urban population allocation
Elke Sauter
Julia Úbeda
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Who we are & why we are here • Masters programme:
– Geographical Information Management and Applications (GIMA)
– Joint initiative between four Dutch Universities
• Broader context: Msc project as a pilot of a PhD
“Advanced planification model, new ICT and
their applicability in urbanism: Spatial
information, scenario simulation and impact
evaluation”- Irene Luque Irene Luque
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Content
• Problem Definition
• Why to model?
• Question to be answered
• Agent-based modeling
• Modeling exercise: conceptualization and implementation
• Results, conclusions and discussion
3
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Problem Definition
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5
Jerez de la Frontera
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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National Statistical Institute INE (Instituto Nacional de Estadística)
Demographics: 2013 - 2029
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Where to accommodate this
incoming population?
Jerez Municipality drafted an Expansion Plan
7
Aiming to control the population allocation
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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What does expansion actually mean for a city like Jerez?
8
Source: byyourbesttraveler.com
• Huge investment in new infrastructure – higher taxes to pay by its citizens
• Degradation of valuable ecosystems and economic drivers in the region
• Loss of the sense of community + reduce liveability of the inner city
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Alternative
Local Urban Planners, on behalf of citizens, support the densifying scenario – taking advantage of the
vacant space existing in the inner city
9 Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Why to model?
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Why to model? – What can be done?
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Real necessity of making the solution visual & understandable
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Leads to…
Informed Stakeholders
Municipality Jerez citizens
Better and supported decisions
Municipality
Why to model? – What can be done?
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Research Question
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Question to be answered: Expand or densify?
To what extent can the Jerez inner city accommodate the upcoming population?
AND
When should the city expansion plan be implemented to guarantee an optimal functioning of the existing city?
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Timing WHEN?
Population allocation WHERE?
Circumstances HOW?
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Agent Based Model
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What is an agent?
single autonomous entity
expresses behavior by making decisions by taking actions
to meet their objectives
-Agarwal et al., 2002; Joffre et al., 2015
1
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Agent-Based Modelling
A computational tool to simulate:
Complex systems in non-computing related scientific domains: biology, ecology & social science
Dynamically interacting components Emergence - can give rise to collective behavior -New England Complex Systems Institute (n.d.)
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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AB models for urban planning – in the frame of the PhD
• Heterogeneous stakeholders diverse opinions
• varying viewpoints
-Joffre et al. (2015)
• Behavior • Decision making processes
Complex systems:
-Salvini et al. (2016)
Contexts scenarios
B A
C
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Gama
Uses the GAML agent-oriented programming language
Connects GIS-based data to populate the model
Enables ‘agentification’ capabilities for geo-data
Agent Geo-data
Census Units (CU)= agent
CU=neighborhood
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Modelling exercise
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Steps
5. Talk to lawmakers
4. Validate
3. Implementation
2. Urban indicators
1. Setting goals
Conceptualization
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Urban Indicators of the Built Environment
Urban Indicators
Land Use Infrastructure
Mobility Population
“An indicator quantifies and aggregates data that can be measured and monitored to determine whether change is taking place” (FAO, 2002)
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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CU attractiveness as a force to pull people in
Attractiveness
to evaluate when
urban expansion plan should be put into effect
CU#1
CU Attractiveness scores
Level of data
aggregation
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Combined CU Attractiveness
Score
Land Use Score Range
[1-5]
Population Score Range
[1-5]
Infrastructure Score Range
[1-5]
Mobility Score Range
[1-5]
Attractiveness Scores
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Attractiveness Scores: Population
0
1
2
3
4
5
6
10 20 30 40 50 60 70 80 90 100
Sco
re
% Population
% Pop. Score
0-10 2
11-20 2.75
21-30 3.5
31-40 4.25
41-50 5
51-60 3.25
61-70 2.5
71-80 1.75
81-100 1
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Attractiveness Scores: Land Use
Land Use Score
MIX 5
RES, PF, EA 3
Dynamic use of space by residents
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Attractiveness Scores: Mobility
Attractiveness Scores: Infrastructure
Not implemented!
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Agent-Based Model (Jerez Overview)
Censal Unit (CU)
Agent
Jerez City Boundary
Land Use
Mobility
Population
Infrastructure
Topology inside
Is a
Part of
Part of
Part of Part of
Part of
Environment
CU
Attributes
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Initial Data
State Variables
Vacant Space
Maximum Population
Capacity of People Population
Built Residential
Surface
Capacity Level
Calculations
Living Area
User Input
To calculate pop. score
- CU level
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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66 m2 18 m2
Living Area
Japan Spain United States 35 m2
Living Area
-Jayantha & Hui, n.d.
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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50%
Capacity Level Measure of how much something can hold
Traffic jams Infrastructure (pipelines) max/ full Crowded & busy
Urban planners can influence city operational levels!
Capacity Level
100%
Free flow cars Infrastructure (pipelines) empty Lonely feel
10%
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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2. Check
Allocation Process Overview Year Pop.
2016 2870
2017 3216
… …
CU
max attractiveness score
not reached full capacity
# people allocated
incoming population
<
CU# 17 Score: 20
CU# 5
Score: 20
4. Recalculate attractiveness scores
5. Update CU capacity
Iteration t=n
year= 2017 3. Select CU + Allocate
If
1. Incoming Population **year= year+1
**if necessary
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Results
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Population Allocation
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Results Table
Capacity Level
Year Expand
Pop. Leftover
70% 201X XX,XXX
80% 202X X,XXX
90% 202X X,XXX
100% 202X X
Capacity Level
Year Expand
Pop. Leftover
70% 2018 36,363
80% 2021 26,551
90% 2025 15,582
100% 2029 2,890
Capacity Level
Year Expand
Pop. Leftover
70% 201X XX,XXX
80% 202X X,XXX
90% 202X X,XXX
100% 202X X
Japan Spain United States 18 m2 35 m2 66 m2
Higher capacity + lower living area = densification
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Original State Year 2015
Spain- Jerez -1
25
8 14.75
3
12.5
5
10.25
2
18
4
20
4
73
6
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Spain- Jerez -2
Allocation: 70% Capacity Level Reached
Expand by year 2018
25
8 12.5
20
10
11
73
6
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Allocation: 100% Capacity Level Reached
Expand by year 2029
Spain- Jerez -3
41
8
97
6
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Conclusions & Discussion
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Conclusions & Reflections
• Living area rule and capacity levels have high impacts on the model. • Capacity levels vs. attractiveness of CU’s:
• find equilibrium between the two • be aware of the risks you want to assume • determinant of when to expand
• Urban planners/ Municipality of Jerez to evaluate whether densification is a good policy to deal with the incoming population.
• Agent based model as a tool for modelling urban complex systems.
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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• Inclusion of other urban indicators for more holistic approach:
• mobility and infrastructure
• housing prices
• demographic characterization of agents and their preferences
• Evaluating sustainability (economic, environmental and social impacts)
• Methodology can be replicated with case specific parameters
Discussion and future work
Problem definition Why to model? R. Question ABM Modelling exercise Results & discussion
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Questions?
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• Agarwal, C., Green, G. M., Grove, J. M., Evans, T. P., & Charles M. Schweik. (2002). A review and assessment of land-use change models: dynamics of space, time, and human choice, 1–61. Retrieved from http://www.geog.ucsb.edu/~kclarke/ucime/Helens-Sem/seminar2001/Land_Use_ Draft_9.pdf
• Food and Agriculture Organization of the United Nations (2002) Pressure-State-Response Framework and Environmental Indicators. Available from: http://www.fao.org/ag/againfo/ programmes/en/lead/toolbox/refer/envindi.html
• Joffre, O. M., Bosma, R. H., Ligtenberg, A., Tri, V. P. D., Ha, T. T. P., & Bregt, A. K. (2015). //***Combining participatory approaches and an agent-based model for better planning shrimp aquaculture. Agricultural Systems, 141, 149–159. http://doi.org/10.1016/j.agsy.2015.10.006
• Jayantha, W. M., & Hui, E. C. M. (n.d.). Central Europe towards Sustainable Building CESB10 Prague Assessment Methods DETERMINANTS OF HOUSING CONSUMPTION AND RESIDENTIAL CROWDING IN HONG KONG.
• New England Complex Systems Institute. (n.d.). About Complex Systems | NECSI. Retrieved April 21, 2016, from http://www.necsi.edu/guide/study.html
• Salvini, G., Ligtenberg, A., van Paassen, A., Bregt, A. K., Avitabile, V., & Herold, M. (2016). REDD+ and climate smart agriculture in landscapes: A case study in Vietnam using companion modelling. Journal of Environmental Management, 172, 58–70. http://doi.org/10.1016/j.jenvman.2015.11.060
Bibliography
Problem definition Question to asnwer Why to model? ABM Modelling exercise Results & discussion