distributed and heterogeneous data analysis for smart urban planning

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Distributed and heterogeneous data analysis for smart urban planning Eduardo Oliveira Michael Kirley Tom Kvan Justyna Karakiewicz Carlos Vaz

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Page 1: Distributed and heterogeneous data analysis for smart urban planning

Distributed and heterogeneous data analysis for smart urban

planning

Eduardo Oliveira Michael Kirley

Tom Kvan Justyna Karakiewicz

Carlos Vaz

Page 2: Distributed and heterogeneous data analysis for smart urban planning

Outline

•  Living  Campus  Project

•  Research  Ques:ons

•  Related  Work:  an  introduc:on  to  middleware

•  Device  Nimbus

•  Case  Study:  proof  of  concept  demonstra:on  

•  Conclusions  and  Future  work

Page 3: Distributed and heterogeneous data analysis for smart urban planning

Living  Campus

University  campuses  represent  an  urban  space  that  in  many  circumstances  reflects  what  is  happening  on  a  larger  scale  across  a  city.  

Page 4: Distributed and heterogeneous data analysis for smart urban planning

Living  Campus

Page 5: Distributed and heterogeneous data analysis for smart urban planning

Living  Campus:    an  interdisciplinary  perspec:ve

[architecture]

•  Architects,  planners,  and  urban  designers  typically  require  access  to  spa:al  and   temporal   data,   which   considers   how   people   perceive,   behave   and  interact  with  their  environment

•  Data  collec:on  and  analysis  is  rarely  pitched  at  the  `micro’  scale

[computer  science]

•  PaSS  =  People  as  Sensors

•  Large  amounts  of  data  from  social  networks,  mobile  devices  and  sensors

Page 6: Distributed and heterogeneous data analysis for smart urban planning

Guiding  research  ques:ons

Is  it  possible  to  automa:cally  collect,  combine  and  analyze  data  from  sensors  (e.g.  environmental  sensors)  and  crowd-­‐sourcing  (e.g.  using  mobile  devices)?  Can  this  data  be  stored  and  processed,  in  order  to  extract  useful  informa:on  to  aid  planning  and  decision-­‐making?

This  leads  to:

(i)  What  is  the  most  effec:ve  way  to  integrate  and  organize  mul:ple  heterogeneous,  autonomous  sub-­‐systems  and  sensors  data?  

(ii)  How  can  data  mining  techniques  be  used  to  provide  `smart’  outputs  for  urban  planners,  architects  and  designers  when  proposing  small  interven:ons?

Page 7: Distributed and heterogeneous data analysis for smart urban planning

Computing!Urban Planning !

Architecture!

Middleware data collection data integration data analysis

Social Network Twitter Facebook*

Weather Station Arduino Crawler

Other NFC GPS Tracking

MSD Analysis [space] Behaviour Analysis [people] Survey/Interview Media

Video Image

Living !Campus!

Page 8: Distributed and heterogeneous data analysis for smart urban planning

Source: IoT Tech World

Smart  Ci:es

Smart  Campus

Middleware

Page 9: Distributed and heterogeneous data analysis for smart urban planning

Middleware

•  Middleware refers to the software that is common to multiple applications and builds on the network transport services to enable ready development of new applications and network services.

Page 10: Distributed and heterogeneous data analysis for smart urban planning

Middleware:  Device  Nimbus

Concept

Page 11: Distributed and heterogeneous data analysis for smart urban planning

Middleware:  Device  Nimbus

Design  Architecture

Page 12: Distributed and heterogeneous data analysis for smart urban planning

Middleware:  Device  Nimbus

Prototypes

Page 13: Distributed and heterogeneous data analysis for smart urban planning

Middleware:  Device  Nimbus

Prototypes

Page 14: Distributed and heterogeneous data analysis for smart urban planning

Technologies  used

Page 15: Distributed and heterogeneous data analysis for smart urban planning

Case  Study:  The  Living  Campus  project

Case  study  area

Page 16: Distributed and heterogeneous data analysis for smart urban planning

Case  Study:  The  Living    Campus  project

Page 17: Distributed and heterogeneous data analysis for smart urban planning

Case  Study

Methodology

Page 18: Distributed and heterogeneous data analysis for smart urban planning

Case  Study

Case  study  area

Page 19: Distributed and heterogeneous data analysis for smart urban planning

Case  Study

Research  Data  Collec:on

Page 20: Distributed and heterogeneous data analysis for smart urban planning

Case  Study:  Analysis

Research  Data  Collec:on

VIDEO [MSD Building]

Page 21: Distributed and heterogeneous data analysis for smart urban planning

Case  Study:  Analysis

Research  Data  Collec:on

Page 22: Distributed and heterogeneous data analysis for smart urban planning

Case  Study:  Analysis

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Page 23: Distributed and heterogeneous data analysis for smart urban planning

Case  Study:  Analysis

#unimelb

•  keywords

[0, 'dale', 176.2412992020248] [1, 'melbourne', 84.96667087748327] [2, 'cartilage', 67.16367302941084] [3, 'info', 63.22857982073028] [4, 'footscray', 49.39121003122881] [5, 'anisotropy', 49.39121003122881] [6, 'melb', 49.39121003122881] [7, 'unimelb', 49.39121003122881] [8, 'uni', 45.847364141486885] [9, 'lawn', 39.54234371115054] [10, 'exhibition', 38.41879050304468] [11, 'hyperelastic', 32.92747335415254] [12, 'mentoring', 32.92747335415254] [13, 'alumni', 32.92747335415254] [14, 'music', 28.579490109712147] [15, 'adventures', 26.19895312931797] [16, 'cars', 21.81943844754072] [17, 'volunteering', 20.62029663783727] [18, 'park', 19.880551254825853] [19, 'geometry', 19.34844564484119]

Research  Data  Collec:on

Page 24: Distributed and heterogeneous data analysis for smart urban planning

Case  Study:  Analysis

#unimelb

•  ngrams

[(14, (u'dale', u'robinson', u'phd', u'seminar')), (11, (u'http', u'dale', u'robinson', u'phd')),

(10, (u'biomechanics', u'engunimelb', u'unimelb', u'http')), (8, (u'unimelb', u'http', u'dale', u'robinson')),

(8, (u'engunimelb', u'unimelb', u'http', u'dale')), (5, (u'your', u'troubles', u'music', u'in')),

(5, (u'when', u'there', u'no', u'cars')), (5, (u'war', u'is', u'now', u'open')),

(5, (u'up', u'your', u'troubles', u'music')), (5, (u'uni', u'it', u'nice', u'when')),

(5, (u'troubles', u'music', u'in', u'the')), (5, (u'there', u'no', u'cars', u'in')), (5, (u'the', u'great', u'war', u'is')),

(5, (u'south', u'lawn', u'car', u'park')), (5, (u'park', u'at', u'melbourne', u'uni')), (5, (u'pack', u'up', u'your', u'troubles')), (5, (u'our', u'exhibition', u'pack', u'up')),

(5, (u'open', u'more', u'info', u'http')), (5, (u'now', u'open', u'more', u'info')),

(5, (u'no', u'cars', u'in', u'it')), (5, (u'nice', u'when', u'there', u'no')), (5, (u'music', u'in', u'the', u'great')),

(5, (u'more', u'info', u'http', u'unimelb')), (5, (u'melbourne', u'uni', u'it', u'nice')),

(5, (u'lawn', u'car', u'park', u'at')), (5, (u'it', u'nice', u'when', u'there')), (5, (u'is', u'now', u'open', u'more')),

(5, (u'info', u'http', u'unimelb', u'http')), (5, (u'in', u'the', u'great', u'war')), (5, (u'in', u'it', u'unimelb', u'http')), (5, (u'great', u'war', u'is', u'now')),

(5, (u'exhibition', u'pack', u'up', u'your')),

Research  Data  Collec:on

Page 25: Distributed and heterogeneous data analysis for smart urban planning

Weather Ruby Crawler

Page 26: Distributed and heterogeneous data analysis for smart urban planning

Case  Study:  Analysis

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Page 27: Distributed and heterogeneous data analysis for smart urban planning

Conclusions  and  Future  Work

•  The   Device   Nimbus   middleware   can   be   used   to   collect/combine   data   from  heterogeneous  sources.

•  Device  Nimbus   can   be   used   to   build   a   richer   understanding   of   urban   systems,  based   on   data   collected,   leading   to   improved   tools   for   planning   and  policymaking.

•  The  full  implementa:on  of  Device  Nimbus  will  provide  the  means    to  effec:vely  monitor  users’   rou:nes  –  help  us   to  understand   the  use  of   small  open  spaces,  providing  important  feedback  of  collec:ve  experience.

•  We  also  plan  to  scale-­‐up  our  ini:al  inves:ga:on  to  include  data  collec:on  from  a  diverse  range  of  loca:ons  distributed  across  the  main  university  campus.

Page 28: Distributed and heterogeneous data analysis for smart urban planning

Distributed and heterogeneous data analysis for smart urban

planning

Eduardo Oliveira – [email protected] Michael Kirley Tom Kvan Justyna Karakiewic Carlos Vaz