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Distributed and heterogeneous
data analysis for smart urban
planning
Eduardo Oliveira 
Michael Kirley 
Tom Kvan
Justyna Ka...
Outline
•  Living  Campus  Project
•  Research  Ques:ons
•  Related  Work:  an  introduc:on  to  middleware
•  Device  Nim...
Living  Campus
University  campuses  represent  an  urban  space  
that  in  many  circumstances  reflects  what  is  
happ...
Living  Campus
Living  Campus:    an  interdisciplinary  perspec:ve
[architecture]
•  Architects,  planners,  and  urban  designers  typi...
Guiding  research  ques:ons
Is  it  possible  to  automa:cally  collect,  combine  and  analyze  data  from  sensors  
(e....
Computing!
Urban Planning !
Architecture!
Middleware
data collection
data integration
data analysis
Social Network
Twitter...
Source: IoT Tech World
Smart  Ci:es
Smart  Campus
Middleware
Middleware
•  Middleware refers to the software that is common to multiple applications
and builds on the network transpor...
Middleware:  Device  Nimbus
Concept
Middleware:  Device  Nimbus
Design  Architecture
Middleware:  Device  Nimbus
Prototypes
Middleware:  Device  Nimbus
Prototypes
Technologies  used
Case  Study:  The  Living  Campus  project
Case  study  area
Case  Study:  The  Living    
Campus  project
Case  Study
Methodology
Case  Study
Case  study  area
Case  Study
Research  Data  Collec:on
Case  Study:  Analysis
Research  Data  Collec:on
VIDEO [MSD Building]
Case  Study:  Analysis
Research  Data  Collec:on
Case  Study:  Analysis
humidity
temperature
luminosity
NFC
noise
PIR
Twitter
è
è
Case  Study:  Analysis
#unimelb
•  keywords
[0, 'dale', 176.2412992020248]
[1, 'melbourne', 84.96667087748327]
[2, 'cartil...
Case  Study:  Analysis
#unimelb
•  ngrams
[(14, (u'dale', u'robinson', u'phd', u'seminar')),
(11, (u'http', u'dale', u'rob...
Weather
 Ruby Crawler
Case  Study:  Analysis
humidity
temperature
luminosity
NFC
noise
PIR
Twitter
è
Conclusions  and  Future  Work
•  The   Device   Nimbus   middleware   can   be   used   to   collect/combine   data   fro...
Distributed and heterogeneous
data analysis for smart urban
planning
Eduardo Oliveira –
eduardo.oliveira@unimelb.edu.au 
M...
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Distributed and heterogeneous data analysis for smart urban planning

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Over the past decade, ‘smart’ cities have capitalized on new technologies and insights to transform their systems, operations and services. The rationale behind the use of these technologies is that an evidence-based, analytical approach to decision-making will lead to more robust and sustainable outcomes. However, harvesting high-quality data from the dense network of sensors embedded in the urban infrastructure, and combining this data with social network data, poses many challenges. In this paper, we investigate the use of an intelligent middleware – Device Nimbus – to support data capture and analysis techniques to inform urban planning and design. We report results from a ‘Living Campus’ experiment at the University of Melbourne, Australia focused on a public learning space case study. Local perspectives, collected via crowdsourcing, are combined with distributed and heterogeneous environmental sensor data. Our analysis shows that Device Nimbus’ data integration and intelligent modules provide high-quality support for decision-making and planning.

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Distributed and heterogeneous data analysis for smart urban planning

  1. 1. Distributed and heterogeneous data analysis for smart urban planning Eduardo Oliveira Michael Kirley Tom Kvan Justyna Karakiewicz Carlos Vaz
  2. 2. 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
  3. 3. Living  Campus University  campuses  represent  an  urban  space   that  in  many  circumstances  reflects  what  is   happening  on  a  larger  scale  across  a  city.  
  4. 4. Living  Campus
  5. 5. 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
  6. 6. 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?
  7. 7. 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!
  8. 8. Source: IoT Tech World Smart  Ci:es Smart  Campus Middleware
  9. 9. 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.
  10. 10. Middleware:  Device  Nimbus Concept
  11. 11. Middleware:  Device  Nimbus Design  Architecture
  12. 12. Middleware:  Device  Nimbus Prototypes
  13. 13. Middleware:  Device  Nimbus Prototypes
  14. 14. Technologies  used
  15. 15. Case  Study:  The  Living  Campus  project Case  study  area
  16. 16. Case  Study:  The  Living     Campus  project
  17. 17. Case  Study Methodology
  18. 18. Case  Study Case  study  area
  19. 19. Case  Study Research  Data  Collec:on
  20. 20. Case  Study:  Analysis Research  Data  Collec:on VIDEO [MSD Building]
  21. 21. Case  Study:  Analysis Research  Data  Collec:on
  22. 22. Case  Study:  Analysis humidity temperature luminosity NFC noise PIR Twitter è è
  23. 23. 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
  24. 24. 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
  25. 25. Weather Ruby Crawler
  26. 26. Case  Study:  Analysis humidity temperature luminosity NFC noise PIR Twitter è
  27. 27. 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.
  28. 28. Distributed and heterogeneous data analysis for smart urban planning Eduardo Oliveira – eduardo.oliveira@unimelb.edu.au Michael Kirley Tom Kvan Justyna Karakiewic Carlos Vaz

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