1. Introduction to Big Data and CyberGIS for urban planning UP 418

2. Outline Big Data for cities Cyberinfrastructure and CyberGIS CyberGIS for planning Urban Big Data and CyberGIS

3. Big Data for Cities

4. The three Vs of big data Sagiroglu & Sinanc 2013

5. Big Data Big Data attributes Volume: – Correlation, Optimization – Mapping current check-ins – Kriging crowd-sourced temperature data Velocity – Real-time monitoring of moving objects – Real-time map of all smart phones – Real-time map of tweets related to disasters Variety – Fusion of multiple data sources – Map of post-disaster situation on the ground

6. Spatial Big Data examples Point data: Check-ins Line data: GPS-tracks from smart phones Raster data: UAV/WAMI Video Graph: Temporally detailed roadmaps, Waze, Open Street Map

7. Traditional Spatial Data vs. Spatial Big Data

8. Big Data vs. Spatial Big Data

9. Big data questions: – What are (previously unknown) side-effects of FDA- approved medicines? Spatial Big Data questions: – What are hotspots of spring-break vacationers today? – What are critical places with many smart phone users in the last hour? – Are there any hotspots of recent disaster-related tweets? – Are there traffic congestion areas reported by Waze?

10. Applications of Spatial Big Data Climate Change: availability of tremendous amounts of climate and ecosystem data Next-Generation Routing Services: GPS trace data, engine measurements, and temporally- detailed roadmaps Emergency and Disaster Response: leveraging geo-social media and Volunteered Geographic Information (VGI)

11. Big raster datasets: Global Climate Models (GCM) data Unmanned aerial vehicle (UAV) Data LiDAR data

12. Big Vector Data Volunteered geographic information (VGI) data GPS Trace Data Spatio-Temporal Engine Measurement Data Historical Speed Profiles

13. Emergence of ‘Smart cities’

14. Cyberinfrastructure and CyberGIS

15. What is Cyberinfrastructure? “ a coordinated and flexibly-configured collection of heterogeneous networked devices (e.g. high performance computers, sensors, instruments and data repositories), software and human resources that are needed to address computational and data intensive problems in science, engineering and commerce.” (Armstrong et al. 2011)

16. Challenges of Cyberinfrastructure information integration from multiple sources implement seamless interoperation among heterogeneous datasets develop a standard interface to data and analytical tools provide effective provenance to trace the origin of data and its movement

17. Cyberinfrastructure Applications Social networks: having important effects on personal interactions, information search and the diffusion of ideas Cyberinfrastructure in geospatial information collection: individuals acting as volunteer geospatial data collection agents (Goodchild 2007).

18. Cyberinfrastructure Applications Information delivery services: mobile devices equipped with GPS able to support the provision of context-dependent information services Data analysis and visualization: visualization of data-intensive, large-scale and multi-scale geospatial problems

19. Why cyberGIS Limited ability of conventional GIS software to handle very large spatial data and manage sophisticated spatial analysis/modeling (SAM) CyberGIS is a seamless integration of CI, GIS, and SAM capabilities

20. CyberGIS software integration

22. Major aspects of cyberGIS Four major aspects: providing access to data, integrating disparate data sources, service chaining and provenance.

23. Types of data services Types of data services for cyberGIS: Web Map Services (WMS): returns geo- referenced static images Web Feature Services (WFS): for exchanging actual feature data Web Processing Service (WPS): facilitate sharing, discovery and dynamic binding of geospatial processes

24. Provenance Provenance feature enabled by cyberGIS provides: a common understanding about the content of data and how the data can be used; an assessment of datasets to aid in determining if they meet requirements (of data quality, accuracy, timeliness, etc.) of specific application needs; and replicability of datasets.

25. Societal issues of CI and CyberGIS Access : Differential use of geospatial information through CI creates imbalances among social and economic groups. Privacy: Access to high-resolution geospatial information can also enable individuals to compromise certain aspects of privacy.

26. Societal issues of CI and CyberGIS Quality: data created by individuals with unknown skill levels, quality can become suspect. Aggression: intentional errors could be introduced into geospatial information

27. Societal issues of CI and CyberGIS Piracy: individuals would be motivated to appropriate and repackage rich data for commercial or other uses Educational shallowness: online access to geospatial information may limit the pursuit of richer, more difficult to obtain or use, resources

28. CyberGIS benefits information now flows bi-directionally. offer opportunity for more meaningful participation than traditional forms of public participation. provide users with increased control over content and presentation.

29. CyberGIS for planning

30. Why planning needs CyberGIS Increasing complexity in urban planning and policy-making due to: Rapid urbanization process asymmetric development in spatial planning rise of big urban data

31. Why planning needs CyberGIS (cont.) Technical challenges for effective decision- making: Different types of geospatial resources from multiple agencies Poorly documented data Data in local standards resulting in a high degree of heterogeneity Duplicate efforts due to lack of collaboration

32. Why planning needs CyberGIS (cont..) Visualization of big data acquired from different fields such as telecommunication, public transportation, social media and crowd simulation. Large amount of ever increasing geo-tagged data Find meaningful urban phenomena, reveal spatial patterns in urban areas, explore interaction between human beings and environment

33. CyberGIS as solution to Planning decision making movement away from the stand-alone desktop paradigm virtual web service-based framework computation is carried out in the cloud improves interoperability of distributed geospatial resources promotes widespread sharing of geospatial data and analytical functionalities empowers data-driven scientific analysis

34. CyberGIS for transportation and mobility in cities Integration of CyberGIS with Location-Based Services and Intelligent Transportation Systems (ITS) Capture real-time traffic information by utilizing anonymous floating car (or any mobile positioning device) data to update road status for adaptive routing optimization Historical trajectories of vehicles can be used for spatio-temporal data mining to find interesting knowledge or statistical patterns to guide practical driving

35. CyberGIS-supported urban risk management Use of social media for hazard response and information sharing Utilizing ‘neo-cartographers’ for better collaboration of professional and participatory communities Multi-hazard risk assessment by stakeholders based on a navigational interface

36. CyberGIS-assisted planning decisions advanced GIS and interactive media provide practical and intuitive tools for neo- geographers. 3D visualization and interactive platform for public participation Noise mapping Solar energy

37. Urban Big Data and CyberGIS

38. Urban Big Data Recent “data-deluge”: generation of large real-time data sets at fine spatial scales and over very short time periods In the past, we have never had real time data and most data has not been people-centric UK examples: London Datastore and ‘data.gov.uk’

39. Geodemographics Geodemographics are small-area summary measures of neighborhood conditions. Recent ‘big data’ sources for geodemographics: consumer surveys, smart travel cards, store loyalty program data, etc. New sources of open data (education, transport, health domains, and social media) are useful alongside conventional travel-to- work statistics

40. Applications of new data source: Twitter a significant new frontier for data miners and sociologists alike Available in large volume, on a real-time basis and accessible with considerable ease opportunity to explore geographical phenomena across space and time, without the same sorts of time delay inherent in many survey methods However, the data are only a small and self- selecting subset of the population, Twitter user unconcerned about locational privacy

41. The spatial distribution of 6.34 million geo-located tweets across London from July and August 2012

42. Geodemographic Uncertainty

43. Utilization Uncertainty Sources of bias – variation in the intensity of usage – nature of events and activities that prompt an individual to tweet – spatiotemporal variation in Twitter usage

45. Semantic and Syntactic Uncertainty determining semantic meaning is challenging without sufficient cultural or personal context Language detection process confirms such uncertainty

46. Spatial Uncertainty uncertainty with respect to the accuracy and precision of the measurement Twitter dataset does not provide any indication of spatial accuracy, nor information on the device Twitter clients report users’ locations at different levels of spatial precision

47. Urban Transport in Real Time Increased use of smart cards generate detail data on travel pattern It can be added with timetable information (to help calculate delays), passenger flow information, and a wealth of socio-economic datasets Creates billions of rows of data requiring gigabytes of storage space CyberGIS is the potential solution to work with this big data

48. A snapshot of the number of passengers passing through nodes in the Tube network during a typical rush hour

49. Monitoring transport Infrastructure Real-time data services can be used Most of the Bus and Train services are occupied with GPS tracking device. APIs can be used for real-time data feeding and system monitoring Any major disruption can be quickly identified and alternatives measures can be taken

50. The impact of a bus-driver strike in June 2012. “A” shows the locations of buses at 9am on a normal day whilst “B” shows the locations of buses at 9am on the strike day. It is clear that east London was far more affected by the action.

51. Planning challenges for CyberGIS CyberGIS has important implications for the science of cities and the depth of insights it can provide Data that pertains to real time geocoded to the finest space-time resolution is becoming the new norm and CyberGIS captures the on-going process of adaption required to handle such changes GIscience is beginning to respond to the real-time streaming of big data but it needs a new kind of big science and new infrastructure to really grapple with the analytics required to make sense of such data.

52. Readings Armstrong, M. P., Nyerges, T. L., Wang, S., & Wright, D. (2011). Connecting geospatial information to society through cyberinfrastructure. The SAGE Handbook of GIS and Society. London, Sage Publications, 109-22. Cheshire, J., Batty, M., Reades, J., Longley, P., Manley, E. and Milton, R. (2013). CyberGIS for Analyzing Urban Data, in CyberGIS: Fostering a New Wave of Discovery and Innovation. Wang, S. and Goodchild, M. (eds) Springer-Verlag (in press) http://eprints.ncrm.ac.uk/3159/ Evans, M. R., Oliver, D., Yang, K., & Shekhar, S. (2013). Enabling Spatial Big Data via CyberGIS: Challenges and Opportunities. CyberGIS: Fostering a New Wave of Geospatial Innovation and Discovery. Springer Book. Li, W., Li, L., Goodchild, M. F., & Anselin, L. (2013). A geospatial cyberinfrastructure for urban economic analysis and spatial decision-making. ISPRS International Journal of Geo- Information, 2(2), 413-431. Sagiroglu, S., & Sinanc, D. (2013, May). Big data: A review. In International Conference on Collaboration Technologies and Systems 2013 (CTS), (pp. 42-47). IEEE. Tao, W. (2013) Interdisciplinary urban GIS for smart cities: advancements and opportunities, Geospatial Information Science, 16:1, 25-34, DOI: 10.1080/10095020.2013.774108 Wang, S., Anselin, L., Bhaduri, B., Crosby, C., Goodchild, M. F., Liu, Y., & Nyerges, T. L. (2013). CyberGIS software: a synthetic review and integration roadmap. International Journal of Geographical Information Science, 27(11), 2122-2145.