Urban Informatics and Smart Cities: Prospects and Challenges with New Forms of Data Piyushimita (Vonu) Thakuriah Dean, Bloustein School of Planning and Public Policy Distinguished Professor of Transportation and Urban Informatics NTTS 2019 Please do not distribute without permission BlousteinUrban Big School/RutgersData Centre University Personal and Wearable Tech Trends An explosion of ICT solutions and data Integrated Systems Smart Smart Transportation Buildings Connected Infrastructure Courtesy ETSISmart, collaborative, self-organizing systems Bloustein School/Rutgers University Generations of “Smart Cities” Critical Ingredients: . ICT infrastructure; Version 1: Smart Infrastructure . Effective resource management; . Cost reduction and accountability; . Performance monitoring. City Smart Version 2: Smart . Business-led development; Innovations . Strengthened civic leadership; . ICT-based urban innovations. City Innovative Version 3: Smart . Well-informed and engaged citizens; Citizenry . Addressing problem causes in addition to service delivery; . Social innovations – innovative solutions for urban problems; . Social learning, education and social capital; Future City . Citizen choices and wellbeing. Bloustein School/Rutgers University One example - Connected, Cooperative and Anticipatory Transport Systems Existing Information Information Existing Environment . Intelligent Transportation Systems . Structural Health Monitoring for asset management . Connected systems V2X: . Vehicle-to-Vehicle (V2V) . Vehicle-to-Infrastructure (V2I) New of Elements . Vehicle-to-Grid (V2G) Information Environment Information Bloustein School/Rutgers University Emerging Forms of “Big Data” for Urban Applications Infrastructure A wide spectrum of naturally-occurring data: . Physical – low to high-tech (multi- Generated through transactional, operational, modal transport, planning and social activities not all of which connected were specifically designed for research or the vehicles, smart linkage of such data to purposefully designed buildings, V2G) data . ICT – communications Complexities associated with which (e.g. systems, sensor voluminous, heterogeneous, unstructured, hard- networks to-access) require special considerations: . Data . Technological . Methodological . Theoretical/epistemological . Political economy UrbanBloustein Big School/Rutgers Data Centre University Urban Informatics Data-intensive approaches to analyzing, visualizing, simulating, understanding, interpreting structured and unstructured data on cities and urban areas to address complex urban challenges. Edited volume of NSF workshop: “Big Data and Urban Informatics” UrbanBloustein Big School/Rutgers Data Centre University Big Data and Better Urban Living Smart City Actions and Analytics . Urban infrastructure development and monitoring – building and monitoring transport, energy, ICT, water and other infrastructure . Detection systems . Understanding links, causality and supporting . Urban operations management – transport operations and traffic processes flow management, energy management and optimisation, crime . Forecasting and detection and prevention understanding the future . Citizen engagement/civic participation – involvement in plan- . Evaluation of actions or making, design and idea-generation; crowdsourcing travel and other potential actions information . Engagement . Timeliness . Urban design - create and maintain well-designed, good quality . Fit for purpose places and sites . Value-for-money . Urban planning – large-scale: urban land-use planning, mega- . Understanding biases, infrastructure planning; small-scale: site design, brownfield planning uncertainty, robustness of and regeneration projects findings . Keeping up with the . Urban knowledge discovery – understanding emerging issues, rapidly changing data behaviours, public mood, critical concerns landscape – including . Urban policy analysis and evaluation – impact of proposed high- privacy, citizen awareness speed rail construction, crime prevention strategies and Bloustein School/Rutgers University Grand Challenges for Urban Management . How to operate cities effectively and efficiently . How to build and manage robust and resilient infrastructure . How to evaluate potential consequences of complex social policy change on urban areas . What makes the economy resilient and strong – how to develop shock- proof cities . How to drive economic growth and revenue . How to support business innovation and economic competitiveness . How cities can recover from man-made or natural disasters . What interventions are needed for healthy behavior . What strategies are needed for lifelong learning, civic engagement and community participation . How does one address challenges of social exclusion UrbanBloustein Big Data School/Rutgers Centre University Social Hazards and Trust in Data - A need to balance the Good, the Bad and the Ugly New technology and data has many benefits in the urban space but also has the potential to lead to unfair practices and unintended consequences Bloustein School/Rutgers University Joining up crime detection and safe transport . About 1.25 million people died in 2013 in road crashes worldwide (World Health Organization, 2013) – many in urban areas . Many types of traffic deviance leading to crashes are not random, but has a root cause in the same social conditions that result in concentrations of crime. Crime and traffic crashes often spatio-temporally overlap in cities and are responsible for decreased accessibility and quality of life in cities. Determine a more unifying approach and integrate operational and policy strategies. BUT variable levels of reporting – incidents in some areas, especially poor, deprived areas tend to be underreported in official records UrbanThe Sensing Big Data City: Centre Real-time Monitoring of Cities Context-Awareness and Semantic Enrichment Using Twitter to Understand Local Concerns and Events Can we use language patterns detected in different parts of the city to understand underlying uses, activities, and concerns? System to help identify social and functional concerns and issues potentially for planning or operational action, eg, where people are not happy with services Known incident from transportation sensor data from highways agency Detecting Road Negative tweets – tweets posted Incidents from when there is no Twitter data Positiveincident tweets – tweets posted when there is an incident Bloustein School/Rutgers University Crime – a huge societal issue Study Area . City of Chicago . 758 homicides in 2016 . 98 people killed, 2028 seriously injured in 2014 (latest figures) Complexity of the problem . Significant concentrations of crime and crashes in micro-places, but also spread throughout city . Deep distrust of authority and contested relationships . Limited English speaking capacity in some areas and limited knowledge of social, medical and legal options . Problem with underreporting of crashes and crimes in some areas Bloustein School/Rutgers University Predictive Analytics of . Generally, crimes increase with crashes. Relationship is more evident at points less Traffic Crashes and Crimes than the 90th percentile . Combined crashes and crimes is long- tailed to the right; calls for evaluating models at different points in the distribution . What factors predict crashes and crime (“events”)? – final goal: Eventsii f() X . Interested in quantiles: = .25, .50, .75, .95 . Significant spatial dependence – Spatial Autoregressive version of quantile regression Model-based Underreporting Correction for Traffic Crashes . In the OLS model, Model I Model I crashes tended to be Poisson with Heterogeneity Poisson with Exogenous Underreporting Variable Marginal Effect Marginal Effect overpredicted in Intercept -4.21*** -2.13*** suburban locations EJ_TRACT (1=”Yes”) 0.65*** 0.33*** Environmental Factors and underpredicted in TAI 2 2.01*** 1.01*** the Chicago PED_LOW 1.61*** 0.59*** SUM_AADT 2 0.48e-06*** 0.24e-06*** downtown business SUM_LENGTH 2 -0.28e-03*** -0.14e-03*** district (the “Loop”) NO_SCHOOLS 0.19** 0.09** POP_SQMILE 9.10E-06 4.60E-04 and in southern areas PERCRIME 0.24** 0.12** PED 0.09*** 0.05*** of the City of Chicago Behavioral Factors WLKTOWRK 0.0008*** 0.0009 MEDHHINC99 -2.20E-07 -1.10E-06 PERNOCAR 2.60** 1.31** . Crashes modeled PER_COMM 1.37 0.69 with Poisson count PERCHILDREN -2.09 -1.05 PERLOWENGLISH 0.21 -0.1 data model with Probit Reporting Equation Intercept 5.40E-08 heterogeneity which COUNTY (1=”Cook”) 0.018** accounts for R 2 0.58# 0.61# Log-Likelihood -1763.25 -1511.36 exogenous /df 2 136.8 93.76 underreporting – Vuong Statistic - -60.75 s 0.13 (p< 0.0001) 0.18 (p< 0.517) acknowledging that r - 0 only a subset of the * Significant at 0.10 ** Significant at 0.05 *** Significant at 0.01 actual number of Cottrill, C., and Thakuriah, P. (2010) Evaluating pedestrian crashes in areas with high low- crashes that occurred income or minority populations. Accident Analysis and Prevention, 42(6), pp. 1718-1728. are reported Social media (Twitter) data is useful in detecting events but very sparse Geotagged Tweets Geolocalized Tweets Twitter users are not representative of the population; locations of those who choose to geotag are further not representative of the locations of all Twitter users – but we get a much larger sample allowing us to detect more events, and see activities in more places Bloustein School/Rutgers University Using our methods, we have discovered traffic-related tweets that are not in incident databases – in