Showing papers by "Constantine E. Kontokosta published in 2017"

Structure of 311 service requests as a signature of urban location.

Abstract: While urban systems demonstrate high spatial heterogeneity, many urban planning, economic and political decisions heavily rely on a deep understanding of local neighborhood contexts. We show that the structure of 311 Service Requests enables one possible way of building a unique signature of the local urban context, thus being able to serve as a low-cost decision support tool for urban stakeholders. Considering examples of New York City, Boston and Chicago, we demonstrate how 311 Service Requests recorded and categorized by type in each neighborhood can be utilized to generate a meaningful classification of locations across the city, based on distinctive socioeconomic profiles. Moreover, the 311-based classification of urban neighborhoods can present sufficient information to model various socioeconomic features. Finally, we show that these characteristics are capable of predicting future trends in comparative local real estate prices. We demonstrate 311 Service Requests data can be used to monitor and predict socioeconomic performance of urban neighborhoods, allowing urban stakeholders to quantify the impacts of their interventions.

Structure of 311 service requests as a signature of urban location

Abstract: While urban systems demonstrate high spatial heterogeneity, many urban planning, economic and political decisions heavily rely on a deep understanding of local neighborhood contexts. We show that the structure of 311 Service Requests enables one possible way of building a unique signature of the local urban context, thus being able to serve as a low-cost decision support tool for urban stakeholders. Considering examples of New York City, Boston and Chicago, we demonstrate how 311 Service Requests recorded and categorized by type in each neighborhood can be utilized to generate a meaningful classification of locations across the city, based on distinctive socioeconomic profiles. Moreover, the 311-based classification of urban neighborhoods can present sufficient information to model various socioeconomic features. Finally, we show that these characteristics are capable of predicting future trends in comparative local real estate prices. We demonstrate 311 Service Requests data can be used to monitor and predict socioeconomic performance of urban neighborhoods, allowing urban stakeholders to quantify the impacts of their interventions.

Equity in 311 Reporting: Understanding Socio-Spatial Differentials in the Propensity to Complain.

Abstract: Cities across the United States are implementing information communication technologies in an effort to improve government services. One such innovation in e-government is the creation of 311 systems, offering a centralized platform where citizens can request services, report non-emergency concerns, and obtain information about the city via hotline, mobile, or web-based applications. The NYC 311 service request system represents one of the most significant links between citizens and city government, accounting for more than 8,000,000 requests annually. These systems are generating massive amounts of data that, when properly managed, cleaned, and mined, can yield significant insights into the real-time condition of the city. Increasingly, these data are being used to develop predictive models of citizen concerns and problem conditions within the city. However, predictive models trained on these data can suffer from biases in the propensity to make a request that can vary based on socio-economic and demographic characteristics of an area, cultural differences that can affect citizens' willingness to interact with their government, and differential access to Internet connectivity. Using more than 20,000,000 311 requests - together with building violation data from the NYC Department of Buildings and the NYC Department of Housing Preservation and Development; property data from NYC Department of City Planning; and demographic and socioeconomic data from the U.S. Census American Community Survey - we develop a two-step methodology to evaluate the propensity to complain: (1) we predict, using a gradient boosting regression model, the likelihood of heating and hot water violations for a given building, and (2) we then compare the actual complaint volume for buildings with predicted violations to quantify discrepancies across the City.

Geo-tagged social media data as a proxy for urban mobility

Abstract: We evaluate the utility of geo-tagged Twitter data for inferring a network of human mobility in the New York City through a quantitative and qualitative comparison of the Twitter-based mobility network during business hours versus the ground-truth network based on official statistics. The analysis includes a comparison of the structure of the city inferred through community detection in both networks, comparison of the models of human mobility fitted to both networks, as well as the comparison of the dynamic population distribution across the city presented by the networks. Once the utility of the Twitter data is verified, the availability of an additional temporal component in it can be seen as bringing additional value to numerous urban applications. The data visualization web application is constructed to illustrate one of the examples of such applications.

Digital Traces: Modeling Urban Mobility using Wifi Probe Data

Abstract: City governments all over the world face challenges understanding mobility paŠerns within dense urban environments at high spatial and temporal resolution. While such measures are important to provide insights into the functional paŠerns of a city, novel quantitative methods, derived from ubiquitous mobile connectivity, are needed, o‚ering decision makers beŠer insights to improve urban management and planning decisions. In this paper, we propose a model that uses Wi€ probe request data to model urban mobility in a dense, mixed–use district in New York City. We collect probe request data over 29 access points of a public WiFi network in Lower ManhaŠan for one day, accounting for more than 1million observations and over 60,000 unique devices. First, we aggregate unique entries per access point and per hour, showing that our method has the potential to collect the same type of data as other common methods, by detecting di‚erences in usage activity paŠerns by time of day. We then use a spatial network analysis to identify edge frequencies of journeys between the network nodes, and apply the results to the road and pedestrian sidewalk network to identify usage and trajectories at the street segment level.

Urban informatics for social good: definitions, tensions, and challenges

Abstract: The vast amount of data generated from diverse sources provides both an opportunity and a challenge to urban policymakers and decision-makers. The application of data science and analytics to parse the detailed data that city agencies continually collect offers the opportunity to identify new areas for operational efficiencies, enhanced service delivery, and better informed policy design and implementation. This exploratory paper articulates the theoretical, practical, and pedagogical foundations for the fields of urban informatics and civic analytics and the challenges and tensions to effectively applying computational approaches to urban management, policy, and planning. It describes the state of the field, defines the need for computational methods in cities, and presents the tensions in creating data-driven approaches that both acknowledge and capitalize on shifting modes of learning, working, and decision-making. The paper concludes with a discussion of connecting urban theory to informatics practice.1

Impact Of Urban Technology Deployments On Local Commercial Activity.

Abstract: While smart city innovations seem to be a common and necessary response to increasing challenges of urbanization, foreseeing their impact on complex urban system is critical for informed decision making. Moreover, often the effect of urban interventions goes beyond the original expectations, including multiple indirect impacts. The present study considers the impact of two urban deployments, Citi Bike (bike sharing system) and LinkNYC kiosks, on the local commercial activity in the affected neighborhoods of New York City. The study uses anonymized and aggregated insights provided through a grant from the Mastercard Center for Inclusive Growth in order to provide initial data-driven evidence towards the hypothesis that proximity of Citi Bike stations incentivizes local sales at eating places, while LinkNYC kiosks help people, especially visitors, to navigate local businesses and thus incentivize commercial activity in different business categories.

Twitter Activity Timeline as a Signature of Urban Neighborhood.

Abstract: Modern cities are complex systems, evolving at a fast pace. Thus, many urban planning, political, and economic decisions require a deep and up-to-date understanding of the local context of urban neighborhoods. This study shows that the structure of openly available social media records, such as Twitter, offers a possibility for building a unique dynamic signature of urban neighborhood function, and, therefore, might be used as an efficient and simple decision support tool. Considering New York City as an example, we investigate how Twitter data can be used to decompose the urban landscape into self-defining zones, aligned with the functional properties of individual neighborhoods and their social and economic characteristics. We further explore the potential of these data for detecting events and evaluating their impact over time and space. This approach paves a way to a methodology for immediate quantification of the impact of urban development programs and the estimation of socioeconomic statistics at a finer spatial-temporal scale, thus allowing urban policy-makers to track neighborhood transformations and foresee undesirable changes in order to take early action before official statistics would be available.

Predictors of Re-admission for Homeless Families in New York City: The Case of the Win Shelter Network.

Abstract: New York City faces the challenge of an ever-increasing homeless population with almost 60,000 people currently living in city shelters. In 2015, approximately 25% of families stayed longer than 9 months in a shelter, and 17% of families with children that exited a homeless shelter returned to the shelter system within 30 days of leaving. This suggests that "long-term" shelter residents and those that re-enter shelters contribute significantly to the rise of the homeless population living in city shelters and indicate systemic challenges to finding adequate permanent housing. Women in Need (Win) is a non-profit agency that provides shelter to almost 10,000 homeless women and children (10% of all homeless families of NYC), and is the largest homeless shelter provider in the City. This paper focuses on our preliminary work with Win to understand the factors that affect the rate of readmission of homeless families at Win shelters, and to predict the likelihood of re-entry into the shelter system on exit. These insights will enable improved service delivery and operational efficiencies at these shelters. This paper describes our recent efforts to integrate Win datasets with city records to create a unified, comprehensive database of the homeless population being served by Win shelters. A preliminary classification model is developed to predict the odds of readmission and length of shelter stay based on the demographic and socioeconomic characteristics of the homeless population served by Win. This work is intended to form the basis for establishing a network of "smart shelters" through the use of data science and data technologies.

Measuring the Impact of Urban Street Trees on Air Quality and Respiratory Illness.

Abstract: New streams of data enable us to associate physical objects with rich multi-dimensional data on the urban environment. This study presents how open data integration can contribute to deeper insights into urban ecology. We analyze street trees in New York City (NYC) with cross-domain data integration methods by combining crowd-sourced tree census data - which includes geolocation, species, size, and condition of each street tree - with pollen activity and allergen severity, neighborhood demographics, and spatial-temporal data on tree condition from NYC 311 complaints. We further integrate historical data on neighborhood asthma hospitalization rates by Zip Code and in-situ air quality monitoring data (PM 2.5) to investigate how street trees impact local air quality and the prevalence of respiratory illnesses. The results indicate although the number of trees contributes to better air quality, species with severe allergens may increase local asthma hospitalization rates in vulnerable populations.