Elizabeth C. Delmelle

Mapping collective human activity in an urban environment based on mobile phone data

Identifying and characterizing variations of human activity – specifically changes in intensity and similarity – in urban environments provide insights into the social component of those eminently complex systems. Using large volumes of user-generated mobile phone data, we derive mobile communication profiles that we use as a proxy for the collective human activity. In this article, geocomputational methods and geovisual analytics such as self-organizing maps (SOM) are used to explore the variations of these profiles, and its implications for collective human activity. We evaluate the merits of SOM as a cross-dimensional clustering technique and derived temporal trajectories of variations within the mobile communication profiles. The trajectories’ characteristics such as length are discussed, suggesting spatial variations in intensity and similarity in collective human activity. Trajectories are linked back to the geographic space to map the spatial and temporal variation of trajectory characteristics. Different trajectory lengths suggest that mobile phone activity is correlated with the spatial configuration of the city, and so at different times of the day. Our approach contributes to the understanding of the space-time social dynamics within urban environments.

Urban Bicyclists: Spatial Analysis of Adult and Youth Traffic Hazard Intensity

As issues related to oil dependency, rising gas prices, and global warming come to the forefront of topics of concern for Americans, the need for alternative modes of transportation has become critical. Urban settings are seemingly ideal for bicycling to become a significant mode, given the greater compactness of destinations. However, in the United States, bicycling is both scarcely used and very dangerous, as bicyclists are 12 times more likely to be killed than automobile drivers. The purpose of this research is to gain greater insights into the geographic dimensions of traffic crash intensity that bicyclists may experience in American cities. Bicycle crashes are studied in Buffalo, New York, for the years 2003 and 2004. The geographic distribution of crashes is determined and compared for both youth and adult bicyclists and factors of crash hazard intensity are statistically identified. Density of development and physical road characteristics such as roadway and intersection functional class are examined, as well as socioeconomic and demographic variables and potential trip attractors. Given the spatial nature of these variables, a spatially weighted regression model is incorporated to account for spatial dependencies of the dependent variables and of their model residuals. The results of the analysis indicate clear distinctions between youth and adult bicycle crashes, both in terms of the neighborhoods where victims reside and in terms of the neighborhoods where these two demographic groups are found to be more frequently involved in a crash with a motorized vehicle.

Trajectories of Multidimensional Neighbourhood Quality of Life Change

This paper provides an empirical analysis of the multidimensional, spatio-temporal quality of life (QoL) trends followed by neighbourhoods in Charlotte, NC, between 2000 and 2010. Employing a combined geocomputational and visual technique based on the self-organising map, the study addresses which types of neighbourhood experienced the most change or stability, where (in attribute and geographical spaces) did neighbourhoods that began the decade with a particular set of characteristics evolve to, and where did neighbourhoods that concluded the decade transition from? Results indicate that the highest QoL neighbourhoods were most stable, while those with lower homeownership, closer to the city centre, exhibited the sharpest longitudinal trajectories. Lower-income neighbourhoods are found to be heterogeneous in terms of their social problems, dividing between high crime concentrations and youth-related social problems. An exchange of these social issues over time is observed as well as a geographical spread of crime to middle-ring suburbs.

Mapping the DNA of Urban Neighborhoods: Clustering Longitudinal Sequences of Neighborhood Socioeconomic Change

The spatial pattern of longitudinal trends in neighborhood socioeconomic dynamics has long been implied by traditional urban models dating back to the Chicago School; however, empirical studies beyond the mapping of change between two points in time are surprisingly limited. This article introduces a methodology to the study of spatial–temporal patterns of neighborhood socioeconomic change. The approach first involves establishing discrete classes of neighborhoods following a k-means clustering procedure and then applies a sequential pattern mining algorithm to determine the similarity of longitudinal sequences. Sequences are then clustered to derive a typology of neighborhood trajectories. The method is employed in an empirical analysis of neighborhood change from 1970 to 2010 for all census tracts in the cities of Chicago and Los Angeles. In Chicago, this time period was marked by a sustained process of center city revitalization through two distinct upgrading processes, whereas in Los Angles, neighborhood upgrading largely came in the form of suburban upgrading. The spatial structure of neighborhood dynamics in Chicago resembled patterns described by Chicago School theorists, whereas the dynamics of Los Angeles deviated from this ordered regularity.

Neighborhood Quality-of-Life Dynamics and the Great Recession: The Case of Charlotte, North Carolina

This paper examines neighborhood responses to the business cycle of the decade 1999–2009. Utilizing a quality-of-life (QoL) framework to profile neighborhoods in the city of Charlotte, North Carolina, we investigate with a Markov-chain methodology how the process of change was impacted by short-term economic fluxes including the greatest downturn since the Great Depression. Results indicate that neighborhoods falling within the lowest QoL category exhibited the greatest boost in relative upward mobility in the boom leading up to the recession. Many of these same neighborhoods reverted back to their previous conditions once the recession hit. Overall, neighborhood resilience is context dependent and so is neighborhood response to exogenous shocks. The same patterns did not hold true when the QoL index was decomposed into its four comprising dimensions.

Differentiating pathways of neighborhood change in 50 U.S. metropolitan areas

Rapid transformations sweeping the United States over the past 50 years have necessitated a reassessment of longstanding theories on how the neighborhood change process has unfolded. This article builds upon recent methodological advancements aimed at understanding longitudinal dynamics by developing a workflow that blends the self-organizing map and a sequential alignment method to visualize pathways of change in a multivariate context. It identifies the predominant pathways in which neighborhoods have changed according to their racial, ethnic, socioeconomic and housing characteristics in the largest US metropolitan statistical areas from 1980 to 2010. The distribution of these pathways is subsequently examined between metropolitan statistical areas and the spatial clustering of these trajectories within cities is analyzed. Results reveal a white-flight type process, the establishment of a multiethnic neighborhood, densification of single-family neighborhoods, gentrification in relatively diverse neighborhoods, upgrading of white single family neighborhoods, and the most frequent pathway of all: no change. High-poverty minority and wealthy white neighborhoods are most spatially compact and expanding in a contiguous manner, while multiethnic neighborhoods are relatively dispersed. Six groups of metropolitan statistical areas are identified based upon the similarity of their neighborhood composition. Parallels are drawn between the formation of enduring high-poverty black neighborhoods in Northern and Midwestern cities and the emergence of clusters high-poverty Hispanic neighborhoods in Hispanic destination cities.

Mutual relationships in neighborhood socioeconomic change

In this article, we investigate the factors that lead to changes in the socioeconomic complexion of urban neighborhoods along four critical dimensions: crime, youth social distress, home ownership, and economic conditions. We argue that the dynamics of these dimensions are better apprehended simultaneously considering their potential mutual relationships and we propose a cross-lagged panel model approach within a structural equation modeling framework. Neighborhoods in Charlotte, North Carolina, are used as a case study, and change is modeled at several time lags throughout the 2000–2010 decade. Findings indicate that the model performs well and that it offers a very promising avenue for modeling the socioeconomic changes of neighborhoods that accounts for complex longitudinal effects as well as spatial dependencies. Specifically, it shows that lower human capital manifested by a decline in youth indicators is significant in explaining subsequent increases in crime and declines in economic indicators. Also, the predominance of housing stock constructed in the 1950s and 1960s is a significant trigger of declines across all neighborhood indicators. Finally, spatial spillover effects between neighborhoods are found to be short-lived and dissipate after a few years.

The effectiveness of Habitat for Humanity as a neighborhood stabilization program: The case of Charlotte, North Carolina

Abstract Habitat for Humanity has been in operation since 1976 as a popular organization that provides housing to low-income families. In more recent years, the organization has gradually shifted its priorities toward becoming a more holistic neighborhood stabilization program, receiving a large amount of federal funding to help stabilize neighborhoods in the wake of the Great Recession. Very limited research has been done to assess the effectiveness of Habitat construction on neighborhood outcomes. This article provides a quantitative assessment of housing price values in Charlotte, North Carolina in neighborhoods that underwent a greater than average amount of Habitat construction compared to a set of similar neighborhoods with no Habitat activity. Using an adjusted interrupted time series model, we find little evidence that Habitat had a substantive impact on housing values compared to control neighborhoods.

Classifying multidimensional trajectories of neighbourhood change: a self-organizing map and k-means approach

ABSTRACT Understanding the dynamic nature of how urban neighbourhoods evolve through time has been a critical issue both in the literature and in public policy practice for decades. Methodological limitations in understanding change across the multiple attribute dimensions that define a neighbourhood, through time and for spatially situated units, have largely reduced empirical analyses to two points in time or for a singular attribute dimension. This paper demonstrates a two-layered approach to classifying neighbourhoods according to their multidimensional, temporal trajectories. The method first projects data onto a two-dimensional output space using a self-organizing map, then constructs temporal trajectories of change across this space and, finally, classifies the resulting trajectories with a k-means algorithm. The resulting typology of neighbourhood trajectories are then mapped in the geographic space to visualize the space–time, multidimensional dynamics. A case study of neighbourhood change from 1970 to 2010 in eight US cities demonstrates the effectiveness approach for grouping neighbourhoods according to the similarity of their temporal pathways of change. Ten processes are uncovered ranging from various forms of suburbanization to revitalization and a suburban densification trend in the newest, rapidly growing cities in the study. Finally, we calculated a join-count statistic to quantify the observed spatial pattern of each of these neighbourhood types in each city.