Commuter Mobility Patterns in Social Media: Correlating Twitter and LODES Data

The Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics (LODES) are an important city planning resource in the USA. However, curating these statistics is resource-intensive, and their accuracy deteriorates when changes in population and urban structures lead to shifts in commuter patterns. Our study area is the San Francisco Bay area, and it has seen rapid population growth over the past years, which makes frequent updates to LODES or the availability of an appropriate substitute desirable. In this paper, we derive mobility flows from a set of over 40 million georeferenced tweets of the study area and compare them with LODES data. These tweets are publicly available and offer fine spatial and temporal resolution. Based on an exploratory analysis of the Twitter data, we pose research questions addressing different aspects of the integration of LODES and Twitter data. Furthermore, we develop methods for their comparative analysis on different spatial scales: at the county, census tract, census block, and individual street segment level. We thereby show that Twitter data can be used to approximate LODES on the county level and on the street segment level, but it also contains information about non-commuting-related regular travel. Leveraging Twitter’s high temporal resolution, we also show how factors like rush hour times and weekends impact mobility. We discuss the merits and shortcomings of the different methods for use in urban planning and close with directions for future research avenues.

A Multi-Level Analysis of Risky Streets and Neighbourhoods for Dissident Republican Violence in Belfast

This paper uses graph theoretical measures to analyse the relationship between street network usage, as well as other street- and area-level factors, and dissident Republican violence in Belfast. A multi-level statistical model is used. Specifically, we employ an observation-level random-effects (OLRE) Poisson regression and use variables at the street and area levels. Street- and area-level characteristics simultaneously influence where violent incidents occur. For every 10% change in the betweenness value of a street segment, the segment is expected to experience 1.32 times as many incidents. Police stations (IRR: 22.05), protestant churches (IRR: 6.19) and commercial premises (IRR: 1.44) on each street segment were also all found to significantly increase the expected number of attacks. At the small-area level, for every 10% change in the number of Catholic residents, the number of incidents is expected to be 4.45 times as many. The results indicate that along with other factors, the street network plays a role in shaping terrorist target selection. Streets that are more connected and more likely to be traversed will experience more incidents than those that are not. This has important practical implications for the policing of political violence in Northern Ireland generally and for shaping specific targeted interventions.

Biking Where Black: Connecting Transportation Planning and Infrastructure to Disproportionate Policing

This study asks whether deficiencies in transportation are associated with disproportionate policing in Chicago using the case of cycling. I examine how the number of bicycle citations issued per street segment are influenced by the availability of bicycle facilities and street characteristics, controlling for crash incidence, police presence, and neighborhood characteristics. Tickets were issued 8 times more often per capita in majority Black tracts and 3 times more often in majority Latino tracts compared to majority white tracts. More tickets were issued on major streets, but up to 85% fewer were issued when those streets had bike facilities, which were less prevalent in Black and Latino neighborhoods. Tickets were not associated with bicycle injury-crashes and inversely associated with vehicle injury-crashes. Infrastructure inequities compound the effects of racially-biased policing in the context of transportation safety strategies. Remedies include the removal of traffic enforcement from safe systems strategies and equitable investment in cycling.

Cross-sectional associations between the neighborhood built environment and physical activity in a rural setting: the Bogalusa Heart Study

Background Insufficient physical activity (PA) is a common health risk and more prevalent in rural populations. Few studies have assessed relationships between the built environment and PA in rural settings, and community policy guidance to promote PA through built environment interventions is primarily based on evidence from urban studies. Methods Participants in the Bogalusa Heart Study, a longitudinal study in rural Louisiana, with International Physical Activity Questionnaire data from 2012 to 2013 and a valid residential address (N = 1245) were included. PA was summarized as the number of weekly metabolic equivalent (MET)-minutes of total, transportation, and leisure time PA. The Rural Active Living Assessment street segment audit tool and Google Street View were used to assess features of the built environment overall and in six categories (path features, pedestrian safety features, aesthetics, physical security, destinations and land use) that influence PA. Scores for street segment built environment (overall and in categories) were calculated, for segments and buffers of 0.25, 0.50, 1.00 and 1.50 miles. Associations between built environment scores and PA were assessed with generalized estimating equations. Results Participants reported little weekly total, leisure time, and transportation PA (mean 470, 230 and 43 MET-minutes per week, respectively). A 1-point increase in the overall built environment score was associated with 10.30 additional weekly leisure time MET-minutes within a 1.50 mile buffer (p-value 0.05), with a similar magnitude observed for a 1.00-mile buffer. A 1-point increase in the aesthetic score was associated with significantly higher leisure time PA for all geographic units (from 22.21 to 38.75 MET-minutes weekly) when adjusted for individual covariates, but was attenuated and only significant for the segment of the residence after accounting for other neighborhood characteristics. Conclusions Significant associations between features of the environment (overall and aesthetic scores) with leisure time PA were observed among adults in this rural population. Built environment interventions in rural settings face additional barriers of lower population density and greater distances for infrastructure projects, and it is important to identify approaches that are both feasible for rural communities and can promote PA.

RIDE VIBRATIONS: TOWARDS COMFORT-BASED BICYCLE NAVIGATION

Providers for common navigation systems and mobile applications apply their route choice concepts for cars almost unmodified to cyclists. In contrast to motorists the latter are not significantly influenced by the traffic situation or speed limits, but notably by other factors like slopes and path’s surface type and quality. In a volunteered geographic information fashion this paper contributes a smartphone-based mobile sensing and evaluation approach for bicycle way’s roughness. It presents the complete process chain from data acquisition using the mobile app ”RideVibes” to a detailed data analysis on street segment level to finally enable a comfort sensitive route optimization and recommendation.

Geocoding Error, Spatial Uncertainty, and Implications for Exposure Assessment and Environmental Epidemiology

Although environmental epidemiology studies often rely on geocoding procedures in the process of assigning spatial exposure estimates, geocoding methods are not commonly reported, nor are consequent errors in exposure assignment explored. Geocoding methods differ in accuracy, however, and, given the increasing refinement of available exposure models for air pollution and other exposures, geocoding error may account for an increasingly larger proportion of exposure misclassification. We used residential addresses from a reasonably large, dense dataset of asthma emergency department visits from all New York City hospitals (n = 21,183; 26.9 addresses/km2), and geocoded each using three methods (Address Point, Street Segment, Parcel Centroid). We compared missingness and spatial patterning therein, quantified distance and directional errors, and quantified impacts on pollution exposure estimates and assignment to Census areas for sociodemographic characterization. Parcel Centroids had the highest overall missingness rate (38.1%, Address Point = 9.6%, Street Segment = 6.1%), and spatial clustering in missingness was significant for all methods, though its spatial patterns differed. Street Segment geocodes had the largest mean distance error (µ = 29.2 (SD = 26.2) m; vs. µ = 15.9 (SD = 17.7) m for Parcel Centroids), and the strongest spatial patterns therein. We found substantial over- and under-estimation of pollution exposures, with greater error for higher pollutant concentrations, but minimal impact on Census area assignment. Finally, we developed surfaces of spatial patterns in errors in order to identify locations in the study area where exposures may be over-/under-estimated. Our observations provide insights towards refining geocoding methods for epidemiology, and suggest methods for quantifying and interpreting geocoding error with respect to exposure misclassification, towards understanding potential impacts on health effect estimates.

Random walks in urban graphs: A minimal model of movement

A framework for calculating a weighted random walk on an urban street segment network is described, and tested as a predictor of pedestrian and vehicle movement in London and the wider region. This paper has three aims. First, it proposes the simplest possible model of agency in that individuals have neither memory, goals nor knowledge of the network beyond street segments immediately visible at an intersection. Second, it attempts to reconcile two divergent approaches to urban analysis, graph centrality measures and agent simulation, by demonstrating properties of topological graphs emerge from the lowest level agent behaviour. Third, it aims for far faster computation of relevant features such as the foreground street network and prediction of movement than currently exists. The results show that the idealised random walk predicts observed movement as well as the best existing centrality measures, is several orders of magnitude faster to calculate, and may help to explain movement without perfect knowledge of the map, by demonstrating the street network is structured such that long range information on optimal paths correlates with geometrical features locally visible at each intersection.