The unequal commute: Comparing commuting patterns across income and racial worker subgroups

The spatial dimension of the journey-to-work has important implications for land use and development policymaking and has been widely studied. One thrust of this research is concerned with the disaggregation of workers into subgroups for understanding disparities in commute. Most of these studies, however, were limited to the disaggregation by single socioeconomic class. Hence, this research aims to examine commuting disparities across commuter subgroups stratified by two socioeconomic variables—income and race—using a visual analytics approach. By applying the doubly constrained spatial interaction model to the 2014 Longitudinal Employer-Household Dynamics data, this research first synthesizes commuting flows for Downtown Houston workers across income-race subgroups at the tract level in Harris County, Texas, USA. It then uses bivariate choropleth mapping to visualize the spatial distributions of major Downtown Houston commuter neighborhoods by income-race classes, and significant commuting disparities are identified across income-race subgroups. The results highlight the importance of considering income and race simultaneously for commuting research. The visualization could help policymakers clearly identify the unequal commute across worker subgroups and inform policymaking.

ClockBoard: a zoning system for urban analysis

Zones are the building blocks of urban analysis. Fields ranging from demographics to transport planning routinely use zones — spatially contiguous areal units that break-up continuous space into discrete chunks — as the foundation for diverse analysis techniques. Key methods such as origin-destination analysis and choropleth mapping rely on zones with appropriate sizes, shapes and coverage. However, existing zoning systems are sub-optimal in many urban analysis contexts, for three main reasons: 1) available administrative zoning systems are often based on somewhat arbitrary factors; 2) evidence-based zoning systems are often highly variable in size and shape, reducing their utility for inter-city comparison; and 3) official zoning systems are non-existent, not publicly available, or are too coarse, hindering urban analysis in many places, especially in low income nations. To tackle these three key issues we developed a flexible, open and scalable solution: the ClockBoard zoning system. ClockBoard consists of 12 segments divided by concentric rings of increasing distance, creating a consistent visual frame of reference for cities that is reminiscent of a clock and a dartboard. This paper outlines the design, potential uses and merits of the ClockBoard zoning system and discusses future avenues for research and development of new zoning systems based on the experience.

Where you Live Matters: A Spatial Analysis of COVID-19 Mortality

The COVID-19 pandemic has caused ~ 2 million fatalities. Significant progress has been made in advancing our understanding of the disease process, one of the unanswered questions, however, is the anomaly in the case/mortality ratio with Mexico as a clear example. Herein, this anomaly is explored by spatial analysis and whether mortality varies locally according to local factors. To address this, hexagonal cartogram maps (hexbin) used to spatially map COVID-19 mortality and visualise association with patient-level data on demographics and pre-existing health conditions. This was further interrogated at local Mexico City level by choropleth mapping. Our data show that the use of hexagonal cartograms is a better approach for spatial mapping of COVID-19 data in Mexico as it addresses bias in area size and population. We report sex/age-related spatial relationship with mortality amongst the Mexican states and a trend between health conditions and mortality at the state level. Within Mexico City, there is a clear south, north divide with higher mortality in the northern municipalities. Deceased patients in these northern municipalities have the highest pre-existing health conditions. Taken together, this study provides an improved presentation of COVID-19 mapping in Mexico and demonstrates spatial divergence of the mortality in Mexico.

Mapping geographic patterns in federal corporate agreements

Purpose The purpose of this study is to use crime mapping techniques to examine geographic patterns of signed deferred and non-prosecution agreements across federal districts. The purpose is also to examine the variation in the number of agreements by the district since 1992. Design/methodology/approach This study uses data from the Corporate Prosecution Registry to examine geographic patterns in federal corporate agreements since 1992 (n = 534). Choropleth mapping techniques were used to create national crime maps displaying the geographic locations of signed corporate agreements. Findings The results showed that, overall, prosecutors in the District of Columbia have signed the most federal corporate agreements although there is some variation over time. Research limitations/implications This study is unable to determine the causes of changes in the geographic placement or number of agreements signed. It is also unable to determine the precise geographic locations of crimes, but only the location of the District Court that elected to pursue a federal agreement with the organization. Practical implications The wide discretion prosecutors have in the agreement process has led to an overall lack of transparency concerning prosecutors’ decision-making when signing agreements with organizations. This study helps to make the number and geographic location of agreements more transparent. Originality/value This study uses crime mapping techniques to visually depict the locations of signed agreements allowing for visual comparisons and analyzes for an extended period of time.

Adaptive Choropleth Mapper: An Open-Source Web-Based Tool for Synchronous Exploration of Multiple Variables at Multiple Spatial Extents

Choropleth mapping is an essential visualization technique for exploratory spatial data analysis. Visualizing multiple choropleth maps is a technique that spatial analysts use to reveal spatiotemporal patterns of one variable or to compare the geographical distributions of multiple variables. Critical features for effective exploration of multiple choropleth maps are (1) automated computation of the same class intervals for shading different choropleth maps, (2) dynamic visualization of local variation in a variable, and (3) linking for synchronous exploration of multiple choropleth maps. Since the 1990s, these features have been developed and are now included in many commercial geographic information system (GIS) software packages. However, many choropleth mapping tools include only one or two of the three features described above. On the other hand, freely available mapping tools that support side-by-side multiple choropleth map visualizations are usually desktop software only. As a result, most existing tools supporting multiple choropleth-map visualizations cannot be easily integrated with Web-based and open-source data visualization libraries, which have become mainstream in visual analytics and geovisualization. To fill this gap, we introduce an open-source Web-based choropleth mapping tool called the Adaptive Choropleth Mapper (ACM), which combines the three critical features for flexible choropleth mapping.

A Choropleth Map Classification Scheme with Control of Robustness for Uncertain Geographical Attributes

<p><strong>Abstract.</strong> Choropleth mapping visualizes geographical data by grouping map units into a few classes and assigning different colors, shades or patterns to each class. Traditional choropleth mapping method assumes that the data to be displayed are certain. However, many geographical attributes contain uncertainty. For example, attributes generated from survey results may contain sampling error, and data summarized based on statistic inferences usually have a confidence interval. In recent years, some efforts have been made to study choropleth maps for uncertain geographical attributes. Existing research mainly focuses on identifying the class breaks that optimize the within-class homogeneity or between-class separability. These studies have paid little attention to the evaluation of the possibility that each areal unit might be placed in a wrong class due to its associated uncertainty. This paper addresses this issue by proposing a new choropleth mapping scheme that explicitly incorporates robustness. Given a user specified acceptable robustness level that defines the maximum tolerance of misplacement, this paper provides a maximum likelihood estimation-based method to determine the optimal class breaks. A discretization method is introduced to solve the optimization model and generate class breaks. The method is applied to map the American Community Survey 5-year estimated county-level income data with normally distributed uncertainty in the United States at various robustness levels. Results are compared with the theoretical lower bound to show the effectiveness of the discretization method. The relationship between the proposed method and the existing approaches is discussed.</p>

Equity Analysis of Proactively- vs. Reactively-Identified Traffic Safety Issues

Traffic safety issues often impede bicyclist and pedestrian trips, preventing potential users from realizing the benefits of active transport. Traditional active transportation safety analyses, however, take a reactive approach to traffic safety, only accounting for people currently walking or bicycling by analyzing crashes, injuries, and fatalities. This begs the question: which populations are most affected by traffic safety issues neglected by traditional crash analyses? To answer this, we developed a tool to proactively measure perceived traffic safety issues. We focused on child pedestrian and bicycle trips to and from schools in Denver, Colorado by measuring the number of children that would encounter roads perceived as unsafe. We converted these perceptions into barriers in a geographic information system network analysis to estimate trip suppression and used that as a proactive indicator of traffic safety. We finally examined—reactively and proactively—the socio-demographics of those affected via linear regression models and bivariate choropleth mapping. Results of both analyses suggest that negative impacts are borne disproportionately by low-income, low-education, Hispanic, and black neighborhoods. Proactive analyses results identified perceived safety issues in north and northeast Denver neighborhoods neglected by reactive analyses results. Findings suggest the inequitable distribution of traffic safety issues identified in past crash-based literature is graver than conventional reactive analysis would lead one to believe. By incorporating the proactive tool into traditional traffic safety analyses, we hope to better define the places and people that could most benefit from traffic safety improvements, thereby more effectively facilitating the benefits of walking and biking.