Reclassification of Sustainable Neighborhoods: An Opportunity Indicator Analysis in Baltimore Metropolitan Area

The “Sustainable neighborhoods” has become widely proposed objective of urban planners, scholars, and local government agencies. However, after decades of discussion, there is still no consensus on the definition of sustainable neighborhoods (Sawicki and Flynn, 1996; Dluhy and Swartz 2006; Song and Knaap,2007; Galster 2010). To gain new information on this issue, this paper develops a quantitative method for classifying neighborhood types. It starts by measuring a set of more than 100 neighborhood sustainable indicators. The initial set of indicators includes education, housing, neighborhood quality and social capital, neighborhood environment and health, employment and transportation. Data are gathered from various sources, including the National Center for Smart Growth (NCSG) data inventory, U.S. Census, Bureau of Economic Analysis (BEA), Environmental Protection Agency (EPA), many government agencies and private vendors. GIS mapping is used to visualize and identify variations in neighborhood attributes at the most detailed level (e.g census tracts). Factor analysis is then used to reduce the number of indicators to a small set of dimensions that capture essential differences in neighborhood types in terms of social, economic, and environmental dimensions. These factors loadings are used as inputs to a cluster analysis to identify unique neighborhood types. Finally, different types of neighborhoods are visualized using a GIS tool for further evaluation. The proposed quantitative analysis will help illustrate variations in neighborhood types and their spatial patterns in the Baltimore metropolitan region. This framework offers new insights on what is a sustainable neighborhood

Retail Location and Transit: An Econometric Examination of Retail Location in Prince George’s and Montgomery County, Maryland

Transit oriented development (TOD) is a widely accepted policy objective of many jurisdictions in the United States. There is both anecdotal and empirical evidence to suggest that the vitality of TODs and the transit boardings from any TOD depends significantly on the extent of retail development in the transit station area. We focus in this paper, on the determinants of retail location in two counties, Montgomery County and Prince George’s County, Maryland, with a particular focus on the influence of proximity to rail transit stations. We used data from two counties in the Washington DC suburbs to construct measures of transit and retail accessibility and constructed an econometric model to estimate the relationship between urban contextual factors and retail firm locations. The results from our analysis provide empirical support for the notion that retail firms are attracted to locations with high levels of transit accessibility. By extension, these findings suggest that investments in transit—particularly fixed rail transit—may be an effective method for stimulating retail development in metropolitan areas

Mapping Opportunity: A Critical Assessment

A renewed interest has emerged on spatial opportunity structures and their role in shaping housing policy, community development, and equity planning. To this end, many have tried to quantify the geography of opportunity and quite literally plot it in a map. In this paper we explore the conceptual foundations and analytical methods that underlie the current practice of opportunity mapping. We find that opportunity maps can inform housing policy and metropolitan planning but that greater consideration should be given to the variables included, the methods in which variables are geographically articulated and combined, and the extent to which the public is engaged in opportunity mapping exercises

spopt: a python package for solving spatial optimization problems in PySAL

Spatial optimization is a major spatial analytical tool in management and planning, the significance of which cannot be overstated. Spatial optimization models play an important role in designing and managing effective and efficient service systems such as transportation, education, public health, environmental protection, and commercial investment among others. To this end, spopt (spatial optimization) is under active development for the inclusion of newly proposed models and methods for regionalization, facility location, and transportation-oriented solutions (Feng et al., 2021). Spopt is a submodule in the open-source spatial analysis library PySAL (Python Spatial Analysis Library) founded by Dr. Sergio J. Rey and Dr. Luc Anselin in 2005 (Rey et al., 2015, 2021; Rey & Anselin, 2007). The goal of developing spopt is to provide management and decision-making support to all relevant practitioners and to further promote the appropriate and meaningful application of spatial optimization models in practice

Mi Casa no es Su Casa: The Fight for Equitable Transit-Oriented Development in an Inner-Ring Suburb

Transit-oriented development (TOD) often raises land values and can promote gentrification and the displacement in low-income communities. Little research, however, has shown how communities have organized to fight for more equitable TOD processes and outcomes within particular metropolitan contexts and dynamics of neighborhood change. This case study examines the role of neighborhood-based advocacy and organizing in fighting for equitable TOD and tackling key political and planning challenges in a predominantly Latinx immigrant inner-ring suburb. Their successes show the strengths of community-based, cross-sector coalitions in generating more equitable and inclusive TOD processes, plans, and policies that target conditions of place-based precarity

Polycentrism as a sustainable development strategy: empirical analysis from the state of Maryland

We present in this paper an analysis of economic centers and their role in shaping employment development patterns and travel behavior in the state of Maryland. We begin by identifying 23 economic centers in the Baltimore-Washington region. We then examine these centers first in their role as centers of economic activity and then in their role as nodes in the state’s transportation system. Finally, we identify the commute sheds of each center, for multiple modes of travel and travel times, and examine jobs–housing balance within these various commute sheds. We find that Maryland’s economic centers not only promote agglomerative economies and thus facilitate economic growth; they also generate a disproportionate number of trips and promote transit ridership. These results provide empirical support for policies that promote polycentric urban development, and especially policies that promote polycentric employment development. Further, they suggest that polycentrism as a sustainable development strategy requires careful coordination of regional transportation systems designed to balance jobs and housing within a center’s transit commute shed. Based on these findings we recommend that the Maryland state development plan, and regional sustainable communities plans across the nation, encourage the concentration of employment within economic centers and encourage housing development within the transit commute sheds of those centers

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

How Do Cities Flow in an Emergency? Tracing Human Mobility Patterns during a Natural Disaster with Big Data and Geospatial Data Science

Understanding human movements in the face of natural disasters is critical for disaster evacuation planning, management, and relief. Despite the clear need for such work, these studies are rare in the literature due to the lack of available data measuring spatiotemporal mobility patterns during actual disasters. This study explores the spatiotemporal patterns of evacuation travels by leveraging users’ location information from millions of tweets posted in the hours prior and concurrent to Hurricane Matthew. Our analysis yields several practical insights, including the following: (1) We identified trajectories of Twitter users moving out of evacuation zones once the evacuation was ordered and then returning home after the hurricane passed. (2) Evacuation zone residents produced an unusually large number of tweets outside evacuation zones during the evacuation order period. (3) It took several days for the evacuees in both South Carolina and Georgia to leave their residential areas after the mandatory evacuation was ordered, but Georgia residents typically took more time to return home. (4) Evacuees are more likely to choose larger cities farther away as their destinations for safety instead of nearby small cities. (5) Human movements during the evacuation follow a log-normal distribution

The PySAL ecosystem: philosophy and implementation

PySAL is a library for geocomputation and spatial data science. Written in Python, the library has a long history of supporting novel scholarship and broadening methodological impacts far afield of academic work. Recently, many new techniques, methods of analyses, and development modes have been implemented, making the library much larger and more encompassing than that previously discussed in the literature. As such, we provide an introduction to the library as it stands now, as well as the scientific and conceptual underpinnings of its core set of components. Finally, we provide a prospective look at the library's future evolution