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Dallas-Fort Worth, the fastest growing region in Texas, has been criticized for decades for auto-centric and corporate development, which has restricted the sustainable design of cultural venues in need of permeable access to the community. Meanwhile, the city’s arts districts – Bishop Arts District and Deep Ellum, officially designated as Cultural Districts in the Dallas Cultural Plan – are known for their clusters of murals, live music venues, and independent businesses, which exemplify the transformation of the forgotten historic streetcar corridors into bohemian cultural destinations. This paper examines the revitalization of Deep Ellum and the Bishop Arts District that have both become successful local destinations with walkable commercial streets. First, we review the literature on cultural placemaking in a post-industrial urban context. Next, using US Census data, we explore the socio-demographic environment of the two neighbourhoods. Then, synthesizing a Geographic Information System (GIS) data set and Google Street imagery, we analyse storefronts that have been modified for cultural use. Urban development of cultural districts shows that reusing storefronts can and should be contextually adapted to the existing built landscape and socio-cultural environment to satisfy both communities’ needs and to enhance local economic growth. In view of cultural placemaking, the city should develop a policy that increases pedestrian and transit access to cultural districts, affordable housing, and spaces for artists and residents, and spurs innovative, often post-retail, cultural storefront repurposing through community engagement in order to build incremental, long-term resilience. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology for urban growth prediction using a machine learning approach. The methodology treats successive historical satellite images of an urban area as a video for which future frames are predicted. It adopts a time‐dependent convolutional encoder–decoder architecture. The methodology's input includes a satellite image for the base year and the prediction horizon. It constructs an image that predicts the growth of the urban area for any given target year within the specified horizon. A sensitivity analysis is performed to determine the best combination of parameters to achieve the highest prediction performance. As a case study, the methodology is applied to predict the urban growth pattern for the Dallas–Fort Worth area in Texas, with focus on two of its counties that observed significant growth over the past decade. The methodology is shown to produce results that are consistent with other growth prediction studies conducted for the areas. [ABSTRACT FROM AUTHOR]

In both rural and urban environments, transportation and access to food are coupled, especially in regions known as food deserts (FDs). However, transportation is not the only factor that may impede food access. This study identifies factors that influence where a household chooses to shop by creating a location-choice model using real travel patterns of low-income households in Dallas. While this analysis shows that travel time to the store remains the most important factor, the results indicate that accessibility also depends on the (1) age of household members (e.g., the presence of seniors or children), (2) access to mobility options, (3) built environment and urban development, (4) household size, and (5) employment. A new metric of food accessibility is formulated, incorporating multiple contributors to inaccess and overcoming limitations of the USDA FD definition. The results show that the proposed metric adapts to any level of aggregation, identifies where targeted interventions could be implemented, and allows for the integration of real travel and shopping behaviors to better coordinate urban planning approaches that increase access to food in the urban environment. [ABSTRACT FROM AUTHOR]

Sprawl measures have largely been neglected in land‐use forecasting models. The current approach for land‐use allocation using optimization mostly utilizes objective functions and constraints that are non‐spatial in nature. Application of spatial constraints could take care of the contiguity and compactness of land uses and can be utilized to address urban sprawl. Because a land‐use model is used as an input to transportation modeling, a better spatial allocation strategy for more compact land‐use projections will promote better transportation planning and sustainable development. This study formulates a scenario‐based approach to normative modeling of urban sprawl. In doing so, it seeks to improve the land‐use projections by employing a spatial optimization model with contiguity and compactness consideration. This study incorporates urban sprawl measures based on smart growth principles together with a mixed‐use factor, and adjacency consideration of nearby land uses. The objective function used in the study maximizes net suitability based on imposed constraints. These constraints are based on smart growth principles that enhance walkability in neighborhoods, promote better health for residents, and encourage mixed‐use development. The formulated model has been applied to Collin County, TX, a fast‐developing suburban county located to the north of the Dallas–Fort Worth metroplex. The suitability of land cells indicates the probability of conversion, which is calculated using spatial discrete choice analysis with Moran eigenvector spatial filtering for vacant cells at a resolution of 150 × 150 m employing factors of the built environment, and socioeconomic and demographic characteristics. This study demonstrates how spatial proximity between land uses, which has been ignored to date, can be used to control sprawl, resulting in better mixing of different land uses based on constraints imposed in a spatial optimization problem. [ABSTRACT FROM AUTHOR]

Since its founding, Dallas, Texas has grown centrifugally. Since the 1990s, however, it has also grown centripetally, with developers building walkable, mixed-use islands in the midst of metropolitan sprawl. This article surveys what amounts to a historic change of habit. [ABSTRACT FROM AUTHOR]

The Dallas-Fort Worth region is subject to a severe expansive soils hazard, resulting in costly damage to many thousands of residential foundations each year. A GIS is used to examine relationships between a sample of approximately 10,000 foundation repair addresses and geologic and soil conditions. The study results show that foundation repairs are concentrated in areas underlain by bedrock, surficial deposits and soils that promote high shrink–swell potentials. A linear extensibility index, designed as an index of overall shrink–swell potential explains about 48% of the variation in the prevalence of repairs. Repairs are concentrated on soils that underlay many areas of new urban growth in the DFW region, suggesting that the incidence of foundation repairs in these areas will probably increase in the future. [ABSTRACT FROM AUTHOR]