Most cities in the United States rely on zoning to address important planning-related issues within their jurisdictions. Planners often use GIS tools to analyze these issues in a spatial context. ESRI’s ArcGIS Urban software seeks to provide the planning profession with a GIS-based solution for various challenges, including zoning’s impacts on the built environment and housing capacity.This research explores the use of ArcGIS Urban for assessing the existing zoning and comprehensive plans in meeting the projected residential growth in the near future using the City of Arlington, Texas as a case study. The exploration provides examples and lessons for how ArcGIS Urban might be used by planners to accomplish their tasks and highlights the capabilities and limitations of ArcGIS Urban in its current stand. The paper is concluded with some suggestions for future studies.

Most data related to urban and regional planning and the assessment of economic, environmental and social sustainability of a city or region have a spatial component. Some of these data are also inherently ambiguous in nature. Traditional Boolean logic in GIS lacks the capability to model the fuzziness, uncertainties and imprecision of data in geographic information and human decision-making processes. While considerable studies have investigated applications of fuzzy system in urban planning, only a few studies have attempted to integrate fuzzy inference within a GIS platform with the Mamdani method. To facilitate the efficient modelling and displaying of the imprecision of geographic data and human judgement, we develop a rule-based fuzzy inference system within ArcGIS environment using the Sugeno method. This article describes the development process of the system and demonstrates the usefulness of fuzzy logic application in urban planning. It concludes with a discussion of implications for urban planning and needs for further research.

Additive manufacturing (AM) or 3D printing is transforming the construction industry by enabling the production of complex structures and components from digital blueprints using materials like concrete, plastics, and recycled materials. This technology reduces material waste, lowers production costs, and opens new possibilities for sustainable and affordable housing. Traditionally used for prototypes and low-volume production, AM has advanced into the architecture, engineering, and construction (AEC) sectors, offering potential solutions to the affordable housing crisis. Concrete 3D printing, for example, can reduce carbon emissions through the use of alternative materials, minimizing the need for raw resources. Additionally, the ability to optimize material usage and reduce construction waste through techniques like prefabrication and rapid construction can significantly lower the cost of building homes. This paper discusses how AM can contribute to addressing the challenges of affordable housing by exploring its applications in construction, its potential for reducing environmental impacts, and its role in improving cost-effectiveness. By integrating AM into manufactured housing models, it becomes possible to develop sustainable, cost-effective homes on a larger scale, which offers a promising solution to the growing demand for affordable housing. Through the widespread adoption of 3D printing technologies, it is feasible to address both affordability and sustainability concerns in the housing sector.

Cities around the world have amassed a variety of data. A main challenge lies in transforming these big data into meaningful knowledge that can inform a city’s strategic decisions and enhance urban sustainability. Along with this challenge is the debate about “Wisdom of Crowds” (WOC) in the Petabyte Age. Using the City of Arlington, Texas as a case study, this research explores a hybrid approach for social inquiries with the aid of WOC and spatial learning techniques to leverage data for developing predictive models to support a city’s service planning. The results indicate that there exist temporal and spatial patterns of service demands, spatial correlation between demands for code compliance and water services, as well as association with neighborhood characteristics. The findings point to opportunities for further data integration and data mining, organizational collaboration, and resource management to improve the efficiency of service provision in cities.