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1. Perceptions, motivators and barriers of using city management applications among citizens: a focus group approach

   https://doi.org/10.1108/ITP-03-2020-0148  

   Annis, Catherine; Hou, Jinghui; Tang, Tian (May 2021, Information Technology & People)

   null (Ed.)

   Purpose The purpose of this paper is to understand citizens' perceptions of smartphone-based city management apps and to identify facilitators and barriers that influence app adoption and use. An aim is to identify how current technology adoption theories might be expanded and enriched for studying citizen adoption of city apps in the US. Design/methodology/approach This paper presents a qualitative exploratory case study of citizen perceptions of city management apps in Tallahassee, a top-ranked digital city in the southeastern United States. The authors derive empirical data from focus group discussions with citizens using thematic analysis. Findings Overall, the findings suggest that city management apps are primarily perceived and used by citizens as handy and efficient tools for the provision of information and public services. The findings suggest that current technology adoption and use models applied to citizen adoption of m-government may benefit by being expanded for the US context. Originality/value This paper highlights what factors of m-government technology are effective, useful or inhibiting in citizens' lives from the perspective of a group of citizens in the southeastern US. Implications that might be learned for researchers and practitioners are discussed. 

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2. Analyzing urban scaling laws in the United States over 115 years

   https://doi.org/10.1177/23998083241240099  

   Burghardt, Keith; Uhl, Johannes_H; Lerman, Kristina; Leyk, Stefan (April 2024, Environment and Planning B: Urban Analytics and City Science)

   The scaling relations between city attributes and population are emergent and ubiquitous aspects of urban growth. Quantifying these relations and understanding their theoretical foundation, however, is difficult due to the challenge of defining city boundaries and a lack of historical data to study city dynamics over time and space. To address this issue, we analyze scaling between city infrastructure and population across 857 metropolitan areas in the conterminous United States over an unprecedented 115 years (1900–2015) using dasymetrically refined historical population estimates, historical urban road network models, and multi-temporal settlement data to define dynamic city boundaries. We demonstrate that urban scaling exponents closely match theoretical models over a century. Despite some close quantitative agreement with theory, the empirical scaling relations unexpectedly vary across regions. Our analysis of scaling coefficients, meanwhile, reveals that contemporary cities use more developed land and kilometers of road than cities of similar population in 1900, which has serious implications for urban development and impacts on the local environment. Overall, our results provide a new way to study urban systems based on novel, geohistorical data. 

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3. GlobalMapper: Arbitrary-Shaped Urban Layout Generation

   He, Liu; Aliaga, Daniel (October 2023, IEEE International Conference on Computer Vision)

   Modeling and designing urban building layouts is of significant interest in computer vision, computer graphics, and urban applications. A building layout consists of a set of buildings in city blocks defined by a network of roads. We observe that building layouts are discrete structures, consisting of multiple rows of buildings of various shapes, and are amenable to skeletonization for mapping arbitrary city block shapes to a canonical form. Hence, we propose a fully automatic approach to building layout generation using graph attention networks. Our method generates realistic urban layouts given arbitrary road networks, and enables conditional generation based on learned priors. Our results, including user study, demonstrate superior performance as compared to prior layout generation networks, support arbitrary city block and varying building shapes as demonstrated by generating layouts for 28 large cities. 

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4. GlobalMapper: Arbitrary-Shaped Urban Layout Generation

   He, Liu; Aliaga, Daniel (October 2023, IEEE)

   Modeling and designing urban building layouts is of significant interest in computer vision, computer graphics, and urban applications. A building layout consists of a set of buildings in city blocks defined by a network of roads. We observe that building layouts are discrete structures, consisting of multiple rows of buildings of various shapes, and are amenable to skeletonization for mapping arbitrary city block shapes to a canonical form. Hence, we propose a fully automatic approach to building layout generation using graph attention networks. Our method generates realistic urban layouts given arbitrary road networks, and enables conditional generation based on learned priors. Our results, including user study, demonstrate superior performance as compared to prior layout generation networks, support arbitrary city block and varying building shapes as demonstrated by generating layouts for 28 large cities. 

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5. A Land-use/Land Cover Classification of Baltimore City in 1927

   https://doi.org/10.6073/pasta/28793d6417db548f26679ab0e5197fb8  

   Lagrosa IV, John J; Sonti, Nancy; Grove, J Morgan (January 2021, Environmental Data Initiative)

   Land-use and land cover classifications are typically created using automated methods to analyze modern, spatially explicit color aerial imagery. However, creating classifications from black and white historical aerial imagery presents a number of challenges that require a combination of more traditional, manual techniques and approaches. A georectified mosaic of 93 aerial images was digitized in ArcGIS to create a land-use/land cover classification. The analyzed area covered 585 km2 (226 mi2) including all of Baltimore City, and an area immediately adjacent to the city known at the time as the Metropolitan District of Baltimore County. A combination of 8 land-use and land cover classes were used: Agriculture, Barren, Built (Other), Forest, Grass/Shrubland, Industrial, Residential, and Water. This geospatial data set captures a moment of dynamic expansion in the city, just prior to the Great Depression and can be used to examine relationships between property ownership and forest patch dynamics across time. These insights may help inform future environmental planning, conservation, management, and stewardship goals for Baltimore City forest patches, and other cities throughout the region. 

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