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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. SaSTL: Spatial Aggregation Signal Temporal Logic for Runtime Monitoring in Smart Cities

   Ma, M. (April 2020, 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS))

   Abstract—We present SaSTL—a novel Spatial Aggregation Signal Temporal Logic—for the efficient runtime monitoring of safety and performance requirements in smart cities. We first describe a study of over 1,000 smart city requirements, some of which can not be specified using existing logic such as Signal Temporal Logic (STL) and its variants. To tackle this limitation, we develop two new logical operators in SaSTL to augment STL for expressing spatial aggregation and spatial counting characteristics that are commonly found in real city requirements. We also develop efficient monitoring algorithms that can check a SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed spatially in a city).We evaluate our SaSTL monitor by applying to two case studies with large-scale real city sensing data (e.g., up to 10,000 sensors in one requirement). The results show that SaSTL has a much higher coverage expressiveness than other spatial-temporal logics, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor can help improve the safety and performance of smart cities via simulated experiments. 

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