Mingxiao Li, W. Gao, Wei Tu, Jun Yue, Zhengdong Huang, Qingquan Li
{"title":"Spatial cooperative scenario simulation for infrastructure siting in Guangdong-HongKong-Macao Greater Bay Area","authors":"Mingxiao Li, W. Gao, Wei Tu, Jun Yue, Zhengdong Huang, Qingquan Li","doi":"10.1145/3557989.3566159","DOIUrl":"https://doi.org/10.1145/3557989.3566159","url":null,"abstract":"Siting transportation infrastructures such highway and highspeed railway connects cities and benefits the coordinate development in urban agglomerations. Previous infrastructure siting models rely on the expert experience or spatial-support decision using spatial data describing current conditions. The future development after the infrastructure construction is less considered. This study presents an alternative spatial cooperative simulation-based infrastructure siting model. The spatial cooperative scenario simulation model is developed to simulate the land, population, and economy variations and assess the benefits of the infrastructure construction. The experiment in the high-speed railway in Guangdong-Hongkong-Macao Greater Bay Area, China demonstrates the effectiveness of the presented model. The results provide valuable insights into the spatial-support siting decision for the new high-speed railway in the urban agglomerations.","PeriodicalId":330320,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134280317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Mesa-Geo: A GIS Extension for the Mesa Agent-Based Modeling Framework in Python","authors":"Boyu Wang, Vincent Hess, A. Crooks","doi":"10.1145/3557989.3566157","DOIUrl":"https://doi.org/10.1145/3557989.3566157","url":null,"abstract":"Mesa is an open-source agent-based modeling (ABM) framework implemented in the Python programming language, allowing users to build and visualize agent-based models. It has been used in a diverse range of application areas over the years ranging from biology to workforce dynamics. However, there has been no direct support for integrating geographical data from geographical information systems (GIS) into models created with Mesa. Users have had to rely on their own implementations to meet such needs. In this paper we present Mesa-Geo, a GIS extension for Mesa, which allows users to import, manipulate, visualise and export geographical data for ABM. We introduce the main components and functionalities of Mesa-Geo, followed by example applications utilizing geographical data which demonstrates Mesa-Geo's core functionalities and features common to agent-based models. Finally, we conclude with a discussion and outlook on future directions for Mesa-Geo.","PeriodicalId":330320,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127083752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Subhankar Ghosh, Jayant Gupta, Arun Sharma, Shuai An, S. Shekhar
{"title":"Towards geographically robust statistically significant regional colocation pattern detection","authors":"Subhankar Ghosh, Jayant Gupta, Arun Sharma, Shuai An, S. Shekhar","doi":"10.1145/3557989.3566158","DOIUrl":"https://doi.org/10.1145/3557989.3566158","url":null,"abstract":"Given a set S of spatial feature-types, its feature-instances, a study area, and a neighbor relationship, the goal is to find pairs such that C is a statistically significant regional colocation pattern in region rg. For example Caribou Coffee and Starbucks are significantly co-located in Minneapolis but not in Dallas at present. This problem has applications in a wide variety of domains including ecology, economics, and sociology. The problem is computationally challenging due to the exponential number of regional colocation patterns and candidate regions. The current literature on regional colocation pattern detection has not addressed statistical significance which can result in spurious (chance) pattern instances. In this paper, we propose a novel technique for mining statistically significant regional colocation patterns. Our approach determines regions based on geographically defined boundaries (e.g., counties) unlike previous works which employed clustering, or regular polygons to enumerate candidate regions. To reduce spurious patterns, we perform a statistical significance test by modeling the observed data points with multiple Monte Carlo simulations within the corresponding regions. Using Safegraph POI dataset, this paper provides a case study on retail establishments in Minnesota for validation of proposed ideas. The paper also provides a detailed interpretation of discovered patterns using game theory and regional economics.","PeriodicalId":330320,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123557733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","authors":"","doi":"10.1145/3557989","DOIUrl":"https://doi.org/10.1145/3557989","url":null,"abstract":"","PeriodicalId":330320,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133524389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}