{"title":"基于agent的高密度城市再开发模型","authors":"S. Leao, B. Gaudou, C. Pettit","doi":"10.1201/9781315642789-7","DOIUrl":null,"url":null,"abstract":"Compact city policies implemented through mixed-use higher density urban redevelopment are reshaping cities worldwide. There is lack, however, of analytical tools integrating information on the existing built form, land market and planning controls that could provide an assessment of how much redevelopment an area can promote over the future, how economically feasible this development is, and if it would meet the demand for housing. Without this, planning will remain responding to individual pressures from redevelopment pushing the boundaries to higher densities without a clear understanding of its consequences at larger scales. This study describes an agent-based model to fulfil this task. It innovates from previous modelling initiatives because it employs actual geographical data, operates at parcel level, provides a 3D dynamic visualisation, and allows the assessment of scenarios. The test of the model to a real case study demonstrated the strength of the model in handling geographic data easily, providing insights of the combined effects of land market and planning framework on the urban redevelopment process, and its ability to be linked to urban design processes to assess their actual delivery potential based on economic feasibility. Current advances in high performance computing and the increasing availability of urban big data raise optimistic horizons for further development of realistic agent based models to assist better understanding, planning and management of urban property development over the future.","PeriodicalId":438512,"journal":{"name":"Real Estate and GIS","volume":"79 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An agent-based model for high-density urban redevelopment under varied market and planning contexts\",\"authors\":\"S. Leao, B. Gaudou, C. Pettit\",\"doi\":\"10.1201/9781315642789-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Compact city policies implemented through mixed-use higher density urban redevelopment are reshaping cities worldwide. There is lack, however, of analytical tools integrating information on the existing built form, land market and planning controls that could provide an assessment of how much redevelopment an area can promote over the future, how economically feasible this development is, and if it would meet the demand for housing. Without this, planning will remain responding to individual pressures from redevelopment pushing the boundaries to higher densities without a clear understanding of its consequences at larger scales. This study describes an agent-based model to fulfil this task. It innovates from previous modelling initiatives because it employs actual geographical data, operates at parcel level, provides a 3D dynamic visualisation, and allows the assessment of scenarios. The test of the model to a real case study demonstrated the strength of the model in handling geographic data easily, providing insights of the combined effects of land market and planning framework on the urban redevelopment process, and its ability to be linked to urban design processes to assess their actual delivery potential based on economic feasibility. Current advances in high performance computing and the increasing availability of urban big data raise optimistic horizons for further development of realistic agent based models to assist better understanding, planning and management of urban property development over the future.\",\"PeriodicalId\":438512,\"journal\":{\"name\":\"Real Estate and GIS\",\"volume\":\"79 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Real Estate and GIS\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1201/9781315642789-7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Real Estate and GIS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1201/9781315642789-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An agent-based model for high-density urban redevelopment under varied market and planning contexts
Compact city policies implemented through mixed-use higher density urban redevelopment are reshaping cities worldwide. There is lack, however, of analytical tools integrating information on the existing built form, land market and planning controls that could provide an assessment of how much redevelopment an area can promote over the future, how economically feasible this development is, and if it would meet the demand for housing. Without this, planning will remain responding to individual pressures from redevelopment pushing the boundaries to higher densities without a clear understanding of its consequences at larger scales. This study describes an agent-based model to fulfil this task. It innovates from previous modelling initiatives because it employs actual geographical data, operates at parcel level, provides a 3D dynamic visualisation, and allows the assessment of scenarios. The test of the model to a real case study demonstrated the strength of the model in handling geographic data easily, providing insights of the combined effects of land market and planning framework on the urban redevelopment process, and its ability to be linked to urban design processes to assess their actual delivery potential based on economic feasibility. Current advances in high performance computing and the increasing availability of urban big data raise optimistic horizons for further development of realistic agent based models to assist better understanding, planning and management of urban property development over the future.