Publicação
Suitability Analysis as a Recommendation System for Housing Search
| Resumo: | The metropolitan cities are facing a huge skewness of service distribution that is given in different parts of the same city. Given the rapid increase in immigration, the quality-of-life factors are often left out while performing housing searches. This paper explores the ideal sub-regions in Delhi, India, for living based on different lifestyle profiles. Using suitability analysis, it is possible to personalize a geographical area for housing. Five such factors, namely, rental budget, commute time, green landscape, pollution, and food accessibility were considered. Four different user profiles (18-65) and their importance to each of the factors were simulated. The range of each variable was standardized using transformations. Data was obtained from data-hubs like Kaggle, OSM, and GEE. The analysis was supported by ArcGIS Pro to get district-level features and suitability modelling. The commute variable is a derived variable from the cost surface raster and AQI values from the weathe r stations were used. Four different suitability maps are generated using multi-criteria evaluation. This automated approach can be useful for customers and agents to find or consult housing for immigrants by making it personalized and providing insights to better explain consumer behaviour based on spatial attributes, hence making spatially intelligent tools. |
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| Autores principais: | Puri, Jaskaran Singh |
| Outros Autores: | Cabral, Pedro |
| Assunto: | Suitability Analysis Spatial Analysis ArcGIS GIS Remote Sensing Computer Graphics and Computer-Aided Design Computer Networks and Communications Computer Science Applications Computer Vision and Pattern Recognition Information Systems Software SDG 10 - Reduced Inequalities SDG 11 - Sustainable Cities and Communities SDG 12 - Responsible Consumption and Production |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | documento de conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | The metropolitan cities are facing a huge skewness of service distribution that is given in different parts of the same city. Given the rapid increase in immigration, the quality-of-life factors are often left out while performing housing searches. This paper explores the ideal sub-regions in Delhi, India, for living based on different lifestyle profiles. Using suitability analysis, it is possible to personalize a geographical area for housing. Five such factors, namely, rental budget, commute time, green landscape, pollution, and food accessibility were considered. Four different user profiles (18-65) and their importance to each of the factors were simulated. The range of each variable was standardized using transformations. Data was obtained from data-hubs like Kaggle, OSM, and GEE. The analysis was supported by ArcGIS Pro to get district-level features and suitability modelling. The commute variable is a derived variable from the cost surface raster and AQI values from the weathe r stations were used. Four different suitability maps are generated using multi-criteria evaluation. This automated approach can be useful for customers and agents to find or consult housing for immigrants by making it personalized and providing insights to better explain consumer behaviour based on spatial attributes, hence making spatially intelligent tools. |
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