[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84126-en":3,"doc-seo-84126-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},84126,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Life Style Levels: Neighborhood Delineation using Geospatial Data","Fine-scale socioeconomic information is often unavailable in rapidly urbanizing regions, limiting the ability to map intra-urban differences in affluence and deprivation. The study proposes a scalable grid-based delineation framework that derives building-morphology indicators from open-source satellite imagery, then combines interpretable features into a transparent rule-based scoring system. Results across 59 Indian cities are validated using ground-level Google Street View observations. Additional density-based clustering of building footprints in Mumbai identifies dense settlements with strong overlap with known informal areas, and an exploratory analysis links derived affluence classes to consumer loan delinquency. ","Life Style Levels: Neighborhood Delineation using Geospatial Data  \nSrivatsa Kulkarni 1 and Debarag Banerjee2  \n1 Senior Data Scientist, L&T Finance  \n2 Chief AI & Data Officer, L&T Finance  \n[srivatsakulkarni@ltfs.com](srivatsakulkarni@ltfs.com)  \nAbstract. Fine-scale socioeconomic information is often unavailable across rapidly urbanizing regions of the developing world, like India, limiting the ability to delineate intra-urban variations in affluence and deprivation. This study proposesa scalable, grid-based urban delineation framework using building morphology derived from open-source satellite imagery. Urban areas across 59 Indian cities and towns are partitioned into high-resolution spatial grids and characterized using interpretable morphological indicators, which are combined into a transparent, rule-based scoring framework to delineate areas with contrasting levels of urban affluence. The resulting classifications are validated through ground-level Google Street View observations, revealing a sharp contrast between the grid classes which are consistent with the expected effects of the lifestyle affluence indicators. We further investigate density-based clustering of building footprints in Mumbai to identify dense urban settlements, demonstrating that the resulting clusters exhibit substantial spatial overlap with known informal settlements across the city. Finally, we conduct an exploratory analysis mapping consumer loan delinquency across the derived affluence classes. By relying entirely on publicly available geospatial data, the proposed framework provides a scalable, interpretable, and cost-effective approach for granular urban affluence mapping across Indian cities.  \nKeywords: Urban Delineation, Informal settlements, DBSCAN Clustering, Open Buildings Dataset, Morphological Features, Sentinel-2 Imagery.  \n1 Introduction  \nRapid urbanization has intensified the need for timely and spatially detailed information on the physical and socioeconomic characteristics of cities. Remote sensing has become an indispensable tool for urban studies by providing consistent, repeatable, and synoptic observations of the Earth's surface over large spatial extents (Weng, 2012; Taubenböck et al., 2012) . More recently, advances in machine learning, together with the increasing availability of open Earth observation datasets and cloud-based geospatial platforms, have substantially expanded these capabilities by enabling automated extraction of urban features and large-scale spatial analysis (Gorelick et al., 2017;  \nReichstein et al., 2019) . These developments have supported a wide range of applications, including urban growth monitoring, land-use mapping, poverty estimation, and informal settlement detection (Jean et al., 2016; Kuffer et al., 2016) . Despite these advances, fine-scale socioeconomic information remains scarce across much of India, where household income, property valuations, and consumption expenditure are rarely available at neighborhoodscales, while census-based socioeconomic statistics are infrequently updated and reported primarily at administrative units (Census of India, 2011) . Consequently, there is growing interest in developing geospatial proxies capable of characterizing neighbourhood-level socioeconomic conditions from remotely sensed observations.  \nUrban morphology has emerged as an important source of socioeconomic information, with numerous studies demonstrating that characteristics of the built environment are strongly associated with wealth, deprivation, and living conditions. Advancesin remote sensing and machine learning have enabled satellite imagery to be used for estimating poverty (Jean et al., 2016), inferring socioeconomic status from urban form (Abitbol & Karsai, 2020), and identifying informal settlements from morphological characteristics (Kuffer et al., 2016; Taubenböck et al., 2018) . Across these studies, attributes such as building density, footprint size, settlement compactness, and","cbCaib5RWSgCNKdW","https://ap.wps.com/l/cbCaib5RWSgCNKdW","pdf",3554675,4,1,43,"English","en",105,"# Introduction\n## Urban studies needs and geospatial data\n## Urban morphology as socioeconomic proxy\n## Open Buildings dataset and research gap\n## Research question and motivation\n## Challenges: neighborhood boundaries and MAUP\n# Methodology and framework (grid-based delineation)\n## Morphological feature extraction\n## Rule-based scoring for affluence classes\n## Validation and additional analyses","[{\"question\":\"What problem does the study address about neighborhood-level socioeconomic data?\",\"answer\":\"Fine-scale socioeconomic information is scarce across rapidly urbanizing regions, and administrative units rarely reflect homogeneous neighborhoods, preventing clear mapping of affluence and deprivation variations within cities.\"},{\"question\":\"How does the framework delineate affluent versus disadvantaged neighborhoods?\",\"answer\":\"It partitions urban areas into high-resolution spatial grids, extracts interpretable building-morphology indicators from open-source satellite imagery, and combines them into a transparent rule-based scoring system to classify affluence levels.\"},{\"question\":\"How are the results validated and what additional analyses are performed?\",\"answer\":\"The grid classifications are validated using ground-level Google Street View observations. The study also performs density-based clustering of building footprints in Mumbai to identify dense settlements overlapping with informal settlements, and explores mapping consumer loan delinquency across the derived affluence classes.\"}]",1784193134,108,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"life-style-levels-neighborhood-delineation-using-geospatial-data","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":20},"https://docshare.wps.com/document/life-style-levels-neighborhood-delineation-using-geospatial-data/84126/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-28","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address about neighborhood-level socioeconomic data?","Question",{"text":75,"@type":76},"Fine-scale socioeconomic information is scarce across rapidly urbanizing regions, and administrative units rarely reflect homogeneous neighborhoods, preventing clear mapping of affluence and deprivation variations within cities.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the framework delineate affluent versus disadvantaged neighborhoods?",{"text":80,"@type":76},"It partitions urban areas into high-resolution spatial grids, extracts interpretable building-morphology indicators from open-source satellite imagery, and combines them into a transparent rule-based scoring system to classify affluence levels.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the results validated and what additional analyses are performed?",{"text":84,"@type":76},"The grid classifications are validated using ground-level Google Street View observations. The study also performs density-based clustering of building footprints in Mumbai to identify dense settlements overlapping with informal settlements, and explores mapping consumer loan delinquency across the derived affluence classes.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]