Smart Toilet Demand Mapping Module: Literature Survey on Location-Based Subsystem for Automatic Grievance Generation in Underserved Urban Areas
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2026
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| author | Astha Mohod Khushboo Bawankule Akanksha Budholiya |
| author_facet | Astha Mohod Khushboo Bawankule Akanksha Budholiya |
| contents | <p>One of the major issues for women, older people, kids, and many people who face while travelling in cities is the unavailability of nearby toilets. Currently available digital maps and toilet finder apps are helpful in finding toilets that already exist. They do not show where toilets are missing or where people often find them unavailable. This study suggests a Smart Toilet Demand Mapping tool, which is a simple addition that can be added to existing navigation platforms like Google Maps apps or other transportation apps. The tool quietly overlooks what people look for when they search for a toilet or a washroom in particular areas. When a lot of people search for a washroom or toilets in a particular area and no toilet is found, this tool identifies that area as a place that needs more toilets. The system uses methods to track search points and, using basic rules, it creates a report which is then directly forwarded towards city officials. This report is created automatically, so people do not have to complain. This paper talks about why this tool is needed, looks at other work on finding toilets and mapping public sanitation, and explains how to implement the tool with the least technology. The research is still in its stages, and no system has been built yet, so the focus is on designing the system, seeing if it is feasible, and understanding its potential impact on society.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19457312 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Smart Toilet Demand Mapping Module: Literature Survey on Location-Based Subsystem for Automatic Grievance Generation in Underserved Urban Areas Astha Mohod Khushboo Bawankule Akanksha Budholiya Urban Sanitation, Smart Cities, Location-Based Services, Passive Demand Mapping, Public Toilet Accessibility, Search Behaviour Analytics, Spatial Clustering, Automated Grievance Generation, Civic Intelligence, Data-Driven Governance <p>One of the major issues for women, older people, kids, and many people who face while travelling in cities is the unavailability of nearby toilets. Currently available digital maps and toilet finder apps are helpful in finding toilets that already exist. They do not show where toilets are missing or where people often find them unavailable. This study suggests a Smart Toilet Demand Mapping tool, which is a simple addition that can be added to existing navigation platforms like Google Maps apps or other transportation apps. The tool quietly overlooks what people look for when they search for a toilet or a washroom in particular areas. When a lot of people search for a washroom or toilets in a particular area and no toilet is found, this tool identifies that area as a place that needs more toilets. The system uses methods to track search points and, using basic rules, it creates a report which is then directly forwarded towards city officials. This report is created automatically, so people do not have to complain. This paper talks about why this tool is needed, looks at other work on finding toilets and mapping public sanitation, and explains how to implement the tool with the least technology. The research is still in its stages, and no system has been built yet, so the focus is on designing the system, seeing if it is feasible, and understanding its potential impact on society.</p> |
| title | Smart Toilet Demand Mapping Module: Literature Survey on Location-Based Subsystem for Automatic Grievance Generation in Underserved Urban Areas |
| topic | Urban Sanitation, Smart Cities, Location-Based Services, Passive Demand Mapping, Public Toilet Accessibility, Search Behaviour Analytics, Spatial Clustering, Automated Grievance Generation, Civic Intelligence, Data-Driven Governance |
| url | https://doi.org/10.5281/zenodo.19457312 |