How can GIS support affordable housing development in urban areas?

Urban form also provides foundational knowledge at the city and neighborhood level. Zoning is critical -- it can either provide opportunity or serve as red-lining in the modern era...

Location analysis, yes, it is important to find the appropriate area for affordable housing. Feasibility, affordability, accessibility and compability are already mentioned, but I would like to add one criteria, namely vulnerability. In location analysis, vulnerability in social, health and physical aspects is important to determine location also. The criminal statistic for example to assess the social vulnerability, sanitation channel for health vulnerability and exposure to flood in physical vulnerability.

A tomada de decisão na seleção de um local para construção de moradias acessíveis, perpassa também por diferentes atores, que estão envolvidos no processo do planejamento territorial. Por isso, a construção de um ambiente SIG alimentado com dados confiáveis e seguros, além de ajudar na construção de diretrizes e na geração de insights confiáveis é uma aplicação importante para diferentes gestores públicos nas esferas municipal, estadual, federal e até em escala internacional.

Geographic Information Systems (GIS) can be instrumental in supporting affordable housing development in urban areas in several ways: - Site Selection and Analysis -Targeted Interventions -Policy Planning and Decision-Making -Community Engagement -Infrastructure Planning -Monitoring and Evaluation By leveraging spatial analysis and data visualization capabilities, GIS becomes a powerful tool for governments, urban planners, developers, and community organizations to strategize, implement, and monitor affordable housing initiatives in urban areas.

Site suitability assessment, a crucial step in affordable housing development, is streamlined with GIS technology. GIS enables the evaluation of potential sites based on zoning regulations, proximity to amenities, transportation accessibility, and environmental factors. This data-driven approach ensures that affordable housing projects are located in suitable locations that align with community needs and contribute to vibrant neighborhoods.

GIS can also help with engaging the community and assessing their needs by analyzing policies by evaluating how they could affect housing, on various levels. By using GIS, urban planners, policymakers, and community members can gain an understanding of housing requirements, in cities. They can also identify communities that are not adequately served and create strategies based on data to tackle the housing challenges faced by city residents.

Neste ponto podemos observar com clareza a possibilidade de integração de um ambiente SIG com diferentes bases de dados. Vale ressaltar, que a manipulação desses dados na construção de mapas com distribuição espacial, não descarta o tratamento e análise estatística dos dados. Dessa forma pode-se observar a importância do GIS e da análise estatística.

There are a lot of tools for house analyzes such as Getis Ord Gi*, hotspot, or spatial autocorrelation using local moran I. The data could be collected from physical aspects, social and also environment. Zoning and spatial planning maps are also important to have. Clusterization also has some correlation with land value/price.

El acceso a fuentes de datos públicas, como el Catastro, y su análisis cruzado con otras variables socioeconómicas, permiten generar patrones de distribución geográfica de la población por tipo de viviendas y barrios. Y en base a estos resultados identificar localizaciones que carecen de viviendas asequibles para determinados colectivos, lo que evita fenómenos como la gentrificación.

All that is described to the left is excellent. Nowadays, it is becoming even easy to create a model and use some machine learning to "adjust" the different polygons based on the different signals listed on the left. It is also quite nice to combine GIS and Elastic Search and do signals cross-overs. To be honest I have been quite disappointed by ES's GIS capabilities but one can always use a proper GIS engine (say PostGIS) in combination with ES for better contextualization.

GIS can play a role, in evaluating the effectiveness of affordable housing policies. They achieve this by tracking and measuring outcomes visualizing the distribution of affordable housing data identifying areas that have potential for investment and assessing the cost-effectiveness of interventions aimed at making housing more affordable. With its ability to provide an integrated view of affordable housing data in relation, to location GIS aids decision-making. Ensures that affordable housing policies are successfully meeting their intended objectives.

Um exemplo de uso para construção de indicadores espaciais no ambiente SIG é a álgebra de mapas, pois utilizando-se de um conjunto de operadores é possível manipulam campos geográficos e criar novos temas de mapas.

Los SIG aportan un valor añadido en las políticas urbanas frente al cambio climático. Por ejemplo, la inversión en mejoras para la eficiencia energética se puede evaluar previamente, en función del tipo de edificación, categoría energética, habitantes por vivienda y renta media. De ahí, se puede establecer un modelo más equilibrado entre financiación pública y privada para acelerar el desarrollo de estas mejoras. Y finalmente, se puede evaluar la inversión en relación con los objetivos climáticos alcanzados, como reducción de consumo de energía y emisiones, y también en relación con la mejora en condiciones de habitabilidad alcanzadas, especialmente si trabajamos en barrios con población en riesgo de pobreza energética.

Hopefully I am not in a tunnel but I see here more and more collaboration between a good GIS engine for "rough" geography/geometry selection and a good ES engine for non-geospatial related signals. For example: Find me all houses in this neighborhood (gis) near a metro station (gis) and a grocery store (gis), that has 3 bedrooms (ES), a pool (ES).

GIS has the capability to support stakeholder involvement by offering a platform, for visualizing and studying data. This improves communication, transparency, and consensus-building among stakeholders. Also enables the creation of interactive maps, charts, and dashboards. It further aids in collecting community input and feedback mapping out stakeholder concerns and interests evaluating fairness and equity aspects promoting transparency and accountability and fostering decision-making. Ultimately GIS empowers stakeholders to make informed decisions that have impacts, on the community.

By experience, maps and heat maps are your best friends when presenting such information. And we are well-served nowadays. MapBox, Google, and even the different GIS engines offer excellent map capabilities. I really love MapBox for this.

Acredito que o engajamento seja essencial para qualquer projeto de SIG! Dados e tecnologia não perdura se não tiver pessoas no projeto. Soluções em SIG é uma das melhores formas de explicar o espaço urbano, mas é importante ter tato para inserir os moradores nesses espaços.

Les données sont nombreuses et diffuses. Un outil SIG partagé permet une compréhension et une lecture facilitée de ces informations par l’ensemble des acteurs d’un projet ou d’une stratégie. Pouvoir s’appuyer sur un socle de connaissances commun avant l’action permet de partir sur des bases solides. C’est ce que l’exploitation de données via un SIG permet.

The use of GIS allows for tracking the production of housing units evaluating housing affordability assessing the quality of housing measuring accessibility monitoring the impact, on communities identifying areas that need improvement, and facilitating decision-making based on data. GIS provides an understanding of how effective affordable housing initiatives aid the stakeholders to make the most out of their efforts, in community development.

Extrêment interessant outil d' analyse spatiale determinant dans la géographie prioritaire à condition que leq données spatiales soient correctement hierarchisées, saisies et communiquables, ce qui sur un territoire nécessite un conventionnement particulier avec les principaux fournisseurs de données

Muy interesante para análisis y toma de datos de grandes áreas con mucha información y muchos datos que analizar. No termino de ver todo el potencial para aquellas zonas de escasez de datos o de pequeñas dimensiones, especialmente si no hay suficientes datos en internet.

What I love most about GIS is that of course one needs to optimize the code and the process, but the core of it is in the model one chooses. What geometries to keep, how to keep and query. There are so many neat tricks that can be done with GIS: subdividing geometries, using a pure geometric model on a "flat earth" etc. We are now using more and more of the H3 model (or hex grid model) which - given some acceptable imprecision - is lightning fast in certain cases. These models are now being integrated in different GIS engines and give us even more tools to model a geographic reality. Depending on the case, the model has a huge impact, and this is such an interesting part: how to create an efficient geographical (geometrical) model.