What are the key data sources and tools for location analytics in real estate?

1 Spatial data sources

Spatial data is any data that has a geographic component, such as coordinates, addresses, boundaries, or features. It can be sourced from public agencies, commercial providers, or crowdsourced platforms. For example, census data provides demographic, social, and economic information about the population and households in a given area. Land use data shows how the land is divided and used for different purposes like residential or industrial. Zoning data defines the legal regulations and restrictions for development and use of land. Property data contains characteristics, ownership, value, and transactions of real estate properties. Transportation data includes infrastructure, services, and usage of various modes of transportation such as roads or bike lanes. Additionally, environmental data covers natural and physical conditions and hazards that affect the land such as climate or soil.

2 Spatial analysis tools

Spatial analysis tools are software applications or platforms that enable you to manipulate, visualize, and model spatial data. These tools can help you with various tasks, such as geocoding, mapping, geostatistics, spatial modeling, and spatial optimization. Examples of spatial analysis tools for real estate include ArcGIS, QGIS, Location Intelligence, R, and Python. ArcGIS is a comprehensive GIS platform with a range of tools and services for spatial data management, analysis, and visualization. QGIS is an open source GIS software with a user-friendly interface and a rich set of features for spatial data processing and mapping. Location Intelligence is a cloud-based platform that integrates spatial data and analysis tools with business intelligence and analytics capabilities. R is a programming language and environment that supports packages and libraries for spatial data manipulation and modeling. Python is a popular programming language with many modules and frameworks for spatial data analysis and visualization.

3 Spatial data integration

Spatial data integration is the process of combining and harmonizing spatial data from different sources and formats, which can help enrich and enhance data quality and accuracy, as well as create new insights and opportunities. For example, you can integrate census data with property data to understand customer preferences, land use data with zoning data to identify development potential, property data with transportation data to measure accessibility, or environmental data with property data to assess exposure. To integrate spatial data, you need to use compatible and interoperable standards and formats such as GeoJSON, Shapefile, GeoTIFF, WKT/WKB, or WMS/WFS.

4 Spatial data visualization

Spatial data visualization is the process of presenting and communicating spatial data in graphical or interactive forms. It can help explore and understand data, as well as share and persuade findings and recommendations. For instance, you can use spatial data visualization to compare distributions, identify patterns and trends, analyze relationships and interactions, and illustrate scenarios and outcomes. To visualize spatial data, you need to use tools such as Tableau, Power BI, Leaflet, or D3 - all of which offer powerful features for creating and customizing maps and charts with spatial data.

5 Spatial data ethics

Spatial data ethics is the practice of applying ethical principles and values to the collection, use, and sharing of spatial data. This practice can help protect and respect the rights and interests of data sources and stakeholders, as well as comply with the laws and regulations that govern spatial data. Aspects of spatial data ethics to consider include privacy, security, accuracy, bias, and transparency. To practice spatial data ethics effectively, you should use spatial data governance frameworks and guidelines such as GDPR, GDA, OGC, and UNGGIM. These frameworks provide rules and standards for the protection of personal data in the digital age, establish roles and responsibilities for geospatial data coordination and management across federal agencies, develop standards for geospatial data interoperability and quality, and provide guidance for the advancement of geospatial information management.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?