Spatial Analysis — An Introduction & Overview (Use Cases, Examples & Process)

A blog post that talks about spatial analysis and how to perform it along with it’s use cases in the enterprise and ESG industry
Ebook on how spatial analysis is impacting and solving complex challenges for ESG industry and non profit organizations

What is Spatial Analysis?

Importance of Spatial Analysis

  • Biodiversity Conservation
  • Fighting climate change
  • Predicting quality of life
  • Digital Farming
  • Crime Studies
  • Drought Analysis
  • Green Infrastructure
  • Land-use planning
  • Automatic Data Interpretation

Spatial Analysis Use Cases

Spatial Analysis for Public Health

Microsoft’s Lucas Joppa talking about Gramener’s spatial analysis solutions
Microsoft’s Lucas Joppa talking about Gramener’s spatial analysis solutions

Spatial Analysis for Urban Resilience

Spatial Analysis for Social Causes

Spatial Analysis for Agriculture

Spatial Analysis for Disaster Management

Spatial Analysis Examples for Key Industries

Building Urban Resilience With GeoSpatial Technology

Ai for resilient city is an AI-driven GIS mapping tool that can help governments analyze climate data and solve urban heat island problems

Locating Elephants in the Wild With Satellite Imagery

Infographic that shows how Gramener helped Save the Elephants organization locate elephants in the wild with the help of machine learning and geospatial technology

Predicting Quality of Life (QoL) from Satellite Imagery

Population Density Mapping to Fight Mosquito-Borne Diseases

Disaster Warning and Recovery with AI

Key Capabilities of Spatial Analysis

  • Geographic search: For spatial analysis, you can populate and update maps and dashboards with specific data. To add the data of particular places, you can search through the zip codes, cities, countries, and much more. If you want to identify the healthcare system of geography, you can look for all the hospitals in the region.
  • Clustering: You can check the geographic density of points and events. Clustering will help you identify the low and high values from the dataset. Planning bodies can understand the time it takes for people in a particular geography to access healthcare facilities. They can do this by checking the distance between neighborhoods and hospitals.
  • Formatting and Annotating: Formatting options like lines, shapes, and colors help you get a comprehensive view of your data. If particular geography has hospitals, clinics, and medical colleges, you can mark and differentiate them on the map. You can use different colors and shapes to represent them on the map.
  • Layers: Besides formatting, you can also perform visual mapping with the help of geospatial analysis tools. You can then view and analyze datasets and represent them on maps. Layers on maps can have heatmaps, areas, charts, bubbles, and line layers. You can get the data for layers and background maps from sources like CAD files and weather systems.
  • Target highlighting: You can select different types and combinations of data on layers right from the map to the bar graph. You can combine the data of neighborhood population and healthcare facilities in the vicinity to check if they are adequate for people.

Benefits of Spatial Analysis

  • Allows identification of relationships between various datasets.
  • Gives an understanding of locations and events
  • Detects and quantifies patterns
  • Identifies patterns to predict events like crimes
  • Finds the best locations and paths

How is Spatial Analysis Performed?

Step 1 — Data collection

Step 2 — Data analysis

Step 3 — Data Presentation

spatial analysis solutions from gramener for esg sector companies

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Gramener

Gramener

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Gramener is a design-led data science company that solves complex business problems with compelling data stories using insights and a low-code platform, Gramex.