What is Geospatial Analysis — Explore Use Cases, Solutions, and Case Studies

Gramener
13 min readMar 10, 2022
An image of satellite sending images to perform data analysis on geospatial data

In 2021, the Geospatial Analytics market was estimated to be $59.5 billion. By 2026, that number is expected to explode to $107.8 billion. Geospatial Analysis has widespread applications across the globe, from being used to further business interests to aiding humanitarian initiatives.

In the middle of a global pandemic, geospatial analytic models are helping to map, monitor, and control the spread of Covid-19 using weather data, live streaming video, maps, and population metrics.

Vegetation management teams can assess moisture and water levels using User Defined Functions (UDFs) in Geospatial Analytics. Meteorologists can also use UDFs to analyze data and predict the path of tornadoes through a region with great accuracy.

Using data from wind forecasts, census data, and satellite imagery, spatial data science technology can aid disaster management teams to track the progress of wildfires and stop their spread. Similarly, there are hundreds more examples of spatial analytics approaches assisting society and people in saving lives.

What is Geospatial Analysis?

Geospatial Analysis, also known as spatial analysis, is the use of computational analysis and AI to extract valuable information from high-resolution satellite imagery, location data, spatial data, and geographic data.

During a natural disaster, governments can use Geospatial AI and Analytics to collect invaluable information about the weather, diseases, and public health to make well-informed decisions.

Electric utility providers can optimize crew scheduling and maintenance using Geospatial Analytics to predict the likelihood of future service disruptions. Insurance providers can calculate the probability of a future event occurring when assessing risks and framing new policies.

Institutional lenders can improve their credit risk scores and reduce the number of bad loans.

Role of Geospatial Analysis in Modern Day Business

Companies can offer a more personalized experience to their customers using Geospatial Analytics solutions. With the help of geospatial data, businesses can also increase their profit margins and operational efficiency.

95% of global executives surveyed claimed that geospatial data and mapping are indispensable to achieving business objectives.

Marketers can apply Geographic Information Systems (GIS) in many ways. It can be used during the initial stages of market research. It can also execute marketing campaigns and evaluate them after completion.

Companies can use Geospatial Analysis to segment their customers according to location. They can also use it to target digital advertisement geographically.

GIS may be used to deliver push notifications based on location. It can also be used to personalize the experience of the customers, such as tailor-made recommendations for the choice of restaurants, cuisine, shopping items, etc.

Researchers can use geospatial data to study the spending habits and patterns of their target demographic and use this information to create consumption maps. These maps can be used to build new supermarkets, malls, etc.

For businesses that enjoy a niche market or operate within a community, location data can complement information collected from behavior and attitude surveys to help formulate marketing strategies.

Under Armour, the US-based sports equipment company uses GIS as an integral component of its business strategy. It collects vast amounts of information from its users through online fitness communities and fitness trackers and uses this data to make customized purchase recommendations.

Soon, spatial data visualization and mobile GIS are expected to transform the way companies serve their existing customers and expand their consumer base.

Gramener and SEEDS India case study to build spatial analysis solution and saving lives in natural disasters

How Does Geospatial Technology Work?

Geospatial data is of two types — vector data and raster data.

Vector data uses polygons, lines, and points to represent real-world objects such as bodies of water, mountains, roads, cities, and properties. Houses and streets can be depicted through lines, and towns can be shown through polygons.

Raster data is gridded or pixelated cells that comprise rows and columns. It can create more complex imagery than vector data, such as satellite images and photographs.

Examples of Geospatial Data

Following are the examples of geospatial data:

  • Vectors: Detailed information about a location such as points, polygons, and lines
  • Point clouds: A collection of closely located points on a chart that can be converted into 3D models
  • Raster and satellite imagery: High-resolution images of the earth’s surface taken from above
  • Census data: Publicly available census data of geographical locations that can help identify community trends
  • Cell phone data: Calls that are routed via satellite and are based on GPS location coordinates
  • Images: CAD images of buildings and other structures. They provide architectural data and geographical information.
  • Social media data: Data scientists can study social media posts to recognize trends and patterns.

Geospatial Technology

The technology required to collect, store and organize geographic information is known as geospatial technology. This includes satellite technology used to map and analyze images of the earth. Geospatial technology also includes remote sensing, geofencing, Global Positioning Systems (GPS), and Geographic Information Systems (GIS).

Use of Python

Data scientists commonly use Python to work with geospatial data. This is because the popular programming language can accommodate both raster data and vector data, the two most common ways of geospatial data representation.

Programs such as xarray can be used to work with Raster data. Vector data can be processed using programs like GeoPandas and Fiona.

Importance of Geospatial Analysis

Geospatial Analysis has many industrial applications. With this technology growing rapidly, the number of ways it can be used to achieve the goals and objectives of institutions and society is expanding significantly.

Geospatial Analytics can analyze topographic, demographic, and environmental data and are powerful tools for decision-making. It helps private corporations, government bodies, and ordinary citizens make well-informed decisions.

Following are some of the critical uses of Geospatial Analytics –

Mapping

Geospatial Analytics can facilitate the visual representation of data. Google Maps is one example of innovative mapping technology that people can use to navigate every day.

Traffic Management

Geospatial Analytics can help identify high accident rates and enable data intelligence to optimize road networks. These measures improve road safety and traffic management.

Urban Planning

Geospatial Analytics helps identify the geographical affinity for urban growth. It can help locate the best sites for investment, resource allocation, and construction.

Logistical Planning

Companies can use Geospatial Analytics to process topical and environmental data and optimize their rail or road routes.

Environmental Protection

Geospatial Analytics can help understand the impact human activities have on the environment, leading to measures to conserve the environment and protect natural habitats.

Farming and Agriculture

Scientists can use Geospatial Analytics to analyze soil data and measure the success of different farming techniques. This can help develop more efficient methods of farming and increase crop yield.

Benefits of Using Geospatial Analysis

Engaging Insights: Geospatial Analysis can help maps and visualizations tell stories, build compelling narratives around situations, and make them easy to understand. This improves communication between organizations, professionals, disciplines, departments, and teams.

Better Foresight: Location and regional information help companies make informed business decisions. For example, using location intelligence, geofencing services, and location insights, a retail store chain can decide where to open its next outlet.

Targeted Solutions: Firms can use detailed GIS insights to offer tailored solutions, increasing their customer base. They can also use spatial information to build a regional buyer persona, catering to the needs of a community and establishing a niche market.

GIS helps organizations conduct market analysis of customers, launch direct mail campaigns, experiment with new communication mediums, and plan marketing personas or strategies.

Governments and public institutions can also use GIS to protect the environment, conserve natural resources, and conduct urban and zone planning.

Geospatial Analysis Tools

Kepler

Kepler.gl is a web-based platform used to visualize location data on a vast scale. The visualization team at Uber created and open-sourced Kepler to supercharge big data using open-source frameworks that are industry-grade.

Kepler is one of the top geospatial visualization tools used to perform Exploratory Data Analysis (EDA). It is the result of exhaustive research by the Uber team on visualization features beneficial to mobility companies.

This solution is user-friendly and is used by businesses that do not possess coding or technical know-how. At the same time, data scientists from Airbnb and Google also use Kepler for its visualizations.

As an open-source solution from Uber, Kepler is used by many mobility companies. It is also a popular tool among journalists and academicians.

Kepler maps have been used in initiatives associated with the San Francisco Street TreeMap, New York City Population, San Francisco Elevation Contours, New York Cab Rides, and California Earthquakes.

Kepler.gl comprises four major suits that can be used to create data-driven maps — React vis, React map, Luma, and Deck. Using WebGL, a JavaScript API, and deck.gl, Kepler can render large data more efficiently and faster.

Microsoft Azure

Azure maps are a collection of SDKs and geospatial services that provide geographical context to mobile and web applications using fresh mapping data.

Azure Maps can help you render and create maps using private indoor map data. It provides REST APIs to render raster and vector maps in multiple styles. Using Azure Map search services, you can also locate points of interest, places, and addresses worldwide.

Azure Maps offer multiple routing options such as matrix routing, traffic influenced, commercial vehicle, electric vehicle, isochrone, multipoint optimization, multipoint, and point-to-point. Applications that require real-time traffic information can use Azure Maps for incidents view and traffic flow view.

Developers can use the Android SDK and the Web SDK of Azure Map services to develop and scale solutions quickly. Azure Maps allow its users to avail a host of services.

Developers can use Azure Map weather services to procure weather data for a particular location. This data includes wind speed information, temperature, precipitation indicator flags, weather icon, UV index, and data and time stamps.

It can also generate weather notifications for regions affected by hazards like heavy rain and flooding.

The Azure Map Search service enables developers to use name, category, or other forms of geographic information to search for business listings, places, and addresses.

Users can avail of advanced features to search for physical stores of famous brands through Point of Interest (PoI) data. They can also search for electric vehicle charging stations or conduct search requests in bulk.

You can use Azure Map to search along a route or over a wide area.

Applications of Geospatial Analysis

Geospatial Analytics can be leveraged to find valuable location-based insights through various industry-specific applications.

Retail

Location-based data can help retailers access a lot of real-time information, such as sales target information, footfall information, product information, and customer information. Mapping and analyzing this data can help businesses identify demographic traits and consumption patterns of target customers and predict future market trends.

Retailers can use location-based data to access IP addresses, mobile location information, zip codes, and physical addresses. This helps them design personalized and targeted marketing strategies for specific communities.

Geospatial data can help retailers identify optimal locations to open outlets and physical stores. It allows them to predict customer demand from a particular site using historical data.

Using geolocation data, businesses can obtain visual representations of their competitors’ customer footfall, marketing strategies, and product offerings, allowing them to shape their strategy.

Mapping retail data can help companies streamline their delivery and shipping processes. Geospatial data enables businesses to schedule goods delivery based on customer shopping patterns and optimize delivery routes.

Transportation and Logistics

Using GIS offerings such as Google maps or customized applications, logistics service providers can locate resources closest to the place of order to deliver the shipment on time.

GIS applications like maps help logistics companies provide effective and efficient service to their customers, identifying the shortest and the best routes from the loading location to the delivery location.

Drivers who are transporting the goods from one place to another can use real-time GIS applications to navigate the routes and reach their destinations safely and on time.

Earlier, businesses would have to call drivers at regular intervals for location updates. Nowadays, GPS devices installed in vehicles can deliver location updates in real-time to both providers and customers.

Businesses can use GIS to calculate the shortest route to their destination and identify other cost-incurring factors like tolls, etc., enabling them to plan routes that save expenditure.

Healthcare Analysis

Healthcare professionals can drive initiatives such as Cancer Surveillance Programs using demographic data such as wearable health tech, cancer type, workplace, and home address.

By mapping and georeferencing this data, healthcare analysts can visualize the locations of patients and identify clusters or patterns of cancer cases associated with residential areas and workplaces. This will help healthcare providers staff experts skilled in certain medical specialties and implement preventative strategies.

GIS systems are being used during the global pandemic to track occurrences of Covid-19 infections. They are also used to predict the spread of infection across a region, helping healthcare professionals take measures to prevent the advance of the virus.

GIS-based maps can also help understand vaccination rates and laws in different communities, enabling the prediction of severe outbreaks in vulnerable locations with great precision.

Wearable technology can collect healthcare-related information such as exposure to the sun, sleeping patterns, and heart rate. Incorporating this data into a GIS can help identify a pattern across a geographical region and the underlying conditions that lead to such clusters.

Researchers can query social media to procure tweets, posts, and messages that carry keywords like “pandemic”, “Covid-19”, “coronavirus”, etc. Adding this information to a GIS map, they can identify which locations the posts originated from and track the spread of the virus.

GIS data can identify regions that lack proper healthcare setups such as hospitals, clinics, nursing homes, etc. By analyzing maps that show areas where patients had to cover large distances to receive treatment, government bodies can allocate resources to develop the region’s healthcare infrastructure.

Banking

During Mergers and Acquisitions (M&A), banks can use geospatial data to identify the location of the branches of the bank being acquired. This enables the acquiring bank to assess the “geographical fit” of the new branches within its branch network.

Regulatory bodies also use GIS data to assess the location of bank branches during M&As. This ensures that M&As that may lead to monopolies in certain regions are not encouraged.

In the event of a potential closure of a branch, banks can use GIS data to evaluate the absorption of the business in neighboring branches.

Combining GPS with location information, banks can run an efficient fleet of delivery vehicles to transport money to their ATMs, schedule their deliveries, and select routes based on traffic-flow information.

GIS can help banks undertake asset management in a cost-efficient manner, helping them to track the cash flow in their bank machines, the location of mortgaged properties, etc., accurately.

Financial fraud often follows a pattern of card-based transactions that occur in disparate locations in short succession. GIS applications can help retail banks flag transactions that emanate from suspicious areas and protect commercial banks and consumers from card theft, cloned cards, and cloning syndicate schemes.

Lenders study GIS information to understand an area’s typology and rainfall patterns when processing agricultural loans. Similarly, commercial property values of surrounding areas are evaluated before approving property loans.

Gramener’s Geospatial Analytics Solutions

Reducing Urban Heat Island with Geospatial Technology

Due to urbanization, certain urban areas experience higher temperatures than their surrounding areas, increasing energy consumption, and heat-related ailments. These areas are called Urban Heat Islands (UHI).

To mitigate the impact of the heat generated in these areas, authorities have to identify the UHI pockets, which is not an easy task.

Gramener partnered with Evergreen Canada, a non-profit, and Microsoft to build an AI-driven analysis and visualization solution on the Microsoft Azure stack. The ‘AI for Resilient City’ solution integrates different datasets like weather, infrastructure, building footprints, and satellite imagery to give a single source of truth. This Urban Heat Island effect solution helps Canadian municipalities identify problem areas and plan evidence-based interventions to combat climate change.

The project was piloted in the Canadian city of Calgary. Using the tool, the municipality was able to take measures to alleviate the extreme heat at street level, helping build a resilient city. The scalable solution can also be rolled out to any other Canadian city.

Download urban resilience with Geospatial Analysis case study

Identify Flood Risk Zones Using the Predictive Abilities of Geospatial AI

Disaster planning and preparedness with AI is possible with GeoAI capabilities. Gramener collaborated with SEEDS, a disaster management non-profit, and Microsoft to develop Sunny Lives, a Geospatial-based AI predictive engine that identifies vulnerable housings in disaster-prone areas and helps save lives.

The model was trained using the logic that the roofing material of a house reflects the socio-economic conditions of its inhabitants, thereby underscoring their vulnerability in the event of a natural disaster.

In the weeks preceding cyclone Yaas in India in 2021, the model was able to identify vulnerable housing with more than 90% accuracy. This enabled the authorities to evacuate 1,100 families to safety.

Download Disaster Planning With AI Case Study.

Combating the Spread of Diseases with Geospatial AI

Mosquito-borne diseases such as chikungunya, zika, yellow fever, and dengue threaten around 40% of the global population. To counter this, the World Mosquito Program (WMP) genetically modifies mosquitoes using natural bacteria to reduce their disease-carrying abilities.

When these lab-modified mosquitoes mate in the open, their offsprings do not carry disease-carrying abilities either.

For the engineered-procreation activity to succeed, the modified mosquitoes have to be released in locations that maximize their chances of mating. Identifying these locations manually is a time-consuming and expensive process.

Working with Microsoft and WMP, Gramener developed an AI solution using computer vision models on high-resolution satellite images. The resulting model cut manual effort by more than 99% and delivered 70% higher accuracy.

Download the WMP case study.

Predicting The Quality of Life Using Geospatial AI

Spatial analysis use cases in the public health domain are contributing to saving millions of lives. The underprivileged sector of the society that deals with poverty, poor sanitation, and low quality of life benefits from the schemes that government defines using census data and satellite imagery data.

Gramener has developed a solution that can be used to predict the quality of life in a region. Using deep learning to process satellite images combined with census data, we can identify areas with a low quality of life.

Our solution uses metrics like water bodies, availability of agricultural land, industrialization, transportation, healthcare facilities, employment & education to make accurate predictions. Local government bodies can formulate strategies that benefit underdeveloped regions using this data.

Download our latest whitepaper on predicting quality of life with satellite imagery.

Conclusion

Geospatial Analytics enjoys many applications across private industries and public initiatives. It optimizes costs and makes business processes more efficient. It can also be used to drive humanitarian initiatives and save lives.

With technology permeating every aspect of our daily lives, the role of geospatial tools will likely increase in the future, enriching the world around us and making it a better place for all of us.

geospatial analysis data and ai solutions by gramener

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Gramener

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.