Spatial Analysis — An Introduction & Overview (Use Cases, Examples & Process)
Spatial analysis is one of the vital aspects of GIS (Geographic Information System). It allows you to use independent sources and conclude results through a set of spatial tools. As data-driven decision-making is on the rise, the application of spatial across businesses and non-profits has increased manifold in recent years.
If you work closely with location-based data, you must have come across the term spatial analysis already. This article looks at spatial analysis, its importance, challenges it solves, use cases across industries, and much more.
What is Spatial Analysis?
We are living in an interconnected age where the role of data is immense. As our reliance on technology increases with each day, a significant amount of information is getting generated. The quality and depth of data today have improved immensely. According to McKinsey, the Internet of Things (IoT) will impact the economy by more than $11 trillion per year by 2025.
Location intelligence is crucial for problem-solving and decision-making capabilities in this era where billions of unknown people are connected through the internet. It brings invaluable context that you cannot otherwise find. It includes patterns or connections between customers’ buying habits, supply chain management strategies, etc.
The internet remains crowded with data, and any given location may have troves of it. The information can include anything from agricultural inputs to transportation routes, all the way down to crime rates.
This data available with researchers paves the way for new insights. You can use the analysis in conjunction with other resources like addresses or coordinates. These datasets provide an exciting map that takes you beyond just looking at points on a globe.
Importance of Spatial Analysis
The power of GIS lies in its ability to combine information from multiple sources and use advanced spatial analysis. As you may already be aware, the process offers results through spatial operators. It helps you find answers to complex queries. The methodology can help you determine if there are significant patterns between variables.
When it comes to deciding about choosing project-related locations, the best place will depend on several factors. What are your needs? How feasible is the site? You cannot always answer these questions with just visuals, which is why ArcGIS offers various ways to analyze data at any given moment.
The spatial analysis finds its applications for a variety of reasons like:
- 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
Here are some examples of how companies employ spatial analysis to address various issues:
Spatial Analysis for Public Health
An effective healthcare system always remains a challenge for most developing countries. Populations contribute to the growing challenges of healthcare institutions, leading to the sparse availability of resources for everyone. The situation gets compounded further when there is black-marketing for healthcare aid.
A high population also has detrimental effects on quality of life. Beneficiaries of public welfare schemes may lament receiving too little help even if some does arrive at all. Population density mapping is thus the key to success for addressing healthcare challenges in developing countries.
AI helps non-profits for this purpose, ensuring aid reaches people who need assistance while also not falling short of the expectations. Spatial analysis tools like predictive modeling and pattern analysis help address ground-level challenges at ease.
With spatial analysis in public health, healthcare professionals can check if climate change and urbanization are impacting people. Researchers may also look at where it is most crucial for bettering healthcare to ensure there isn’t any inequity among different regions or countries globally.
Another use of spatial analysis in the public health space is the Data for Good initiative of Facebook. One of the vital aspects of this program is the use of population density mapping. The initiative is helping Facebook create accurate population datasets worldwide. Facebook’s Spatial Computing team uses pattern-recognizing algorithms for mapping populations.
They can then locate buildings with the help of satellite imagery data. Trained algorithms then study various landscapes for precise modeling.
Spatial Analysis for Urban Resilience
Urban planning can pose several challenges for planners, including making cities resilient. It is especially critical in light of climate change events like droughts and heatwaves. Resiliency ensures that the impact of these events on people is minimum. GeoSpatial AI and urban mapping are some vital tools to tackle this problem.
Using these tools effectively can be difficult due to several issues. These include disparate sources of information, lack of clear insights, forecasting complexities, and scalability. Spatial data visualization tools can solve these issues. It can comprehensively track, predict, and visualize the effects of climate change, including the creation of urban heat islands.
Planning bodies can also use the tool to gauge the effects of other climate-related changes to make cities more resilient. Besides Geospatial Analytics, AI and big data also chip in to gather data from multiple sources in one place. The datasets include weather, satellite imagery, infrastructure, socioeconomic indicators, etc. These form a single basis of truth.
With all the data consolidated in one place, you can also seamlessly track the relationships between them. The visualization tool also offers insights in various formats that enable you to get an in-depth view. By studying the impact of each building at a micro-level, it becomes easier to understand the heat island effect on a particular geography.
Spatial Analysis for Social Causes
Even as the world continues to evolve rapidly, several social issues often hamper growth. Urban poverty and its impact on housing inequality are one of the many related issues. High-resolution satellite imagery data and machine learning can help identify urban poverty in geography at the community level.
When applied with machine learning techniques, image features like line segment detector, histogram of oriented gradients, and local binary patterns help identify urban poverty. Similarly, researchers can also find relatively critical variables.
Housing inequality at the community level can be due to the gaps in wealth and income. However, it is still a bright indication of urban poverty. Planning bodies and government agencies can use this data to identify urban poverty. They can then use the data to address housing inequality and enable rationality in urban planning. It will lead to the creation of a more sustainable society.
Spatial Analysis for Agriculture
Agriculture is another field that stands to gain immensely from spatial analysis due to the issues it faces. The unpredictability of climate is one of the crucial factors that affect the crop production rate. Forecasting of weather is thus vital for improved management of crops. Related industries like sugar, which relies on sugarcane, also stand to benefit from this.
Crop yield prediction with ML helps in logistics planning as well. When we specifically talk of crop yield prediction, you can do it through satellite imagery, climate data, soil conditions, and pest attacks. Besides, AI and ML can also enable remote monitoring of farms, resource and field mapping, predictive analytics, and much more.
The AgTech industry is also using neural network algorithms to identify crop yield. Backpropagation algorithms help calculate the error derivative through the yield’s appropriate weight value. Predicting crop yield is critical to keep tabs on the food production ecosystem worldwide.
Planning bodies can make better and informed decisions with data available to them. Government agencies can also arrange for national food security through these tools.
Spatial Analysis for Disaster Management
GIS in the disaster management field is increasingly finding acceptance around the world. When a disaster strikes, time is critical for emergency management operations. To offer prompt response, emergency managers need to decide quickly on the best course of action. Geospatial data, fortunately, help them across various phases of emergency management operations.
The scale of a disaster can also affect the gathering of critical data for any emergency manager. They thus have to rely on disaster management scenarios and simulate the action plan. GIS helps overcome this problem as it reduces the role of perception. Disaster managers can thus have well-defined response patterns.
Besides helping in disaster management planning, GIS also helps with tabletop activities and the tasks of Emergency Operations Centers (EOCs). Disaster managers can maintain situational awareness, allocate resources responsibly, alert communities, and have what-if scenario modeling.
Spatial Analysis Examples for Key Industries
Gramener is at the forefront of using Spatial Analytics to support the data science life cycle of a project and create impactful Data Apps. As a Microsoft Gold Partner, we have collaborated with the technology giant on multiple projects under the “AI for Earth” initiative.
We provide solutions to our global clients that help them combat deadly diseases, farm digitally and efficiently, and build resilient and smart cities. We analyze GeoSpatial datasets by using GeoSpatial mapping, satellite imagery, and remote sensing technologies.
Building Urban Resilience With GeoSpatial Technology
Evergreen is a non-profit organization dedicated to increasing the urban resilience of cities in Canada. It wanted to study the effect construction projects had on the environment. This would enable the company to undertake construction without causing adverse effects such as climate change, etc.
Under Microsoft’s AI for Earth initiative, Gramener partnered with Evergreen to provide urban resilience solutions. With the help of the AI for Resilient City Data Visualization Tool, we used spatial data and publicly available satellites to aid the local authorities in preventing climate-related hazards such as earthquakes, flooding, etc.
Locating Elephants in the Wild With Satellite Imagery
Every 15 minutes, poachers kill an elephant in Africa. Working with Save the Elephants initiative, Gramener created a solution to aerially detect elephants and transmit their location to the authorities. Our deep learning model processed thousands of images to identify elephants and other livestock. Our tool served to curb elephant poaching in the area substantially.
Predicting Quality of Life (QoL) from Satellite Imagery
Predicting social living standards through satellite imagery can facilitate effective decision-making for urban planning and management. Vegetation, water, and land coverage can be monitored using remote sensing data analysis.
Gramener can leverage deep learning across metrics such as agriculture land, unemployment, literacy, and population density using its QoL tool. This can be used to map US counties against 3 QoL classes.
Population Density Mapping to Fight Mosquito-Borne Diseases
The World Mosquito Program (WMP) faced tedious and monetary challenges in identifying spots that would be ideal for releasing modified mosquitoes. Partnering with Microsoft, Gramener used GeoSpatial AI to save millions of lives and prevent the transmission of mosquito-borne diseases.
Using computer vision models on satellite images, our award-winning solution gauged the human population density of an area to develop an effective release plan.
Disaster Warning and Recovery with AI
Gramener collaborated with Microsoft and SEEDS India to develop a model for Flood Risk Assessment. Its objective was to predict flood risk during a cyclone. Using satellite imagery, each roof type was classified. A risk score from 1 to 5 was generated using several influencing measures. This model identified at-risk houses with 96 percent accuracy, and effective interventions were planned ahead of the disaster.
Key Capabilities of Spatial Analysis
Spatial analysis data is more than just visualizing data on maps. They allow you to analyze and aggregate data for solving the most pressing issues. Let’s look at the various 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
Here are the various benefits of using 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?
There are many reasons why GIS is powerful. You can use it for analysis, such as modeling problems and getting answers by processing them geographically. It is effective in feasibility studies of locations or predictions on outcomes related to weather. You can create models of your problems and use processing techniques to get results. Based on the findings, you can then arrive at a suitable conclusion.
Applying spatial analysis isn’t difficult, even if you are new to the field of GIS. Spatial analysis workflows allow you to get comprehensive solutions. Let’s look at various steps involved in the process.
Step 1 — Data collection
It is the primary task for spatial information users. The collection of data here can include remote sensing data processing and field data. Several sources allow you to gather geospatial data. These include remote sensing data and light detection and ranging as primary sources.
You can generate qualitative land-use maps through high-resolution satellite images and aerial photographs. Medium-resolution satellite images are ideal for quantitative land-use maps. If you are looking for topographical characteristics for GIS analysis, you can use light detection and range.
Step 2 — Data analysis
You can leverage AI and ML-based tools to analyze the collected data. If you have millions of high-resolution satellite images, you can train an ML model to identify objects in a particular locality. These could include buildings like hospitals, schools, and housing societies. You can use visualization and analysis tools to mark these buildings in various shapes and colors.
Step 3 — Data Presentation
Projection is one of the crucial elements, which can also take up a lot of your time. You can use visualization tools like charts, graphs, and tables to understand results and communicate findings. When you represent data visually, there are several geographic elements you need to consider.
You can also use 3-D data visualization tools that add a new perspective to the information you want to communicate. By getting a holistic view of objects, your planning and implementation efforts will lead to better results.