What is Spatial Analysis and How Does Spatial Analysis Work
What is Spatial Analysis?
Spatial analysis is the study of entities through the examination, assessment, evaluation, and modeling of spatial data features, such as attributes, locations, and relationships, that reveal data’s geographic or geometric properties. This method uses analytic methods, computational models, and algorithmic approaches to assimilate geographic information and determine whether it is suitable for the target system.
Astronomy is a field that requires spatial analysis. It is used to explore and study the position of our infinite universe’s star system. The ‘place-and-route algorithms’ used in the chip fabrication are also used to create wiring structures and frameworks. Spatial analysis is also important in other fields, such as healthcare, agriculture, and urban ecosystem management. It’s also used for disaster warning, recovery, and supply chain and logistic modeling.
Spatial analysis is based on spatial data. This provides a numerical description of any physical entity within a geographical coordinate system. It is used as a unit for a map. Geometric and geographic data are the two main types of spatial data.
Geometric data is a mapping of spatial information on 2D surfaces. Geographic data that represents the latitude and longitude of a location or entity is then overlaid onto a sphere. The sphere is the Earth. Google Maps, for example, uses 2D data in order to give its users navigational routes, whereas GPS devices provide detailed geographic information.
Georeferencing and geocoding, in addition to these two aspects, are also vital for geospatial analyses. Georeferencing is a way to model the Earth’s surface using vectors or rasters, while geocoding pinpoints specific addresses on the planet, such as states, counties, etc.
Vector and raster are two formats in that spatial data can be expressed. Vectors are a graphic representation of the world, using points, polygons, and lines. Points can be used to create lines that then lead to polygons. These vector data types allow the graphic mapping of the Earth’s surface of objects. Shapefiles (.shp) are used to store the data.
Raster, on the other hand, is a grid of pixels that represents data. Each pixel contains specific information, such as color or measurement unit. In the traditional sense, a raster is an image; however, when used in spatial analysis it is a reference to satellite or aerial images.
Attributes are used to represent non-spatial information, such as images and maps. This is information added to spatial data. For example, images, maps, or other information that enhances the value of spatial data.
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Importance of Spatial Analysis
Spatial analysis goes beyond the mapping of objects. Because spatial information comes from many sources, decision-makers can use this consolidated data to act based on their needs. For example, Spatial analysis can help with traffic management in urban settings, which allows authorities to build resilient cities.
Users can also use the technology to track the geographical progression of a disease and invest in efforts to stop its spread. Government bodies can also use spatial data in order to map vaccination strategies during a deadly epidemic, such as COVID-19.
In today’s modern world, spatial analyses have become a vital part of our daily routine. You can track your e-commerce, food, or taxi deliveries using an app. It is used to navigate from one location to another using GPS. Spatial analysis has become a ubiquitous technology.
Satellite images and other spatial data have been around for many years.
Filtering through huge volumes of data and generating usable information was a difficult task. With artificial intelligence (AI), machine learning (ML), and other technologies, spatial analysis is now much easier to do. This can boost profits for businesses, and even save lives.
Also read: What Is the Data Analysis Process? 6 Key Steps to Follow
How Does Spatial Analysis Work?
The Geographic Information System (GIS) is not complete without spatial analysis. It is used primarily to determine whether a certain system can be installed in a particular location or to predict the weather for a particular geographic area. It allows users to model problems and find comprehensive solutions that have geographically based attributes. Let us understand spatial analysis in detail:
1. Data collection
The spatial analysis process is not complete without data collection. Data is collected from a variety of sources including LiDAR devices (light detection range) and airborne systems.
The data acquired by these devices can be used to build maps that show the geographical distribution of entities. For example, a temperature map for different regions. In this case, data includes images or photos taken by satellites and aerial systems.
2. Data Analysis
The collected data is analyzed by AI and ML to produce results in the second stage. When millions of images are analyzed, the ML model can be trained to detect structures or objects in a given area.
You can use objects such as colleges, schools, parks, roads, residential areas and more. Using visualization tools, you can highlight objects using different shapes, colors, and annotations. These tools make it easier to identify objects within large data sets.
3. Data Presentation
It can take a lot of time to present data after analysis, as you need to highlight the most important elements. Data visualization tools make it easier to communicate relevant data with stakeholders by using charts, graphs, and tables.
In addition, 3D tools can add variables to 2D information and provide a more accurate perspective. These practices optimize the planning and implementation of strategies, resulting in better solutions to modeled problems.
These three steps are used to analyze and aggregate information to solve complex problems. We’ll look at some key capabilities of spatial analytics workflows.
1. Helps in geographic search
Interfaces allow for spatial analysis, which allows users to visualize data on maps. Dashboards allow users to search for geographic data using city names, countries, zip codes, etc. This search feature allows users to easily find places of interest, like schools, in a particular region.
2. Clustering datasets
Spatial analysis allows for the clustering and aggregation of data. This helps authorities to understand demographic similarities by analyzing the density of projected data. Maps can be used by governing bodies to understand the distance between schools within a particular region. Such data can be used to determine whether certain areas are easily accessible to school facilities or not.
3. Provides comprehensive data view
One can see an area from a birds-eye view by using different colors, shapes, and annotations. To give a more comprehensive view, you can mark and annotate different places on the map, such as hospitals, colleges, and car repair shops.
4. Enables visual mapping
Layers, like those seen in bubble charts or heat maps, can be used to represent data on maps. Weather data, for example, can be displayed on different layers to facilitate visual mapping.
5. Underscores target entities
Combining the data projected onto the layers of the map can create simple graphs that present different types of data. You can, for example, combine the number of clinics in a particular area with the population to see if there is an adequate number of centers in that neighborhood.
Also read: IP Geolocation Database Definition
Spatial Analysis Examples
The modern, connected world of today is driven by technologies like the Internet of Everything and IoT. These technologies rely heavily on spatial analysis for their day-to-day operation. Numerous government and private agencies also use location-specific information to run successful social welfare programs and guarantee the well-being of the local population.
ReportLinker’s June 2022 report states that the global location-based service (LBS), the market was valued at $55.92 Billion in 2021 and will reach $70.16 Billion in 2022. If the current trend continues, the location-based service economy will grow to $114.9 Billion by 2026.
Apple, Google Intel Microsoft Oracle are among the leaders in location-based service. Other companies continue to invest their resources and efforts to exploit the LBS market.
Some examples of spatial analysis are:
1. Urban planning & development
In urban planning and development, spatial analysis is crucial. We will now look at some of the different projects that fall under this category:
Build resilient urban cities
Climate change has adverse effects on urban life. The city authorities are constantly trying to find ways to reduce its impact on urban dwellers. GIS technologies, which provide geospatial data, can help. GIS can be used by policymakers to achieve sustainability.
GIS is a powerful tool that can be used to create maps, graphs, and other GIS-based applications.
- Analyzed data can be used to plan and strategize your deforestation program.
- Track industrial emissions and decide whether you need to use renewable energy.
- Check the rising sea level due to global warming to create a plan of action to counteract these threats
Evergreen is a company in Canada, that is using AI to create green and resilient cities. It wants to work with municipalities to plan ways to reduce the effects of climate change on Canadian cities.
Monitor urban heat islands (UHI) in cities
The urban heat-island effect (UHI) is a phenomenon that occurs when natural vegetation has been removed to build structures and apartments which tend to retain more heat. It is a grave problem that humanity must address, and technologies like spatial analysis can be helpful. As an example, satellite imagery collected by the Landsat Program, thermal remote sensing, and field observational study can help develop a better understanding of how the UHI effect is causing specific spatial patterns. You can determine the UHI cause and take the appropriate action.
Determining the quality of life
Geospatial data can be used to identify the patterns of data that reveal the socioeconomic status of the people who live in a particular region. For example, Spatial patterns can be used to highlight the presence of schools, hospitals, and government offices within a particular area. A region with a well-balanced distribution of infrastructure is likely to have all the amenities necessary for a high quality of living.
Machine learning and deep-learning techniques can be used to process satellite data. Analyzing data can reveal the relationships between factors like poverty, literacy and education.
Traffic flow analysis
Modeling traffic flow and congestion is a way to identify flaws in the transportation system. With the help of spatial imagery, it is possible to identify roads that are constantly congested and exceed their capacity limits. It is also observed that low-income people do not own independent vehicles. This makes it difficult for them to travel.
In addition, they will be further handicapped if there is no public transport or facilities, as it is more difficult for them to reach employment opportunities and other amenities. Spatial analysis can help identify these gaps in urban areas, allowing authorities to immediately take remedial measures.
2. Public health management
Various health and government agencies use spatial analysis for public health management.
Map disease spread
Satellite data is crucial for predicting the spread of disease across different regions. These spatial data patterns enable policymakers to prevent the spread of disease by taking preventative measures.
Moreover, you can combine the data on diseases with weather variables like temperature or rainfall to better understand how weather affects disease prevalence or spread in different regions. In the case of waterborne disease, the temperature and presence of nearby water bodies, such as rivers and lakes, are critical to understanding the progression of the disease.
World Mosquito Program is a non-profit initiative that works with local communities in order to reduce the spread of mosquito-borne diseases. The World Mosquito Program (WMP) uses spatial analysis and machine learning technology to operate.
Analysis of Sanitation and health facilities
Spatial analysis can be used to map areas with direct access to sanitation facilities. You can map wastewater collection systems, pumping stations, and waste treatment plants in geography. These data can be used to identify the population who has access to such services. The data can also be used by the authorities to plan and implement new sanitation services in communities where necessary.
GIS systems can also be used by authorities to map out health facilities, such as hospitals and clinics. Data can be used to identify populations that do not have access to these services. According to the requirements, authorities can plan healthcare programs using spatial maps.
Map vaccination stats
COVID-19 has been a major challenge for the healthcare industry, especially in the United States, because the only solution to this pandemic was vaccination. How can one track such vaccination programs effectively? GIS technology can help governments handle this situation. Spatial analysis can be used to track vaccine distribution and ensure uniform coverage in localities. Spatial analysis is therefore crucial to large-scale vaccination campaigns.
3. Agriculture and farming
Spatial analysis is used in agriculture for crop monitoring, crop yield prediction, soil analysis, and more.
Each use case will be examined in more detail.
Multiple variables, including soil nutrients, sunlight, and climate, work together to produce crop productivity. These factors differ across geographic regions. Remote sensing data can be used to improve crop monitoring. It helps to track crop performance and distribution as well as environmental variations which affect crop yield.
Predict crop production
Crop production has been severely affected by climate change. Satellite-gathered images reveal weather information (wind, temperature, and air pressure), along with soil conditions, which can be used to better predict crop yields. Satellite imagery can also be used to determine the likelihood of pest attacks. All these factors, when combined, can be used to determine the best time to harvest crops and perform other related processes.
Monitor farm animal
The importance of farm animals in an agricultural setting cannot be overstated. Spatial analysis can monitor cattle that are moving freely. Research on livestock has also shown that these animals produce methane which is the cause of global warming. In addition, livestock contaminates soils by releasing nitrogen. The soil mixes with rivers and other water bodies, causing them to be polluted. GIS tools can be used to analyze the environmental impact of livestock movement.
Soil nutrient analysis
To achieve a successful farming process, soil nutrients are essential. Farm monitoring is now easy with GPS and spatial analysis tools. Soil experts are able to analyze spatial data to determine soil characteristics, such as pH and nitrogen content. Geospatial analyses, in conjunction with interpolation techniques such as linear interpolation, can help determine unknown values within known values. This reveals the spatial variability of soil features.
This information enables authorities to put into practice practices that help restore soil nutrients. Farmers can plan to add fertilizers to the soil based on their nutritional value.
Spatial analysis is now a part of many industries, such as healthcare, banking, forestry and supply chain management, government and nongovernmental institutes, and others. State and national agencies use complex spatial algorithms to analyze geographical information and plan strategies for implementing programs. This data-backed approach also ensures the success of such programs, which is crucial when they relate to public welfare.
The spatial analysis technology allows organizations to create fixed plans which optimize their finances and benefit all.