What Is Spatial Analysis? Definition, Working, and Examples

Spatial analysis utilizes spatial data to derive results based on the geography of the target area.

August 17, 2022

Spatial analysis is defined as the process of studying entities by examining, assessing, evaluating, and modeling spatial data features such as locations, attributes, and their relationships that reveal the geometric or geographic properties of data. This article explains the principles of spatial analysis, how it works, and a few examples.

What Is Spatial Analysis?

Spatial analysis refers to studying entities by examining, assessing, evaluating, and modeling spatial data features such as locations, attributes, and relationships that reveal data’s geometric or geographic properties. It uses a variety of computational models, analytical techniques, and algorithmic approaches to assimilate geographic information and define its suitability for a target system.

Spatial analysis is relevant to astronomy, wherein the process is used to study, explore, and understand the position of the star system in our infinite cosmos. It is also a part of the chip fabrication process where ‘place and route algorithms’ are used to develop wiring structures and frameworks. Apart from these, spatial analysis is crucial in healthcare, agriculture, urban ecosystem management, disaster warning and recovery, supply chain and logistics modeling, and several other fields.

Fundamentally, spatial analysis derives insights from spatial data, which provides a numerical representation of any physical entity in a geographic coordinate system. It serves as a spatial unit of a map. Spatial data is classified into two basic types, namely, geometric and geographic.

Geometric data maps the spatial information onto 2D surfaces. In contrast, geographic data representing the latitude and longitude of an entity or location is overlaid on a sphere, where the sphere represents the Earth. For example, Google Maps is an application that uses 2D data to provide navigational routes to its users, while GPS devices give us detailed geographic information.

Apart from these, georeferencing and geocoding are other vital aspects of geospatial analysis. Georeferencing models the Earth’s surface by assigning data coordinates to vectors or raster, while geocoding pinpoints exact addresses such as states, countries, etc., on the planet’s surface.

Spatial data

Spatial data can be expressed in several formats, including vector and raster. Vectors graphically represent the real world through points, lines, and polygons. With the help of points, one can create lines, which further give rise to enclosed spaces in the form of polygons. These vector data types allow the graphical mapping of physical objects found on the Earth’s surface. The data is kept in shapefiles (.shp).

On the other hand, raster gives data a pixel grid representation, wherein each pixel reflects specific information such as color, measurement unit, etc. Conventionally, raster denotes imagery; however, in the context of spatial analysis, it refers to aerial photographs or satellite images, known as ‘orthophotographs’.

Apart from vector and raster, non-spatial data is represented by attributes. It refers to add-on information to spatial data, for example, images, maps, or additional information that adds value to the spatial data of the object under consideration.

Importance of spatial analysis

Spatial analysis is more than only the mapping of physical objects. As spatial information is collected from multiple sources, decision-makers can use the consolidated version of this spatial data to take action based on the need. For example, spatial analysis can aid in traffic management in an urban setting, thereby allowing authorities to take steps toward building resilient cities.

The technology can also allow users to check on the geographic progression of the disease and thereby invest in efforts to control its spread. Moreover, government bodies can use spatial data to map vaccination strategies for a geographical area during a deadly pandemic, such as COVID-19.

Today, in this modern world, spatial analysis has become an essential part of our everyday routine. From booking a cab on an app, tracking our ecommerce or food deliveries, to accessing GPS to travel from one place to another, spatial analysis is now observed as an omnipresent technology.

Although spatial data such as satellite pictures have been in place for long, filtering through vast volumes of data to generate usable information has been quite a task. However, with artificial intelligence (AI) and machine learning (ML) coming to the fore, spatial analysis has become significantly accessible, boosting profits for businesses and saving lives at times.

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How Does Spatial Analysis Work?

Spatial analysis is a crucial component of the Geographic Information System (GIS). It is primarily used to determine the feasibility of a location for certain systems or to make weather predictions for a specific geographic area. It allows users to model problems and find comprehensive solutions that have geographical attributes associated with them.

Let’s understand the working of spatial analysis in detail:

1. Data collection

Data collection is fundamental for the spatial analysis process. It includes data gathering from various sources, including remote sensing devices such as LiDAR (light detection and ranging) and airborne systems.

Data acquired by such devices is used for building maps that reveal the geographic distribution of entities under consideration, for example, a map showing temperatures for different regions. Data, in this case, includes high-resolution images or photographs taken by satellites or aerial systems.

2. Data analysis

In the second step, the collected data is analyzed using AI and ML solutions to derive results. Moreover, one can train ML models to detect objects or structures in an area when millions of images are analyzed.

For example, objects can include colleges, playgrounds, traffic zones, residential areas, etc. Additionally, one can use visualization tools to highlight different objects with different colors, shapes, or annotations. Such tools ease the process of object identification within large chunks of data.

3. Data presentation

Post-analysis data presentation can be time-consuming as one needs to highlight crucial elements that reveal the findings. Such tasks are made easier with data visualization tools that use tables, charts, and graphs to project relevant data and communicate with concerned stakeholders.

Moreover, 3D visualization tools add variables to 2D data and give a better perspective. Such practices optimize planning and implementation strategies, which results in better solutions for the modeled problems.

Spatial analysis uses these three steps to analyze and aggregate data to solve complex problems. Let’s look at some of the critical capabilities of spatial analysis workflows:

1. Helps in geographic search

Spatial analysis allows one to visualize specific data onto maps leveraged via interfaces. The user can search for relevant geographic data through dashboards using elements such as city name, country, zip code, etc. With such a search facility, one can easily identify the spots of interest in a region, such as schools in an area.

2. Clustering of datasets

The spatial analysis enables the clustering of data, which helps authorities understand demographic commonalities by looking at the density of projected data points. For example, governing bodies can use maps to comprehend the distance between two schools in a region. One can use such data to decipher whether certain areas have easy or no access to school facilities.

3. Provides comprehensive data view

By using a variety of colors, shapes, and annotations, one can get a bird’s eye view of an area. For example, hospitals, colleges, and car repair shops can be marked and annotated differently on the map to give a comprehensive view of the geographic data.

4. Enables visual mapping

Users can represent datasets on maps by using layers, such as those observed in heatmaps or bubble charts. For example, weather data can be presented on different layers, facilitating visual mapping.

5. Underscores target entities

Different types of data can be presented on simple graphs by combining the data projected on the layers in the map. For example, one can combine the population of a region and clinic facilities in the nearby area to determine whether there are an appropriate number of health centers for the people in the said neighborhood.

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Examples of Spatial Analysis

Today’s modern and connected world is driven by advanced technologies such as the internet of everything and IoT that rely heavily on spatial analytics for day-to-day operations. Moreover, numerous government and private agencies use location-specific data to successfully run social welfare programs and ensure the well-being of the regional population.

According to a June 2022 report by ReportLinker, the global location-based services (LBS) market stood at $55.92 billion in 2021 and is expected to reach $70.16 billion in 2022. Moreover, if the current trend is to be believed, the location-based services economy is forecasted to grow to $114.9 billion by 2026.

These stats only reveal the meteoric rise in spatial analysis technology usage. Leaders in location-based services include tech giants such as Apple, Google, Intel, Microsoft, Oracle, and others who continue to pour in resources and efforts to exploit the LBS market.

Examples of spatial analysis include:

1. Urban planning & development

Spatial analysis plays a crucial role in urban planning and developmental activities. Let’s understand some of the projects that come under this:

  • Develop resilient urban cities

The adverse consequences of climate change plague urban life. City authorities are continually trying to find solutions to minimize its impact on city dwellers. That’s where technologies such as GIS, which offers geospatial information, come to the rescue. Policymakers can use GIS, keeping in mind the sustainability goal.

Some of the GIS-based programs include:

  • Plan and strategize deforestation programs based on analyzed data
  • Keep track of industrial emissions to decide upon the necessity of using a renewable energy source
  • Keep a check on rising sea levels due to global warming factors to implement an action plan to negate the threats

A company called Evergreen is striving to develop green and resilient cities in Canada using AI. It aims to collaborate with municipalities and plan steps to reduce climate change’s effects on cities across Canada.

  • Monitor the urban heat island (UHI) effect in cities

The urban heat island (UHI) effect refers to the phenomenon where natural vegetation is taken down to build apartments and structures that tend to retain heat for longer. As it is one of the grave problems that humanity needs to tackle, technologies such as spatial analysis can help. For example, measures such as satellite imagery collected via the Landsat program, thermal remote sensing, and field observational studies can aid in developing a better understanding of how the UHI effect is causing a specific spatial pattern. One can identify the cause of the UHI effect and take appropriate action.

  • Determine the quality of life

Geographical spatial data allows one to detect the data pattern that can reveal the socioeconomic quality of life of people living in a region. For example, spatial patterns can highlight the presence of hospitals, schools, government offices, etc., within an area. An even distribution of such infrastructure shows that the region has all the facilities necessary to lead a good quality of life. 

Users can process satellite data by using machine learning and deep learning techniques. The analyzed data can provide insights into the relationship between factors such as poverty, literacy, education, healthcare, and others.

  • Traffic flow analysis

Traffic flow and congestion can be modeled to reveal glitches in a transportation system. With spatial imagery, roads that face constant congestion and run above capacity limits can be identified. Moreover, it is observed that people with low income do not generally possess independent vehicles, thereby causing difficulty in transit for such groups.

Additionally, without proper public transport facilities, they are further handicapped as it makes it even more difficult to access employment places or other amenities. Thus, spatial analysis helps identify all these loopholes in urban areas and enables authorities to take immediate remedial action.

2. Public health management

Spatial analysis is used by various health and government authorities for public health management.

  • Map disease spread

Satellite findings are crucial to predicting disease spread across regions. Such spatial data patterns allow policymakers to keep a check on disease spread by taking preventive steps.

More so, one can combine weather variables such as rainfall or temperature with disease data to understand better the weather’s effect on disease spread or prevalence in different areas. Typically, in the case of waterborne diseases, temperatures and the presence of water bodies such as lakes and rivers in the vicinity are crucial to understanding disease progression.

The World Mosquito Program (WMP) is one such initiative run by a non-profit unit that collaborates with local communities to reduce the spread of mosquito-borne diseases. The program uses machine learning and spatial analysis technology for its operation.

  • Analysis of sanitation & health facilities

With spatial analysis, one can map regions with direct access to sanitation facilities. This can include mapping wastewater collection systems, water pumping stations, and waste treatment plants within geography. The data can reveal the population that has such services at their disposal. Moreover, concerned authorities can use such spatial information to brainstorm and implement additional sanitation services wherever necessary for the community’s well-being.

Similarly, authorities can map health facilities such as clinics and hospitals through GIS systems. The data can identify the population that does not have access to these healthcare services. Depending on the requirement, respective authorities can plan healthcare programs using spatial maps.

  • Map vaccination stats

The COVID-19 pandemic has been one of the most significant challenges faced by the world, and more so by the healthcare sector, as vaccinating people was the only way out of this problem. But how does one keep track of such vaccination programs? Governments can handle this scenario well with the use of GIS technologies. They can track vaccine distribution with spatial analysis and ensure uniform coverage around localities. Thus, spatial analysis is key to such wide-scale vaccination programs.

3. Agriculture & farming

In agriculture, spatial analysis enables crop monitoring, predicting crop yield, soil analysis, and so on.

Let’s look at each use case in detail:

  • Crop monitoring

Crop productivity results from multiple variables working in sync with each other, such as climate, soil nutrients, sunlight, etc. These factors vary across geographical regions. Crop monitoring is better facilitated by capturing GIS data through remote sensing. It also helps track crop performance, distribution, and environmental variations that affect crop yield.

  • Predict crop yield

As climate change continues to bother the world, crop production has also been significantly hit. Farmers can better study crop yield prediction through satellite-gathered images that reveal weather data (wind, temperature, air pressure) along with soil conditions. Moreover, one can determine the possibility of any potential pest attacks based on satellite imagery data. When combined, all these factors can help determine the appropriate time for crop harvesting and other allied processes.

  • Monitor farm animals

Farm animals are critical to an agricultural setup. Freely moving cattle can be monitored with spatial analysis. Moreover, livestock research has shown that such animals produce methane, which is responsible for global warming. Furthermore, livestock also contaminates farm soil by releasing nitrogen. The soil then mixes with water bodies such as rivers and pollutes them considerably. Considering such environmental impacts, one can employ GIS tools to study the damage caused to the natural ecosystem at places where livestock move around.

  • Soil nutrient analysis

Soil nutrients are vital to carrying out successful agricultural processes. With spatial analysis and GPS tools, farm monitoring has become easy. Soil specialists can analyze spatial data to determine soil properties such as pH level, nitrogen content, and other variables. Geospatial analysis, along with interpolation methods such as linear interpolation, helps in deciding unknown values between known values, thereby revealing the spatial variability in soil features.

This information allows concerned authorities to implement practices that restore soil nutrients and help better soil management. Depending on its nutritional content, farmers can also plan to add appropriate fertilizers to the soil.

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Takeaway

Spatial analysis has become an inevitable part of several industries across disciplines, including healthcare, astronomy, banking, forestry, supply chain, government and non-government institutes, and plenty others. National and state agencies employ complex spatial algorithms to analyze geographic information and plan strategies to implement programs. Moreover, this data-backed approach ensures that such programs are completed successfully, which is critical when they are related to public welfare.

Spatial analysis technology allows organizations to set out fixed plans that optimize their overall finances and ensure that the programs benefit the masses.

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Vijay Kanade
Vijay A. Kanade is a computer science graduate with 7+ years of corporate experience in Intellectual Property Research. He is an academician with research interest in multiple research domains. His research work spans from Computer Science, AI, Bio-inspired Algorithms to Neuroscience, Biophysics, Biology, Biochemistry, Theoretical Physics, Electronics, Telecommunication, Bioacoustics, Wireless Technology, Biomedicine, etc. He has published about 30+ research papers in Springer, ACM, IEEE & many other Scopus indexed International Journals & Conferences. Through his research work, he has represented India at top Universities like Massachusetts Institute of Technology (Cambridge, USA), University of California (Santa Barbara, California), National University of Singapore (Singapore), Cambridge University (Cambridge, UK). In addition to this, he is currently serving as an 'IEEE Reviewer' for the IEEE Internet of Things (IoT) Journal.
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