What Is Spatial Analysis in GIS? Complete Beginner's Guide
Updated: Jul 31
Spatial analysis has become the strongest tool of Geographic Information Systems (GIS). Using geographic data, it allows to reveal patterns, relationships, trends, and other valuable information related to geographic phenomena.
Spatial analysis is effective in solving several tasks, including studying urban growth, analyzing risks of flooding, managing delivery and transportation routes, controlling woodlands, or locating suitable areas for construction purposes.
What Is Spatial Analysis?
Spatial analysis refers to the analysis of attributes, places, and relationships of geographical features. Similarly to traditional data analysis, spatial analysis allows answering various spatial questions.
However, spatial analysis’s approach is different from past analysis. In traditional analysis, one can ask only how many something is. For example
How many hospitals are there?
In contrast, spatial analysis allows for asking:
What neighborhoods are located the farthest from hospitals?
What hospitals serve the biggest population?
What areas do not have any proper emergency service?
This way, spatial analysis allows making intelligent data-driven decisions.
Why Is Spatial Analysis Important?
Geographic information can be found in nearly all datasets.
Some of the examples are:
Addresses of the customers
Highways
Rivers
Structures
Electricity networks
Climatic centers
Information received from satellites
Data collected from drones
GPS-coordinated roads
Spatial analysis allows one to assess how these factors interact in the geographical sphere.
The list of advantages includes:
Improved decision making
Risk evaluation
Rational use of resources
Nature monitoring
Infrastructure activities
Business intelligence
Predictive modeling
Better effectiveness and efficiency in operations
How Spatial Analysis Works
In general, it can be divided into the following stages:
Collecting Geographic Data
There are many sources of information, such as:
GPS
Satellites
Drones
LiDAR
Geographic databases
OpenStreetMap database
Census database
Data Management
Before the analysis, data has to be prepared:
There should be no mistakes; data should be checked, and formats should be changed.
Using Tools for Spatial Analysis
Different calculations that can be made with the help of GIS software are the following:
Measuring distances,
Creating buffers,
Overlay analysis,
Network routing,
Surface analysis,
Density estimation,
Understanding the Results
Maps, charts, dashboards, and reports would help the user understand the results.
Types of Spatial Analysis
Buffer Analysis
Buffer analysis process involves creating zones around specific geographic features.
Example:
A 500-meter buffer around the school can be created in order to see which parks are nearby.
Applications:
For emergency planning
For service area mapping
For environmental protection
For site selection
Overlay Analysis
The overlay analysis process involves overlapping different layers of maps to find out relationships between them.
Example:
This can be done by combining:
Soil type
Elevation
Rainfall
Land use
So that the best areas for agricultural use can be determined.
Applications:
Land suitability
Conservation planning
Urban planning
Proximity Analysis
Proximity analysis is the process of determining how close the features are to each other.
Sample questions that can be answered:
What is the closest hospital?
Which houses are located in the flood zone?
How far is the closest fire station?
Network Analysis
Network analysis examines interconnected systems such as:
Roads
Railways
Pipelines
Utility networks
Applications:
Route optimization
Delivery planning
Emergency response
Logistics
Hotspot analysis
Hot Spot Analysis
Hotspot analysis detects clusters that are statistically significant.
Examples include:
Crime hotspots
Epidemics
Road accidents
Market.
Density Analysis
Density analysis determines how much a feature is concentrated in a certain area.
Applications include:
Population density
Trees density
Crime density
Cell towers coverage
Terrain Analysis
Evaluation of terrain makes use of elevation models, which include:
DEM
DSM
DTM
Common evaluations:
Slope
Aspect
Hillshade
Watershed
Visibility
Contours
Interpolation
Interpolation
Interpolation finds unknown values between observed data.
Examples:
Rainfall prediction
Temperature mapping
Pollution assessment
Soil moisture
Normal methods are:
Inverse Distance Weighting (IDW)
Kriging
Spline
Common Spatial Analysis Techniques
Common GIS analysis techniques consist of:
Buffering
Spatial joins
Overlay analysis
Raster processing
Vector analysis
Viewshed analysis
Hydrological models
Cost-distance analysis
Least cost path analysis
Clustering analysis
Kernel density estimation
Spatial interpolation
Terrain modeling
Change detection
Multi-criteria analysis
Popular GIS Software for Spatial Analysis
A few examples of popular GIS software tools are as follows:
ArcGIS Pro
GRASS GIS
GeoServer
Global Mapper
SAGA GIS
WhiteboxTools
Google Earth Pro
Programming libraries consist of:
Spatial Analysis with AI
The advent of Artificial Intelligence is revolutionizing the manner in which spatial analysis is conducted.
GeoAI capitalizes on GIS technology in order to process huge sets of geographic data using machine learning algorithms.
Some applications this technology may be used for are:
Detection of objects
Classification of land cover
Extraction of roads
Identification of buildings
Detection of changes
Mapping of floods
Observation of crops
Analysis of forests
Deep learning technologies allow for analyzing thousands and thousands of satellite photos in no time compared with human labor.
The basis of modern Geographic Information Systems is the use of spatial analysis. By analyzing natural features, organizations are able to solve complex problems, strengthen the decision-making process, and discover important information.
With the volume of geospatial datasets growing due to satellites and drones, spatial analysis is of increasing relevance in different branches, including urban studies, agriculture, ecology, transportation, health care, and business. When combined with cloud technologies and GeoAI, spatial analysis becomes a powerful tool for forecasting.
For more information or any questions regarding Spatial Data Analysis, please don't hesitate to contact us at
Email: info@geowgs84.com
USA (HQ): (720) 702–4849
(A GeoWGS84 Corp Company)
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