Spatial analysis allows you to solve complex location-oriented problems and better understand where and what is occurring in your world. It goes beyond mere mapping to let you study the characteristics of places and the relationships between them. Spatial analysis lends new perspectives to your decision-making.
Have you ever looked at a map of crime in your city and tried to figure out what areas have high crime rates? Have you explored other types of information, such as school locations, parks, and demographics to try to determine the best location to buy a new home? Whenever we look at a map, we inherently start turning that map into information by analyzing its contents—finding patterns, assessing trends, or making decisions. This process is called “spatial analysis,” and it’s what our eyes and minds do naturally whenever we look at a map.
Spatial analysis is the most intriguing and remarkable aspect of GIS. Using spatial analysis, you can combine information from many independent sources and derive new sets of information (results) by applying a sophisticated set of spatial operators. This comprehensive collection of spatial analysis tools extends your ability to answer complex spatial questions. Statistical analysis can determine if the patterns that you see are significant. You can analyze various layers to calculate the suitability of a place for a particular activity. And by employing image analysis, you can detect change over time. These tools and many others, which are part of ArcGIS, enable you to address critically important questions and decisions that are beyond the scope of simple visual analysis. Here are some of the foundational spatial analyses and examples of how they are applied in the real world.
This 3D hot spot analysis of 20 years of storm cell data across the United States uses the vertical z-axis to represent time, so when tilted just right in a 3D viewer, it shows two decades of change in storm activity.
Using data complied by the National Drought Mitigation Center from numerous agencies, this map focuses on the widely varying degrees of drought in Texas from 2011 to 2016.
This space–time trend analysis of Florida auto crash data factors in time of day and underlying road conditions to identify new hot spots.
Statistical analyses can identify patterns in events that might otherwise seem random and unconnected, such as crimes in San Francisco.
GIS analysis is used to explore how effectively the citizens of Atlanta are being served by public transit in this large urban community. Anyone who commutes understands that the time of day matters as well. You can use this story map to explore levels of transit service for different time windows.
Spatial analysis is used by people around the world to derive new information and make informed decisions. The organizations that use spatial analysis in their work are wide-ranging—local and state governments, national agencies, businesses of all kinds, utility companies, colleges and universities, NGOs—the list goes on. Here are just a few examples.
A spatial interaction model identifies the hot spots for crimes in Chicago.
This temporal analysis of the evolution of the 2010–2015 Texas drought applies both raster and vector analysis methods. The project succeeds because of the attention to the final information product: a story map.
Esri’s green infrastructure initiative set out to develop data for the continental United States of critical 100-acre patches depicting “intact habitat cores.” It is making this data freely available as source data for land-use planning and to create information products that help everyone understand the importance of preserving the nation’s remaining natural heritage.
GeoPlanner for ArcGIS is a planning app used to evaluate opposing or competing land uses at local and regional scales. This screen capture shows a scenario where proposed protected areas (light green) are within areas of high projected population growth.
GeoDescriber analyzes landscape layers in the Living Atlas of the World to generate a short narrative of descriptive text to characterize the most important elements about a landscape.
In many cases, just by making a map you are doing analysis. That’s because you’re making the map for a reason. You have a question you want the map to help answer: Where has disease ravaged trees? Which communities are in the path of a wildfire? Where are areas of high crime? It’s also because when you make a map, as with any analysis, you’re making decisions about which information to include and how to present that information. Effective visualization is valuable for communicating results and messages clearly in an engaging way.
A surface displayed in 3D space has value as a visual display backdrop for draping data and analyzing it. This perspective scene shows a restored watershed and river draped on a digital elevation model of the terrain.
Solar radiation tools in ArcGIS enable you to map and analyze the potential for solar panels to generate electricity. (Naperville, Illinois, shown here.)
Multispectral imagery can provide a new perspective on crop health and vigor. The Normalized Difference Vegetation Index (NDVI) reveals healthy potato and canola crops in Saskatchewan, Canada.
This historical story map uses GIS visibility analysis to tell the fateful tale of the Battle of Gettysburg in the American Civil War. At the moment General Robert E. Lee (at the red eye) committed to engage with Union troops, he could see only the troops in the light areas; everything shaded (the much greater part of the Union’s strength) was invisible to him at that moment. Historians using personal accounts, maps of the battle, and a basic elevation layer were able to unlock the mystery of why Lee may have committed to battle facing such poor odds.
Most data and measurements can be associated with locations and, therefore, can be placed on the map. Using spatial data, you know both what is present and where it is. The real world can be represented as discrete data, stored by its exact geographic location (called “feature data”), or continuous data represented by regular grids (called “raster data”). Of course, the nature of what you’re analyzing influences how it is best represented. The natural environment (elevation, temperature, precipitation) is often represented using raster grids, whereas the built environment (roads, buildings) and administrative data (countries, census areas) tends to be represented as vector data. Further information that describes what is at each location can be attached; this information is often referred to as “attributes.”
In GIS each dataset is managed as a layer and can be graphically combined using analytical operators (called overlay analysis). By combining layers using operators and displays, GIS enables you to work with these layers to explore critically important questions and find answers to those questions.
In addition to locational and attribute information, spatial data inherently contains geometric and topological properties. Geometric properties include position and measurements, such as length, direction, area, and volume. Topological properties represent spatial relationships such as connectivity, inclusion, and adjacency. Using these spatial properties, you can ask even more types of questions of your data to gain deeper insights.
GIS analysis can be used to answer questions like: Where's the most suitable place for a housing development? A handful of seemingly unrelated factors—land cover, relative slope, distance to existing roads and streams, and soil composition—can each be modeled as layers, and then analyzed together using weighted overlay, a technique often credited to landscape architect Ian McHarg.
The true power of GIS lies in the ability to perform analysis. Spatial analysis is a process in which you model problems geographically, derive results by computer processing, and then explore and examine those results. This type of analysis has proven to be highly effective for evaluating the geographic suitability of certain locations for specific purposes, estimating and predicting outcomes, interpreting and understanding change, detecting important patterns hidden in your information, and much more.
The big idea here is that you can begin applying spatial analysis right away even if you are new to GIS. The ultimate goal is to learn how to solve problems spatially. Several fundamental spatial analysis workflows form the heart of spatial analysis: spatial data exploration, modeling with GIS tools, and spatial problem solving.
Spatial data exploration involves interacting with a collection of data and maps related to answering a specific question, which enables you to then visualize and explore geographic information and analytical results that pertain to the question. This allows you to extract knowledge and insights from the data. Spatial data exploration involves working with interactive maps and related tables, charts, graphs, and multimedia. This integrates the geographic perspective with statistical information in the attributes. It’s an iterative process of interactive exploration and visualization of maps and data.
Smart mapping is one of the key ways that data exploration is carried out in ArcGIS. It’s interesting because it enables you to interact with the data in the context of the map symbology. Smart maps are built around data-driven workflows that generate intelligent data displays and effective default ways to view and interact with your information to see things such as your data’s distribution.
Visualization with charts, graphs, and tables is a way to extend the exploration of your data, offering a fresh way to interpret analysis results and communicate findings. Typically you might begin by browsing through the raw data, looking at records in the table. Then maybe you’d plot (geocode) the points onto the map with different symbology and begin creating different types of charts (bar, line, scatter plot, and so on) to summarize the data in different ways (by district, by type, or by date).
Next, you can begin to examine the temporal trends in the data by plotting time on line charts. Information design is used to arrange different data visualizations to interpret analysis results. Combine a series of your strongest, clearest elements such as maps, charts, and text in a layout that you present and share.
Insights for ArcGIS® is a browser-based analytic workbench that enables you to interactively explore and analyze your data coming from many sources. Insights enables you to quickly derive deeper understanding and powerful results through its rich, interactive user experience.
Insights for ArcGIS has the ability to integrate a variety of data sources for your analysis. It integrates and enables analysis of GIS data, enterprise data warehouses, big data, real-time data streams, and spreadsheets, and more. Insights for ArcGIS also leverages Esri’s vast ecosystem of data, including the curated and authoritative Living Atlas of the World, by including a wider variety of information in analysis.
Create an Insights workbook, visualize your data, and explore.
Add data from different sources, and extend your data with location fields, attribute joins, and calculated fields.
Create and interact with great-looking visualizations, thanks to smart defaults.
Update maps, draw buffers, use spatial filtering, and aggregate data across any geography and more.
Spatial analysis is the process of geographically modeling a problem or issue, deriving results by computer processing, and then examining and interpreting those model results. The spatial model that you create is based on a set of tools that apply operations on your data to create new results.
Each geoprocessing tool performs a small yet essential operation on geographic data, such as adding a field to a table, creating buffer zones around features, computing the least-cost paths between multiple locations, or computing a weighted overlay to combine multiple layers into a single result.
ArcGIS contains hundreds of analytical tools to perform just about any kind of analytical operation using any kind of geospatial information. For example, see the comprehensively rich set of operators found in the geoprocessing toolboxes that come with ArcGIS Pro. ArcGIS Pro also includes ModelBuilder, a visual programming application you can use to create, edit, and manage geoprocessing models.
Spatial analysis supports the automation of tasks by providing a rich set of tools that can be combined into a series of tools in a sequence of operations using models and scripts. Through spatial modeling, you can chain together a sequence of tools, feeding the output of one tool into another, enabling you to compose your own model.
The metropolitan area of Greater Los Angeles region extends to 4,850 square miles (12,561 square kilometers) and represents the second-largest metropolitan area in the United States. The region has retained some of its original natural areas, and in the mountains surrounding the metropolis, the mountain lions (cougars) are the largest carnivores that live, hunt, and breed in this Southern California area. Our challenge is to ensure they survive. By connecting their remaining natural habitats to one another, in theory, this will allow the animals to seamlessly move between them.
This study analyzed ways to connect cougars located in several core areas with cougars in other geographically separated core areas. You will identify potential wildlife corridors that researchers and authorities can use to develop physical connections between cougar habitats located in the Santa Susana Mountains with habitats in the Santa Monica Mountains, the San Gabriel Mountains, and in the Los Padres National Forest. The complete workflow is described in the Learn ArcGIS lesson below.
Many types of problems and scenarios can be addressed by applying the spatial problem solving approach using ArcGIS. You can follow the five steps in this approach to create useful analytical models and use them in concert with spatial data exploration to address a whole array of problems and questions:
Set the goals for your analysis. Begin with a well-framed question that you’d like to address based on your understanding of the problem. Getting the question right is key to deriving meaningful results.
Use geoprocessing to model and compute results that enable you to address the questions you pose. Choose the set of analysis tools that transform your data into new results. More often than not, you’ll build a model that assembles multiple tools to model your scenarios, and then apply your model to compute and derive results that help you address your question.
Use spatial data exploration workflows to examine, explore, and interpret your results using interactive maps, reports, charts, graphs, and information pop-ups. Seek explanations for the patterns you see and that help explain what the results mean. Effective exploration enables you to add your own perspectives and interpretations to your results.
After exploring and interpreting your analytical results, make a decision and write up your conclusions and analytical results. Assess how adequately your results provide a useful answer to your original analysis question. Often new questions will arise that need to be addressed. These will frequently lead to further analysis.
Identify the audience that will benefit from your findings and whom you want to influence. Then use maps, pop-ups, graphs, and charts that communicate your results efficiently and effectively. Share those results with others through web maps and apps that are geo-enriched to provide deeper explanations and support further inquiry. You can communicate your results using story maps as an effective way to share your findings with others.
Geography plays a crucial role in health analysis. Fundamentally, it represents the context in which health risks occur; environmental hazards, risks, susceptibility, and health outcomes all vary spatially. Access to health care is characterized by both human and physical geographies. Furthermore, management and policy differ by location, and resources are allocated geographically. Health is important to everyone, but health analysis is challenging and demands a number of skills including epidemiology, statistics, and geographic information science. Spatial epidemiology is truly multidisciplinary, and although complex techniques are required for analysis, results must be accessible to everyone.
All these challenges were faced during the development of The Environment and Health Atlas for England and Wales. The atlas was developed with the ambitious goal of providing a resource— for the public, for researchers, and anyone working in public health—with a collection of multiscale, interactive web maps that illustrate geographic distributions of disease risk and environmental agents at neighborhood scale.
Environmental monitoring and heath surveillance has advanced in recent decades, but emergencies continue to cause economic and social damage and, of course, loss of life. As the world becomes ever more interconnected both socially and economically, environmental and health impacts are felt at a wider scale than ever before. For example, following volcanic eruptions and nuclear accidents, or as a result of disease outbreaks such as avian influenza and Ebola, too often the impacts of environmental hazards fall disproportionately on the most vulnerable populations.
GIS offers the technology to explore, manipulate, analyze, and model data from multiple sources. With spatial analysis hazard mapping and predictions developed for risk assessment, you can use models to evaluate response strategies, and maps to illustrate preventative strategies and for risk communication and negotiation.
As technology has evolved, so have the science, the data, and the tools to test hypotheses and gain deeper insights into public health. We find ourselves at a time when, for many analyses, we are no longer awaiting technological or data advances. Instead, we should challenge ourselves to improve our understanding and public health through analysis.
ArcGIS spatial analysis tools are implemented in several places within the online and desktop environments.
The analytic capabilities of ArcGIS Online are accessed through the Analysis button on the map viewer:
At the time of this writing, Insights for ArcGIS requires ArcGIS® Enterprise. Look for its appearance in ArcGIS Online in the future.
ArcGIS Pro is Esri’s premier spatial analysis application. Its geoprocessing toolbox contains hundreds of spatial analytic tools. Your Learn ArcGIS Student membership allows complete use of the system for noncommercial purposes where you can learn spatial analysis by doing. Download the software; your license will be activated by the Learn ArcGIS organization.
This Massive Open Online Course (MOOC) runs periodically throughout the year. In this course you’ll get free access to the full analytical capabilities of ArcGIS Online, Esri’s cloud-based GIS platform.
An impressive set of spatial analysis case studies are on the ArcGIS Analytics website.
The mega city of Los Angeles is one of the few world cities that have big cats living within the natural areas of the city. But the city landscape is becoming increasingly fragmented by urban development, roads, and freeways, thus leaving less space for mountain lions (cougars) to survive. As they attempt to cross roads and freeways in search of prey and mates, cougars often get struck and killed. For the Los Angeles cougar population to survive as well as maintain genetic diversity, and overall population health, a long-term solution must be found that will allow them to move safely between the isolated pockets of land they currently occupy.
In this project, your goal is to identify current cougar distributions and build a spatial model that identifies corridors we can establish to connect the various core mountain lion habitat areas within the city to each other.
The workflow emphasizes setting analysis goals which lead to questions that will give meaningful results. Following the workflow, you will examine and interpret analysis results, seek explanations for observed patterns, and explore their meaning from a spatial or temporal perspective. A strong emphasis of this workflow is on locating and using community and Living Atlas of the World data, and then contributing and sharing results and findings back to the community. The workflow also emphasizes the use of infographics and GeoEnrichment tools to provide deeper explanations and support for further inquiry.