Spatial analysis in ArcGIS AllSource

Spatial analysis allows you to solve complex location-oriented problems, explore and understand your data from a geographic perspective, determine relationships, detect and quantify patterns, assess trends, and make predictions and decisions. Spatial analysis goes beyond mapping and allows you to study the characteristics of places and the relationships among them. Spatial analysis lends new perspectives to your decision-making.

Using spatial analysis, you can combine information from many sources and derive new information by applying a set of spatial operators. This collection of spatial analysis tools allows you to answer complex spatial questions. Statistical analysis can determine whether the patterns that you see are significant. You can analyze various layers to calculate the suitability of a place for a particular activity and, using image analysis, you can detect change over time. These tools allow you to address important questions and decisions that are beyond the scope of simple visual analysis.

Hot spot analysis map of San Francisco crimes

You can use the analysis and geoprocessing capabilities in ArcGIS AllSource to answer many spatial questions and perform spatial analysis. Spatial analysis in ArcGIS AllSource is extended from 2D to 3D and through time.

A typical spatial analysis workflow involves the following:

  • Frame the question you want to answer.
  • Find and prepare the data using data engineering to ready it for analysis.
  • Explore the data on a map and with charts to better understand it.
  • Perform the spatial analysis, using the appropriate tool or set of tools to answer the question.
  • Repeat or automate the analysis using modeling and scripting.
  • Share the results to communicate findings or allow others to repeat the process.

Data engineering

Using data engineering, you can explore, visualize, clean, and prepare data. The data engineering process is a common first step for many spatial analysis and mapping workflows. The Data Engineering view and ribbon can help you better understand the data and prepare it for GIS workflows.

Learn more about data engineering

Visualization with charts

Visualizing data using charts can uncover data patterns, trends, relationships, and structures that may otherwise be difficult to see as raw numbers in a table. Interpret the results of an analysis and communicate the findings with charts.

Scatter plot shows the relationship between two variables.

Learn more about charts

Spatial analysis

Using spatial analysis capabilities in ArcGIS AllSource, you can perform the following types of operations on geographic data:

  • Extract and overlay data.
  • Add and calculate attribute fields.
  • Summarize and aggregate data.
  • Calculate statistics.
  • Model relationships and discover patterns.

Learn more about geoprocessing

Learn more about raster functions

Machine learning and artificial intelligence

Machine learning refers to a set of data-driven algorithms and techniques that automate the prediction, classification, and clustering of data. Deep learning is an important subset of machine learning techniques that uses artificial neural networks to learn from data. Machine learning and deep learning can play an important role in spatial problem solving in a wide range of application areas, from image classification to spatial pattern detection to multivariate prediction.

In addition to traditional machine learning techniques, ArcGIS AllSource includes a subset of machine learning techniques that are inherently spatial. These spatial methods that incorporate geography directly into their computation can lead to better understanding. The spatial component often takes the form of a measure of shape, density, contiguity, spatial distribution, or proximity. Both traditional and inherently spatial machine learning can play an important role in solving spatial problems, and ArcGIS AllSource supports their use in a number of ways.

Machine learning can be computationally intensive and often involves large and complex data. Advancements in data storage and parallel and distributed computing make solving problems related to both machine learning and GIS possible.

The following capabilities and tools use machine learning and deep learning:

Big data analytics

ArcGIS AllSource includes tools that can transform large amounts of spatial data into manageable information. Using parallel processing on a single machine or distributed processing with multinode servers, you can analyze and gain insights from large volumes of data that were previously too large or complex.

You can use the following capabilities and toolboxes to analyze big data:

  • The GeoAnalytics Desktop toolbox provides a parallel processing framework for analysis on a desktop machine using Spark. Through aggregation, regression, detection, and clustering, you can visualize, understand, and interact with feature and tabular big data. These tools work with big datasets and allow you to gain insight into the data through patterns, trends, and anomalies.

    Learn more about the GeoAnalytics Desktop toolbox

    Tools in the Movement Analysis toolset in the AllSource toolbox also use Apache Spark.

  • You can perform visual analytics and data exploration of big data in a cloud data warehouse or other enterprise source using charts.

    Learn more about creating charts with a big data source

  • Dynamically bin big data and display it on a map to aggregate and summarize the data on the fly without preprocessing steps.

    Learn more about feature binning

Modeling and scripting

Save time on repetitive tasks, minimize errors, and iterate analysis efficiently by creating models or scripts. Then turn a model or script into a custom tool. Use Python to script workflows or build models of workflows using ModelBuilder.

ModelBuilder model that runs multiple geoprocessing tools
A ModelBuilder model runs multiple geoprocessing tools using the output of one tool as the input to another tool.

You can also add third-party libraries using the Package Manager to extend ArcGIS AllSource.

Sharing and collaboration

You can share the analysis methodology as well as the data you have analyzed in ArcGIS AllSource with your colleagues, organization, or community as geoprocessing packages or web tools. With these shared analysis tools, anyone can take advantage of your expertise in spatial analysis while performing the analysis themselves.

Learn more about sharing analysis