What do we mean by Machine Learning? Machine Learning (ML) refers to a set of data-driven algorithms and techniques that automate the prediction, classification, and clustering of data. Machine learning can play a critical role in spatial problem solving in … Continue reading
Under the specific set of conditions outlined below, analyzing a Space Time Cube using either Emerging Hot Spot Analysis or Local Outlier Analysis is not working correctly. The large majority of workflows are not affected. The issue occurs when you … Continue reading
The Optimized Hot Spot Analysis tool in the 1.3 release of ArcGIS Pro is not working correctly. This issue impacts workflows that start with incident points and aggregate into fishnet or hexagon grids. Instead of counting the total number of … Continue reading
The ArcGIS 10.3 release includes the new Space Time Pattern Mining toolbox for analyzing data distributions and patterns in the context of both space and time in ArcMap and Pro. Unfortunately this initial release contains a substantial logic flaw in the Create Space Time Cube tool which makes the results from the Emerging Hot Spot Analysis tool unreliable. We have corrected this problem in ArcMap 10.3.1 and Pro 1.1 and are providing the patches below for your immediate use. Regrettably you must rerun all of your previous analyses using the provided fix. Internally we have enhanced our validation strategies to ensure this is not a recurring error.
The Business Analyst team invited me to blog about some work I’ve been doing with the BAO API, and I’m really excited about the opportunity!
As a product engineer on the Geoprocessing and Analysis team, my team and I work hard to push the limits of what spatial analysis is and how it can be used to solve real-world problems, and we often use Business Analyst to take our analysis to the next level.
One very powerful use of spatial analysis and geoprocessing is Continue reading
Are you an epidemiologist, crime analyst, demographer, emergency response
planner, transportation analyst, archeologist, wildlife biologist,
retail analyst, or other GIS practitioner interested in moving beyond
putting points on a map to analyzing spatial patterns and trends?
We’re really excited because the recording of the free live training seminar on Spatial Pattern Analysis is now available from the training website: Spatial Pattern Analysis. We had over 2,000 attendees when it aired live, and we’re hoping that even more people are going to be able to take advantage of the recorded version of the seminar. The session talks about:
- Using descriptive spatial statistics to summarize the most
important characteristics of a spatial distribution.
- How global
pattern analysis statistics assess and quantify broad geographic
patterns and trends.
- How to use local pattern analysis
statistics to find hot spots, cold spots, and spatial outliers.
Check it out!
And for more Spatial Statistics resources, check out http://esriurl.com/spatialstats
When it comes to the Spatial Statistics tools in Version 10.0, there are a couple of things that you need to keep in mind when using background geoprocessing. Many of the Spatial Statistics tools have textual output that can be viewed from the progress dialog box and the Results window when the tools are run in the foreground. When the tools are run in the background, you must rely on the Results Window to give you all of that important information (and save it for future use).
Let’s use the Spatial Autocorrelation (Moran’s I) tool as an example. The Spatial Autocorrelation tool returns five values: the Moran’s I Index, Expected Index, Variance, z-score, and p-value. These values are accessible from the Results window and are also passed as derived output values for potential use in models or scripts. Right-clicking on the Messages entry in the Results window and selecting View will display the results in a Message dialog box (as illustrated below). If you execute this tool in the foreground, output values will also be displayed in the progress dialog box.
Optionally, the Spatial Autocorrelation tool will create an HTML file with a graphical summary of the results. In previous versions, you could choose to have a graphic pop up tell you whether your results are clustered, random, or dispersed. A similar graphic summary of your results is still available, but now it is actually saved as an HTML output. This will help us as we share and review our findings in the future. Double-clicking on the HTML file in the Results window will open the HTML file in your default Internet browser.
Also keep in mind that if you are running model tools that you created in ModelBuilder, you will have to make sure that you set the output files (like HTML pages) as Model Parameters. That way they will show up in the Results window.
So, make sure that you remember how important the Results window is for many of the Spatial Statistics tools! And for more resources on using the Spatial Statistics tools, check out our resources page at http://esriurl.com/spatialstats.
There is a new sample script toolbox called Supplementary Spatial Statistics ready for download from the Model and Script Tool Gallery. The toolbox includes two sample script tools that we think you will find very useful. The first tool is Exploratory Regression, which is designed to help you find a properly specified OLS model from a set of candidate explanatory variables. The second tool is Incremental Spatial Autocorrelation, which is designed to help you figure out the right distance band to use for your spatial statistics analyzes. Each tool includes several documents that will help you get started and learn more.
We’ve also recently added several new tutorials that can also be downloaded from the Model and Script Tool Gallery. To find those resources, and many more, check out http://bit.ly/spatialstats which is always up-to-date with the latest resources for Spatial Statistics.
Great blog post on the ArcGIS Desktop blog about Network Analyst Barriers in ArcGIS 10. Find out how things have improved, and download a related model from the Geoprocessing Model and Script Tool Gallery that uses weather data to accurately measure travel times through severe weather conditions.
A new toolbar, Image Classification, has been introduced at ArcGIS 10 to make image classification tasks both faster and easier (to use this toolbar, you need the Spatial Analyst extension). Expressed simply, the image classification process converts multiband raster imagery into a single-band raster with a number of classes, which you can then use to make thematic maps or for further analysis. Example applications for image classification include landcover mapping and landuse change detection. Continue reading