{"id":182811,"date":"2012-11-13T18:41:36","date_gmt":"2012-11-13T18:41:36","guid":{"rendered":"http:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=182811"},"modified":"2018-12-18T11:26:51","modified_gmt":"2018-12-18T19:26:51","slug":"more-adventures-in-overlay-counting-overlapping-polygons-with-spaghetti-and-meatballs","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-desktop\/analytics\/more-adventures-in-overlay-counting-overlapping-polygons-with-spaghetti-and-meatballs","title":{"rendered":"More adventures in overlay: counting overlapping polygons with spaghetti and meatballs"},"author":3991,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341],"tags":[24321,39581,28481],"industry":[],"product":[36991],"class_list":["post-182811","blog","type-blog","status-publish","format-standard","hentry","category-analytics","tag-geoprocessing","tag-overlapping-polygons","tag-overlay","product-arcgis-desktop"],"acf":{"short_description":"I\u2019ll show how spaghetti and meatballs can be used to calculate the number of overlapping polygons.","flexible_content":[{"acf_fc_layout":"content","content":"

In my\u00a0last blog post about overlay<\/a>, I introduced the concept of cartographic spaghetti and how it can be used to split polygons by line features. In this post, I want to continue with the cartographic spaghetti theme by introducing a companion concept: meatballs, which are the centroids of the spaghetti polygons. I\u2019ll show how spaghetti and meatballs can be used to calculate the number of overlapping polygons.<\/span><\/p>\n

Downloads<\/h3>\n

If you\u2019re looking for a tool you can use immediately to count overlapping polygons, you can download the\u00a0Count Overlapping Polygons<\/a>\u00a0tool.\u00a0 You can also download the sample data and model used in this post\u00a0here<\/a>.<\/p>\n

The scenario<\/h2>\n

The\u00a0Deepwater Horizon oil spill<\/a>\u00a0in the Gulf of Mexico lasted from April 20 to July 15, 2010. During this time, NOAA mapped the extent of the plume daily and distributed it in various GIS formats. The map below uses this data to show the plume concentration over 67 days. The count value in the legend is the number of days the plume covered an area. Since there is one polygon per day, count is simply the number of overlapping plume polygons.<\/p>\n

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Figure 1: Number of days that the plume covered an area<\/p>\n<\/div>\n

Counting overlaps<\/h2>\n

The easiest and fastest way I\u2019ve found to count the number of overlapping polygons is the \u2018spaghetti and meatballs\u2019 method described below. To use this method, you need a single feature class containing all the overlapping polygons. For example, the oil spill data above started out as 67 feature classes, each containing one polygon, and I used the\u00a0Merge<\/a>\u00a0tool to merge all 67 features into one feature class (the\u00a0Append<\/a>\u00a0tool can also be used).<\/p>\n

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Figure 2: Example data<\/p>\n<\/div>\n

To illustrate the details of this technique, I\u2019ll use the dataset shown in Figure 2, one I created by buffering a bunch of point features.<\/p>\n

Create some spaghetti<\/h3>\n

The first step is to create some cartographic spaghetti from the overlapping polygons. As described in the\u00a0previous post<\/a>, the\u00a0Feature To Polygon<\/a>\u00a0 tool does this for you. Feature To Polygon takes one or more line or polygon feature classes and creates a polygon feature class as output. And, as mentioned in the post, you want to avoid having any attributes on the output of Feature To Polygon and there\u2019s a trick you can use of supplying an empty point feature class to the\u00a0Label Features<\/strong>\u00a0parameter of Feature To Polygon. (Alternatively, you could use the\u00a0Delete Field<\/a>\u00a0tool.) Figure 3 shows the model to create an empty point feature class in memory and connecting it to Feature To Polygon.<\/p>\n

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Figure 3: Creating spaghetti<\/p>\n<\/div>\n

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Figure 4: Overlapping polygons and spaghetti polygons<\/p>\n<\/div>\n

The output of Feature To Polygon is shown on the right side of Figure 4. The left side is the original overlapping polygons and I\u2019ve highlighted two void areas that are made into polygons by Feature To Polygon. I call these \u2018artifact\u2019 polygons. I note these because such artifact polygons require a bit of special handling later.<\/p>\n

Create some meatballs<\/h3>\n

The next step is to create a centroid point for each polygon in the output of Feature To Polygon. These centroids are the \u2018meatballs\u2019 of the spaghetti and meatballs technique. We\u2019re going to use these points in an overlay of the original overlapping polygons to produce a count.
\nTo create the meatballs, use the\u00a0Feature To Point<\/a>\u00a0tool, illustrated in Figure 5.\u00a0 Be sure to check the\u00a0Inside<\/strong>\u00a0option\u2014this guarantees that the point created will be inside the polygon.<\/p>\n

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Figure 5: Using Feature To Point to create centroids<\/p>\n<\/div>\n

Figure 6 shows the meatballs inside their spaghetti polygons along with the attribute table. On the attribute table,\u00a0ORIG_FID<\/em>\u00a0is the\u00a0OBJECTID<\/em>(feature ID) of the spaghetti polygon from which the point was constructed.<\/p>\n

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Figure 6: Meatballs and their attributes<\/p>\n<\/div>\n

Point-in-polygon overlay<\/h2>\n

Now comes the interesting part\u2014we overlay these meatball centroids with the original overlapping polygons to get a count of how many polygons overlap each meatball point. There are several ways we can do this. For example, the\u00a0Intersect<\/a>\u00a0 tool could be used to create a new point feature class containing one point for each polygon that overlaps it. The next step would be to count up the number of occurrences of\u00a0ORIG_FID<\/em>\u00a0using the\u00a0Summary Statistics<\/a>\u00a0 tool. But this is a lot of steps to go through just to get the count.<\/p>\n

I\u2019ve found the fastest way is to use the\u00a0Spatial Join<\/a>\u00a0tool. Figure 7 shows the model and dialog.<\/p>\n

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Figure 7: Overlaying the centroids (meatballs) with the overlapping polygons<\/p>\n<\/div>\n

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Figure 8: Result of Spatial Join<\/p>\n<\/div>\n

Figure 8 shows the attribute table of the result of Spatial Join \u2013 a copy of the meatball point feature class with a\u00a0Join_Count<\/em>field, which is the number of polygons that overlap the point.<\/p>\n

A couple of things to note about the Spatial Join parameters:<\/p>\n