Heatmaps are one of the best visualization tools for dense point data. Heatmaps are used to easily identify find clusters where there is a high concentration of activity. They are also useful for doing cluster analysis or hotspot analysis. We will work with a dataset of crime locations in Surrey, UK for the year 2011 and find crime hotspots in the county. Heatmaps are one of the best visualization tools for dense point data. Heatmaps are used to easily identify find clusters where there is a high concentration of activity. They are also useful for doing file on your computer and open it. (Your filename maybe different if you downloaded a fresh copy of the dataset). Select . The CSV importer assumes the CRS EPSG:4326 if your coordinates are in Latitude/Longitude. If your X and Y coordinates were in a projected CRS, you will get a dialog prompting you to choose the CRS. As our data is in EPSG:4326, you can ignore the warning. Zoom-in a bit closer to get a better look at the data. You will notice that the data is quite dense and it is hard to get an idea of where there is a high concentration of points. This is where a heatmap will come in handy. If you need to create a heatmap for purely visual purpose or for printing – QGIS has a built-in symbology renderer called where there is a high concentration of crime. There are quite a few options available in the heatmap renderer to create the most appropriate visualization for your dataset. If you just wanted to create a heatmap for print or visual inspection – you are done! But we will explore another more powerful heatmap creation option where you can use the results in your analysis also. Let’s make our heatmap look more like the traditional heatmap similar to the earlier visualization. Right-click on the heatmap layer and click . This will scan the heatmap and find the minimum and maximum pixel values. These values will be used to generate an appropriate color ramp. In the section tool and click on any pixel of the heatmap. You will see the pixel value in the resulting pop-up. This pixel-value is a measure of how many points from the source layer are contained within the specified radius ( in our case – 1000m) around the pixel. Now you have your heatmap layer that can be saved for future use. Many times, you want to identify the hotspots where there is high-concentration of points. We will now try to identify such hotspots using this heatmap. Go to You will have to decide on a threshold value first. All pixel values above that threshold will be considered to be in a cluster. Let’s use a value of will be added to QGIS. This layer has pixels with values of either 0 or 1. All pixels in the input layer where the pixel value was larger than added to QGIS. This is the vector representation of the clusters that were created in the previous step. The layers contain clusters with both 0 and 1 values. Let’s filter out the 0 values, so we get the clusters of hotspots. Right-click on the layer and select In the main attribute table window, you will see some features highlighted. These are the features that matched our query. Click the Source.