This function uses inverse distance weighting to interpolate values for each cell in the output raster image. Unlike the kernel density map, in this case every cell of the resulting raster will have a value based upon this interpolation. The influence of a given point to neighboring cells decreases as the distance between the point and cell increases. When you select the “Generate Interpolative Map” menu option, you will be presented with a dialog box similar to the one shown below:

Heat Maps provide a way to geographically visualize the concentration or densities of events across an area. Heat maps can be a useful tool to visualize or analyze concentration of events such as crime activity, population, or measurements. Simple GIS generates heat maps by taking an input point shape file and interpolating the data to a continuous surface. In the case of Simple GIS, this surface is represented as a raster Tiff file. To generate the surface, Simple GIS considers three different parameters. The first parameter is the individual cell sizes in the raster. The raster surface will be made up of square cells of a given width and height. The smaller these cell sizes the smoother the resulting surface but the slower the processing time. Therefore, there is a trade off as to the resolution of the surface versus processing time. The second parameter is the search radius. This determines the area of influence assigned to each point. The greater the search radius, the more generalized the density patterns will be on the resulting surface. The smaller the search radius the density patterns may become too restricted to interpret significant patterns. The third parameter involves the type of calculation to use to interpret cell values. There are two distinct ways Simple GIS interpolates this data. The first is to use a kernel density function which uses a quartic probability density function to calculate the density distribution across the surface. The kernel density function is best for data that may not exist across the entire surface. For example, population data. There would likely be areas across your surface where it may be reasonable and likely to have no population. Areas covered by seas, deserts, or other inhabitable areas for instance. The second calculation Simple GIS can use is the inverse distance weighted function. This method is best for data or measurements you would expect to find across the entire surface. Temperature data for instance would be a good example as every area covered by the surface would have a temperature value. The Heat Map generating functions are found under the “Geo Processing” of the Map Data View document. Each calculation is described in further detail below.

Generate Interpolative Map

where dij is the distance from center of cell to point and h is the search distance.

When you select the “Generate Kernel Heat Map” menu option, you will be presented a dialog box similar to the one shown below.

Just like for the kernel density heat map, you would select your input point layer and adjust the raster width and height, or the cell width and height or accept the default. Also, the weight field, gradient color start, gradient color end, and new raster file name work the same as described for the kernel density function above. However, in this case you may notice a new parameter named “Distance coefficient p”. This parameter determines how much influence is attributed to points located at greater distances from a cell. The smaller the value of p, the greater the influence attributed to points located at greater distances from a given cell. The larger the value of p, the greater the influence attributed to points closer to the cell. In this way, by varying the value of p, you can achieve different effects in your heat map. In the example below, we show the resulting heat map created from temperature data from select cities using the attributes shown in the dialog box above.

Generate Kernel Heat Map