I have some points that represent animal locations over the course of a few months. I am emulating the work of previous researchers in this field. I'm still new to GIS, so I previously thought that I wanted to run my animal locations through a Kernel Density Estimate. However, re-reading the previous literature and comparing the maps, I think I need a particular kind of KDE. The previous research was published in 2011, so the author used Hawth's tools to conduct a Fixed KDE with volume contours (referenced here and below). Unfortunately, Hawth's doesn't work with Arc10 and it's analogue, GME doesn't work with ArcMap 10.6, which is on all the university computers.

Is there another tool that I can download to make this percent volume contour?

Here is a picture of the KDE from the cited report

Management of pinyon-juniper woodlands at Kirtland Air Force Base: Pinyon Jay summer and winter home ranges and habitat use 2009 final report:

For the kernel analysis we used the Hawth’s Tool, Fixed Kernel Density Estimator. Kernel analysis is a nonparametric statistical method for estimating probability densities from a set of points. When used to analyze home range data, kernel density methods describe the probability of finding an animal in any one place. The Fixed Kernel Density Estimator calculates a fixed kernel density estimate and produces contour lines representing the boundary of the area that contains a specified percent of the volume of a probability density distribution. A 95% volume contour, for example, typically contains 95% of the points used to generate the kernel density estimate. Kernel parameters were set as follows: scaling factor- 1000000, kernel- bivariate normal, single parameter smoothing factor (h)-1000, raster cell size-100, and percent volume contours- 50, 90, and 95.


2 Answers 2


Although this answer is probably a bit late, there is a very simple way to overcome this problem using R which doesn't require proficiency and may be helpful to anyone with a similar problem . The code below will calculate the required contours and then write to a shapefile which can be projected in ArcGIS.


# read in a .csv which contains lat and long for your data points


# create spatial points object


kud <- kernelUD(datapoints, h=0.2) #kernel density utilization distribution (h=smoothing factor)

homerange75 <- getverticeshr(kud, percent=75) #convert into vector object, use 75,50,25 for quartic kernel

# set coordinates and write to shapfile which can be projected in ArcGIS

WGScoor<-  homerange75
proj4string(WGScoor)<- CRS("+proj=longlat +datum=WGS84")
raster::shapefile(WGScoor, "homerange75_new.shp")

ArcGIS 10.6 is the latest and probably last release of ArcGIS Desktop before ESRI shifts all their efforts behind ArcGIS Pro. As such, the tools like Hawth's Tools and GME will likely not be updated for ArcGIS 10.6.

So the solution to my problem, barring becoming proficient at spatial modeling in R this week, was to find someone with ArcMap 10.3 and use GME to create isopleths from my previous Kernel Density Estimates. Sometimes you have to climb down before you can go up!


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