For the ArcGIS hot spot analysis I used the tutorial available at https://www.arcgis.com/home/item.html?id=6626d5cc81a745f1b737028f7a519521. Note that the tutorial must be downloaded using the “open” button below the image on the web page. This will download a folder with pdf file of instructions and a dataset for their analysis.
I followed the tutorial, but substituted a dataset of fire ignitions logged by the Oregon Department of Forestry between the years 1960 and 2009 (Fig. 1). This dataset does not include some fires that occurred in the state during that time, for instance some on federal land and many on non-forested land.
Figure 1: Oregon Department of Forestry logged fire occurrences from 1963 to 2009.
The tutorial was straightforward and easy to follow. The only “problem” I had was with the spatial autocorrelation step. My data did not produce any peaks in the z-score graph, so I arbitrarily chose a distance towards the lower end of the graph (tutorial steps 10 f and g).
My results from the hot spot analysis step (Fig. 2) showed areas with hot spots. These tended to be near roads and cities in most cases, with a few instances at high elevation in northeastern Oregon.
Figure 2: Results from hot spot analysis.
Results from interpolating the hot spot results using the IDW tool (tutorial step 12) (Fig. 3) produced anomalous edge effects (Fig. 3). Note the areas of high values spreading from hot spot areas into areas with no data along the coast. A close up image of the Medford area (Fig. 4) shows edge effects spreading into areas with no data.
Figure 3: Hot spot analysis interpolated surface produced by the application of the Arc IDW tool. Note the edge effects, especially along the coast and northern state boarder.
Figure 4: Interpolated hot spot surface produced by the Arc IDW tool along with the original ODF ignitions dataset points. Note the edge effects in the southwest corner of the image spreading from the area of high fire occurrence into areas with no data.
As part of a study I did for a master’s thesis, I used the ODF data along with another dataset to do a logistic regression analysis of ignition occurrences in the Willamette watershed excluding the valley floor. Within this area, the hot spot analysis placed hot spots along some roads and population centers in several instances (Fig. 5A). These results are consistent with the results from my study, which showed that human-caused fires were more likely to occur along roads and near areas of high population, and that lightning-caused fires were likely to occur at high elevations (Fig. 5B). The lack of a match between the two methods at high elevation is due to data points that were used in the logistic regression but not in the hot spot analysis. However, one disadvantage of the hot spot analysis is that it is univariate, or strictly occurrence based. The logistic method allows for correlation and the application of that correlation to the prediction of future occurrence
Figure 5: Results from A) hot spot analysis and B) logistic regression analysis for fire occurrence in the hills and mountains of the Willamette watershed. Redder areas are “hotter” in the hot spot analysis and have a higher ignition probability in the logistic regression analysis.
In conclusion, hot spot analysis is a good technique for finding areas of higher occurrence, but has no predictive power beyond analyzing past occurrences. The ArcGIS IDW can produce an interpolated surface, but it has an edge effect issue that could lead to questionable results. Regression methods, for example logistic regression, may produce results more suited to analyzing correlation of independent variables with event occurrence.
