Hello! Welcome to my Github. You will find a Python3 script in this repository which shows the working of my Hotspot Analysis code. If you are new to the concept, do give this a read and if you have any questions/comments/suggestions please feel free to email me on anirudh.ash2594@gmail.com Thank you and hope you enjoy learning! ---------------------------------------------------------------------------------------------------------- Q. What is a hotspot analysis? A. Hotspot analysis is a spatial analysis and mapping technique interested in the identification of clustering of spatial phenomena. These spatial phenomena are depicted as points in a map and refer to locations of events or objects. Q. How is a heatmap different from a hotspot? A. Excellent question! While a heatmap does the degree or magnitude of occurence of an event, it does not take the one most important factor into account, which we have learnt in statistics, 'correlation'. More than often, we know that 'correlation' is that one factor that causes endogeneity and thereby giving rise to a bias in our results. There is no point in working with biased results as your study has gone wrong one step above, when you did not take into account the correlation/autocorrelation factors. A hotspot analysis ensures that all the correlations are accounted for and hence gives us a more accurrate picture of the subject. Q. Is that all? A. No. Another very important concept, 'statistical significance', is not analyzed by a heatmap. After all, it just plots the points we want it to. Statistical significance, as we know, is the like-hood that a relationship between two or more variables or features is well defined and not a random occurrence. Hotspot Analysis uses statistical analysis to define areas of high occurrence of an event or an outcome from areas that have a lower occurrence of the same event. Q. So how do we test for correlation? A. We have several tests to study the correlation between explanatory variables and the error terms or the other lesser-accounted factors. We first have the Pearson correlation test which measures the linear dependence among two variables (x and y). It is also known as the parametric correlation test due it's dependency on how the data has been distributed. Then we have my favorite, the Spearman correlation which is a non-parametric test and this is a rank-based test where we compute the correlation between the rank of x and the rank of y variables.