Each police call record in meant to contain an address field. I use this address field (or the common name recorded in the call record, if the address field is blank) to calculate counts of visits to an address. Note that
Data preparation involved the creation of time series longitudinal, 1 day level count data of the following attributes: The count of JDPD personnel dispatches that were deemed to by the JDPD to be an official visit to the site. This is the ‘OFFENSE DATE’. It is important to note here that common sense suggests that the choice of the field name is for administrative purposes and not intended to be interpreted as a legal offense. All visits are recorded with OFFENSE DATE attributes. << Move this to introduction >>
All count data was sorted from high to low.
Exploratory data analysis
Exploratory data analysis involved (a) generating descriptive statistics of the dataset. (b) Time series visualisation of the observed variable across the full dataset. (c) A simple X-Y plot of locations (the independent variable, ordered from high to low) against the count of visits attributable to the location. I also performed simple ratio analysis of the service volume information, by the locations that were attributed to 10 or more calls per day.
Model discovery and fitting
I attempted to find the appropriate stochastic model iteratively fitting the service volume data to the distribution and observing result of the ChiSquare test.
I then attempted to fit the data against a polynomial and reported the result.
Longitudinal analysis
I performed longitudinal data analysis, yearwise, with a focus on locations. I selected the top “high needs” locations that were common in each of the ten years.
Visualisation
I then geocoded the locations, by (unique) street addresses and plotted the result geospatially.
Section
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