The data shows the crimes occurred in November and December of 2012 in Estonia, it contains column named ‘Precision’ that has the resolution with the data was obtained, such as 500 or 1000, than means the cell size of the grid that was used for collect this data, and the spatial visualization displays the greatest resolution in urban areas and lower resolution in rural areas. (Figure 1).  As a quick remark, the data location is confidential, so that gathered the crimes represented as points overlapped, interesting representation that let us use spatial tools for representation. 

1.2 Task 2 - Analysing Point Density

1.2.1 Weighted Points Representation

As it was mentioned, it is not possible to visualize all the points, because of confidentiality the points are stored by grid resolution overlapped. In total, 15.230 crime incidents are displayed in Estonia, considering all categories.

Then, to appreciate the spatial weight of the crime incidents we used the ‘Collect Events Tool’. (ESRI, 2019)., from ArcGIS software. The result is shown by quantiles classification, and as a comment we can see (Figure 2) the crime incidents are concentrated in urban areas of Estonia.

1.2.2. Point Density with 1000m Grid

Nevertheless, it is possible to display the crime incidents in another way with ‘Point Density Tool’. (ESRI, 2019)., and it represents the points concentration by different grid cell size. It is recommended to find the suitable size of grid cell in order to make the visualization pleasant, and most important to make it reliable.

The point density in this result is shown each 1000m cell size, it means each square kilometre. As we can see (Figure 3), the crime incidents are concentrated in urban areas, and personally makes a reliable visualization because in it let us see precise around the area. 

1.2.3. Point density with 5000m Grid

As we saw in the last result, the small areas also show concentration. But, in this analysis we are performing ‘Point Density Tool’. (ESRI, 2019). with 5000m cell size, to determine which grid is showing the reliable crime incident density.

 The result of this analysis, is a map with no concentration of crime incidents in rural areas (Figure 4). And in this moment, is where the analyst decides to drop some visualizations off and appreciate more others, because some representations are more reliable than others.

To understand better how the data behaves, the differences between Figure 3 and Figure 4 are shown in the next picture (Figure 5).

1.3. Task 3 - Analysing relationship between population and crime incidents

As we can see in the previous results, the greatest concentration of crimes incidents is in cities, and it leads the hypothesis that populated places are related with crime, and to solve this idea we are going to analyze the population and crime in two different levels.

1.3.1. Administrative level analysis

As a basic understanding, was necessary to create a choropleth map with absolute population in administrative areas. The result, as we expected, shows the population concentrated in administrative areas with cities as Tallin, Tartu, Pärnu or Narva. Then, the choropleth is overlapped with number of crimes (Figure 6).

Figure 6: Map of population distribution in administrative areas and crimes Source: Vallejo, Spatial Data Studio Lab 8, 2019.

But the conclusion is not already finished, we needed to clarify the idea about population and crime, and for the next step we are performing a ‘Spatial Join’ that basically it counts how many crimes are occurring per administrative area. The idea, is to comprehend how much resemblance this resulted (Figure 7) has with the map of population per administrative areas.

Figure 7: Crime Incidents per administrative area Source: Vallejo, Spatial Data Studio Lab 8, 2019

And as it was told, the map of number of crimes per administrative unit resembles with the map of absolute population, both shows concentration in urban areas. At this point, we performed an Correlation Analysis to see how much population and number of crime events are related, the interpretation revealed that R² = 0,95 (Figure 8), it means that the 95% of the variation in the population (dependent variable – y) is explained by the variation in the number of crimes (independent variable – x). (Wiseman, 2013). But the square kilometer analysis is still missing.

Figure 8: Correlation analysis between population and crimes Source: Vallejo, Spatial Data Studio Lab 8, 2019

1.3.2. Square Kilometre Analysis

Working with spatial data requires some clues to be considered, like the spatial normalization. When you are working with absolute numbers or administrative areas the data is unreliable at spatial dimension; in this case, the bigger is the administrative area, bigger is the number of crimes and population. 

Figure 9: Population of Estonia per square kilometre Source: Spatial Data Studio Lab 8, 2019

Figure 10: Fishnet with normalization of population and crime incidents Source: Vallejo, Spatial Data Studio Lab 8, 2019

Figure 11: Correlation analysis between population and crimes - Normalized area Source: Vallejo, Spatial Data Studio Lab 8, 2019

References

• ESRI. (2019). Retrieved 14/11 of 2019, from: http://desktop.arcgis.com/en/arcmap/10.3/tools/
• Wiseman, A. (2013). youtube.com. (T. T. University, Editor) Retrieved 14/ 11 de 2019, from: https://www.youtube.com/watch?v=w5H6ZAg2WIA