My team’s project was to analyse crimes around Schools in different neighbourhoods of West Side Vancouver in 2015. To do so the group acquired data from the online Open Data Catalogue of the City of Vancouver,and from the database of the Geography Department of the University of British Columbia. In order to carry out the analysis, our group compared crime occurrences around schools to overall crime within individual neighbourhoods, as well as to our entire targeted project area, and moreover evaluated the results to the different relative number of schools in each of the 12 targeted neighbourhoods. The organisation of the project was very fluid; each team member took on a more focussed jobs (Map making, FlowChart drawing, Uncertainty analysis) yet, the overall flow of the analysis was carried out together as a team, in the lab, as well as outside through constant communication between each members and regular updates on the advancement of the work. Such effective communication enabled our group to come to the consensus that our original project proposal had to be altered. Indeed, our original idea of analysing the impact of urbanisation and human activities on Wild life species around Vancouver was short-ended and few data was available. Consequently, our team quickly refocused and promptly found another topic of interest; that of mapping out the relative safety for children to attend school in different neighbourhoods in the West Side of Vancouver.

As a matter of fact, acquiring the data was rather easy. Analysing the data and summarising it was more challenging. The tools of Clipping, Intersecting and Buffer from the previous labs in the course were very useful to target and focus our analysis. Furthermore, the tool of “Summarise” in the layers’ attribute tables were perhaps the most important for our analysis as they enabled summarising crimes per neighbourhood and permitted the calculations of the different percentages required for 3 out of 4 of our maps through the Field Calculator. Moreover, by joining tables with layers, and creating new data frames the group was able to carry out precise comparison of the different data that was calculated through the analysis with ArcMap. Many steps were required to achieve different final maps, and this project did confirm that organisation in Geographic information Systems is crucial. Naming layers, creating Summary Output tables, adding fields, and joining tables can quickly become confusing if layers are not probably organised and named. Thankfully the team was effective in organising its work which rendered the final analysis of the large amount of information we build up easier and more focussed.

Final Submission of Project: gis_final_project

Flowchart of analysis: flowchart