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Introduction

Over the course of the past few years, there has been an increase in crime, especially in random, unprovoked stranger attacks in the streets of Vancouver (Culbert, 2022). In terms of safety, Vancouver is not as safe a city as it used to be, thus, as locals residing in this city, this elicits our interest in the topic of crime prevalence in Vancouver. Furthermore, in a study conducted by Andreson & Hodgkinson (2021), it was observed that crime has increased significantly since the COVID-19 pandemic began. Needless to say, with such a sudden spike of crime in a city where we once believed to be quite safe even in broad daylight, this is a problem relevant for discussion and analysis. By delving our analysis further into this topic, it could potentially unveil an informative narrative, particularly on the trends of crime over the years and provide details on which neighbourhoods of Vancouver are considered to be hotspots for crime.

Objectives

In terms of audience-action support from a high level perspective, the main goals of our InfoVis project is to support the production of new knowledge in our audience. When engaging in the visual analysis of our InfoVis, the audience can observe how the overall crime rate of Vancouver has changed (i.e the trend) from 2013 to 2022, and how each crime category has changed for the past nine years as well. Moreover, for audiences that are interested in using our InfoVis to help them decide which neighbourhood to buy or rent in, they would have a better sense about which neighbourhoods are relatively more dangerous (i.e the hotspots for crime) or safer than others currently based on the choropleth map. Furthermore, because we hypothesize that crime in Vancouver has especially increased due to the impacts of the COVID-19 pandemic, we will also closely analyze the trend of crime during the years of the pandemic since then as well. 

By investigating this topic, the information accomplished from this project would be helpful to professionals like the police, criminologists and, more generally, the field of criminology research in analyzing and introducing better solutions to reduce crime. At the same time, it would also be helpful to the general public and people who are interested in moving to Vancouver to know more about the crime prevalence in each neighbourhood of Vancouver. For instance, the immigrant population that is new to Vancouver, which makes up about 40% of the total population in Metro Vancouver (NewToBC, 2018), could use this information about crime rates to decide which neighbourhood to purchase or rent a property. As a matter of fact, crime rate is one of the deciding factors when buying a property for many people (e.g., 69% of the potential home buyers in the UK have concerns about the crime activity when looking to purchase, according to a research by Churchill Home Insurance in 2019) and a driving factor in determining a property’s value in cities (Shapiro & Hassett, 2012). To deliver this information to a variety of audiences in the most effective way, we have chosen Tableau as a visual analytics tool for this task. Thus, the three elements we hope to examine for this project are the overall crime trend in Vancouver, the crime trend breakdown for each year and the crime trend by neighbourhood.

Changes

Although our goals remained fairly consistent throughout the design process, we did deviate from our plan a little bit to make some revisions on our topic and in the way that we decided to portray this information. Originally, we had planned to focus our project on the timespan of 2007 to 2022. However, we decided to focus on the years of 2013 to 2022 instead because of how large the data size was in our original plan. This allowed us to keep our data manageable for the cleaning process and design stage. 

Furthermore, we had initially proposed to create a heat map of the city of Vancouver to illustrate the crime rate in different neighbourhoods in which warm hues like red would signify a higher crime rate and cool hues like blue would signify a lower crime rate. Our dataset is sequentially ordered data since it has a homogenous range with a minimum to maximum value. Therefore, using divergent colour hues like red and blue for our choropleth map does not make sense because our data cannot be deconstructed into “two sequences pointing in opposite directions that meet at a common 0 point” (Munzner, 2015, p. 33). Since Munzner (2015) advises that continuous choropleth maps should be used for representing quantitative attributes like spatial fields, following the Expressiveness and Effectiveness design principles of the magnitude channel, we decided to use colour luminance to encode the Crime Trend by Neighbhourhood in Vancouver (p. 225).

Moreover, since we generated all our InfoVis and graphs on Tableau, we did not feel the need to use tools like Infogram, Visme, Piktochart or Canva like we originally proposed. Since Tableau created the type of InfoVis that we were hoping to achieve, we did not feel the need to create an Infographic as well as that would just result in an extra InfoVis that illustrated the same story, but only with a different appearance in terms of font and design choices. Thus, since we did not create an Infographic, it felt unnecessary to create a webpage on Weebly, when we only had to upload our InfoVis on UBC Blogspot.

Data Used

The main data that we used for this project was the crime data retrieved from the Vancouver Police Department website. The source contains data dating back to 2003, all the way to the most current year: 2022. This data is appropriate for this project because The Vancouver Police Department is considered to be a professional source, especially since it holds official government records and data, therefore rendering it to be credible and authoritative. There are no biases that we have been aware of in our runthrough of the data, since it is all purely raw data.

Additionally, we used a most up-to-date GeoJSON file of Vancouver spatial data to map out the Vancouver neighbourhood boundaries for the visualization of our map. We retrieved this data set from The City of Vancouver’s Open Data Portal, and it contains the boundaries for the City’s 22 local areas (also known as local planning areas). We also found that this file is most compatible with the crime data we obtained from the Vancouver Police Department website. 

Tools Used

The main tools that we used to design the InfoVis for our project are Microsoft Excel, Tableau Prep and Tableau Desktop. Specifically, we first used Tableau Prep to join the original datasets, which were organized by year on the Vancouver Police Department website, into one big dataset containing data from 2013 to 2022 using the ‘union’ function. Then, we proceeded to clean the raw data by removing unnecessary attributes (e.g x and y coordinates of the reported crime’s location on the map respectively), and exporting it as an excel file. A strength of this tool is that it lets us keep track of all the changes that we have made to the original dataset. Next, we used Microsoft Excel to make the data compatible with a GeoJSON spatial file, both of which are needed to create our map of Vancouver. A great thing about this tool is that it has a find and replace function, which allows us to make the changes quickly, especially with a big dataset like ours. After this, the cleaned data was imported into Tableau Desktop to create the visualizations. All these tools, including Tableau Desktop, were chosen because we have already received training for these softwares in the class, and we believed that they are the best tools to use for this project.

     

Design Process & Analytic Steps

For our project, we had three different parts that we hoped to investigate on crime in Vancouver: the overall crime trend, the crime type breakdown by year, and the crime trend by neighbourhood. We also included filters in each visualization so that users can adjust their search more easily.

First, to examine the overall crime trend in Vancouver, we created a line chart that spanned from a period of 2013 to 2022. Since the number of reports is a quantitative variable and year is an ordered attribute, we believe that a line chart would be the best option to show their relationship. These attributes and variables are consistent with the principle of expressiveness. Each number that is shown as a point on the graph represents the number of crimes reported in all parts or neighbourhoods of Vancouver of that year. The fact that the numbers are visible in addition to the line allows users to compare and contrast between different years quickly just by looking. This visualization provides an overall picture of how crime in Vancouver has evolved over the past nine years. And a trend line is also added to the line chart to clearly show if crime has increased or decreased over time. 

Next, because we felt that it was equally important to understand what those numbers on the line chart meant, we created a packed bubble chart that shows the breakdown of the types of crime for each of those numbers shown on the line chart. Following the principle of effectiveness, the most important attribute—crime frequency—is encoded with the most effective channel, 2D size (Munzner, 2015). So the bigger the bubble, the more common a type of crime is and the smaller the bubble, the less frequent a type of crime is. And each type of crime corresponds to a different hue, which obeys the principle of expressiveness in design. 

For our third graph, we were interested in creating a choropleth map to illustrate the crime trend of Vancouver by neighbourhood. This allows users to observe which neighbourhoods are relatively more dangerous (i.e., being the hotspots of crime) or safer than others based on luminance. Specifically, a lighter hue of purple represents fewer crime reports, and a darker purple corresponds to more crime reports. Each neighbourhood is also encoded with the position/ spatial region on the map, which is the most effective channel for a categorical variable. These design choices complies with the principle of expressiveness. 

We chose a choropleth map because the regions themselves are encoded using the actual spatial position and the quantitative attribute (number of crimes), which is encoded in the colour hue of purple (Munzner, 2015, p. 181). This regional data is valuable since it allows users to observe how the data is related to the physical space.

Lastly, we created a dashboard that combines the view of the choropleth map with the line chart together so that users could see the big picture of crime in Vancouver over the years, by neighbourhood. Users are able to adjust their filters to a certain neighbourhood, type of crime or year. Moreover, after the filters are applied, the results will be changed accordingly across all four graphs. For instance, if Downtown is selected in the filter, the line chart, choropleth map, and packed bubble chart – all four visualizations will only display the data for Downtown. 

The Story & Key Findings

From the four InfoVis that we have created, we discovered some interesting findings. For instance, 2019 had the highest amount of crime—48,243 reports—out of all the years from 2013 to 2022. In fact, we can compare that number with other years. And from the choropleth map titled ‘Crime Trend by Neighborhood’, we can see that Downtown has the highest number of crime reports among all neighbourhoods in Vancouver, since it has the darkest hue of purple on the map. Furthermore, it is observed that over the years from 2013 to 2021”Theft from Vehicle” is the most common type of crime that was reported. But in 2022, “Other Theft” exceeds that number in “Theft from Vehicle”. So theft in general seems to be the most common crime reported throughout the years consistently. However, a caveat is that the data for 2022 is incomplete, since the year of 2022 had not ended yet at the time we retrieved this data. So the exact numbers might change. Additionally, contrary to our hypothesis, it seems that the number of crime reports have decreased after the pandemic began. In other words, there were fewer crimes in the years after COVID-19 started (2019-2022). And this seems to be the case for every type of crime during this time span. Some possible reasons might be that there were increased travel restrictions and quarantine policies during the pandemic, so people stayed at home most of the time, which led to a decrease in crime activity. At the same time, it is not the case that the crime trend has decreased for every single neighbourhood, but with all the neighbourhoods combined. Therefore, the decrease that we observed is reflective of all the neighbourhoods in the city of Vancouver. This insight is particularly interesting because recent literature has suggested otherwise, and we’d like to explore this more in future projects.

Pros & Cons

In terms of pros, we believe that we have developed effective and user-friendly InfoVis that are quite accessible for a typical user with no specialty in a specific type of background and it would certainly be useful for someone planning or thinking of moving to Vancouver or a young adult planning to move out of their parents’ home to live independently. The InfoVis are pretty straightforward and easy to use, since users can personalize their search results to what they are interested in. Thus, a user would not make the mistake of making a deduction from viewing our InfoVis that Vancouver is dangerous because there are a lot of homicides, but actually that it is because of theft from vehicles.

In terms of cons, the data that we have for the year of 2022 is incomplete since it is not the end of 2022 yet. We downloaded the data on November 20, 2022, so any new data that resurfaces after that on the Vancouver Police Department archives would be excluded from our dataset for this project. This would mean that even if there is a possibility that the statistics could fluctuate, our dataset for this project would be exclusive of that. Another limitation is that the population density is different for each neighbourhood, so that could explain the difference in number of crimes reported by neighbourhood. Specifically in our choropleth map, Downtown showed a much higher crime activity than than other parts as it is the region with the darkest purple hue. This might be due to the fact that it is a neighbourhood that is more densely populated. As such, we hope to explore this factor in future projects.

References

Andresen, M. A., & Hodgkinson, T. (2022). In a world called catastrophe: the impact of COVID-19 on neighbourhood level crime in Vancouver, Canada. Journal of Experimental Criminology. https://doi.org/10.1007/s11292-021-09495-6 

City of Vancouver (2022, November 20). Local area boundary. Open Data Portal https://opendata.vancouver.ca/explore/dataset/local-area-boundary/export/?disjunctive.name

Churchill Home Insurance (2019). Crime fears cause £6.6bn in property sales fall through every year. Retrieved from https://www.churchill.com/press-office/releases/2019/crime-rates

Culbert, L. (2022, August 13). Random stranger attacks in Vancouver: The fear, the reality, the solutions. Vancouver Sun. https://vancouversun.com/news/crime/random-stranger-attacks-in-vancouver-the-fear-the-reality-the-solutions 

NewToBC: The Library Link For Newcomers and Public Library InterLINK (2018). Immigrant Demographics by Community. Retrieved from https://newtobc.ca/settlement-information-for-newcomers/immigrant-demographics-by-community/

Shapiro, R. J., & Hassett, K. A. (2012). The economic benefits of reducing violent crime: A case study of 8 American cities.

Vancouver Police Department. (2022). Crime Data. [Data set]. GeoDASH. https://geodash.vpd.ca/opendata/