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Edmonton Safety Map

The City of Edmonton gathers data based on resident complaints of where residents felt unsafe and I built a dashboard highlighting their experiences

Skills Used

Challenge

Just like starting any other data project, most of the time is spent on cleaning up and standardizing the data. Initially, I had difficulties conducting geospatial analysis since the only geo based column that I had was 'Neighborhood Name'. This required me to use Google Maps and extract the coordinates for each neighborhood into the dataset. Luckily it was as simple as just using the 'Replace' feature in excel, where you would replace the neighboorhood with the coordinates. However, a lot of the times, people call the same location with different names or they spell it wrong, therefore having knowledge of the city was helpful.

Results

Going into the the project, my assumption was that majority of the complaints would be from women as they are harassed significantly more than other genders. Across all the categories of complaints, woman reported more cases of feeling unsafe than any other respondents. This was consistent between all age groups starting at 15 years old. Most of the complaints were from the Edmonton Downtown region during the afternoon, when they go for lunch, or in the evening, when they are leaving work. I think Downtown Business Association and as well as the city of Edmonton should look at addressing this issue as more and more business are calling for people to come back to the office.


Process

Process

Process

01

Data Entry

While the dataset contained neighborhood categorical data, it was lacking the lat and long coordinates that was needed to conduct a geospatial analysis. This required manual entry of the coordinates based on the neighborhood name

02

Data Cleanup

Dataset contained many missing values and empty rows that needed to be processed before I could create the dashboard

03

Summary Statistics

To better understand the trends in the data, I looked at the distribution of the data to better understand trends and identify any outliers

04

Data Visualization

The dashboard was designed in FIgma and build using Tableau highlighting coordinates where people felt unsafe

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

01

Data Entry

While the dataset contained neighborhood categorical data, it was lacking the lat and long coordinates that was needed to conduct a geospatial analysis. This required manual entry of the coordinates based on the neighborhood name

02

Data Cleanup

Dataset contained many missing values and empty rows that needed to be processed before I could create the dashboard

03

Summary Statistics

To better understand the trends in the data, I looked at the distribution of the data to better understand trends and identify any outliers

04

Data Visualization

The dashboard was designed in FIgma and build using Tableau highlighting coordinates where people felt unsafe

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

01

Data Entry

While the dataset contained neighborhood categorical data, it was lacking the lat and long coordinates that was needed to conduct a geospatial analysis. This required manual entry of the coordinates based on the neighborhood name

02

Data Cleanup

Dataset contained many missing values and empty rows that needed to be processed before I could create the dashboard

03

Summary Statistics

To better understand the trends in the data, I looked at the distribution of the data to better understand trends and identify any outliers

04

Data Visualization

The dashboard was designed in FIgma and build using Tableau highlighting coordinates where people felt unsafe

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

01

Data Entry

While the dataset contained neighborhood categorical data, it was lacking the lat and long coordinates that was needed to conduct a geospatial analysis. This required manual entry of the coordinates based on the neighborhood name

02

Data Cleanup

Dataset contained many missing values and empty rows that needed to be processed before I could create the dashboard

03

Summary Statistics

To better understand the trends in the data, I looked at the distribution of the data to better understand trends and identify any outliers

04

Data Visualization

The dashboard was designed in FIgma and build using Tableau highlighting coordinates where people felt unsafe

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights