Explore businesses on Mass Ave and see the relationship between the type of business and metrics such as stakeholder impact and social media presence.
Most stores and retail services tend to be along Tremont street. Community and public services are located in the outskirts of the Mass Ave neighborhood. Restaurants and cafes are either on or a few blocks away from Mass. Toursim/recreation/arts businesses are located mostly along Mass Ave as well. Health/wellness businesses tend to be in a tight rectangle, a few blocks off of Shawmut Ave. The other types of businesses are rather scattered across the neighborhood. Since community and public services are located on the outskirts of Mass Ave, residents likely will not walk past them. Thus, perhaps flyering or education should be done so residents know about these organizations and understand what community and public resources are available to them.
The most common type of business is stores/retail services with 31 businesses while the least common is education with 3 businesses. Surprisingly, health/wellness and religious services are the second least common business, with each category having 6 businesses. Both health/wellness and religious services provide essential services for human well-being and spiritual health. Perhaps there could be more of these types of businesses to support the health of residents. Further, 5 of the religious services are churches and 1 is a temple. Thus, more religious organizations could be welcomed into the community to be more inclusive of all faiths.
The types of businesses with the highest stakeholder impact is education followed by recreation/arts and restaurants/cafes. Stores/retail services have the least stakeholder impact and thus are not as vital to the community as other types of businesses. Businesses with Facebook pages tend to have more stakeholder impact than businesses without Facebook. This phenomenon presents an area of exploration to see if having Facebook makes businesses more impactful. Finally, businesses with more employees tend to have a higher stakeholder impact.
Overall, our charts highlight that there are geographic clusters of businesses, while some public and community services are in tucked away corners of the neighborhood in places that residents do not frequent thus perhaps residents are not aware of these public resources that are available to them. Further, the community could have more businesses with higher stakeholder impact such as health/wellness and religious services types of businesses. Finally, there appears to be a correlation between businesses with Facebook having a higher stakeholder impact and businesses with more employees having a higher stakeholder impact.
Massachusetts Avenue is one of the main thoroughfares of Boston, MA, containing businesses, parks, college campus, commuter rail stations and so on, which is almost everything of a city. Our main goal of this project is to help the Mass Ave Coalition better know the businesses on the Mass Ave, so that they can have an idea how to support them and to attract more people to the Mass Ave. We firstly gathered and cleaned up the datasets that are related to the businesses and could influence the stakeholder impact levels of the businesses. Then we are going to make interactive visualizations to find what are the factors that would have influence on the stakeholder impact levels. Finally, we will summarize the observations and then present it to the Mass Ave Coalition to help them better know and support the businesses on the Mass Ave.
In our primary datasource, there are two sheets with stakeholder and residential data The Stakeholder data contains columns: Business Name, Type of Organization, Stakeholder Impact Level, Rationale of our choice, # of employees, Address, Email, Phone number, Facebook Handle, Instagram Handle. The Business Name should be categorical since it is not ordered. The Type of Organization is also Categorical. The Stakeholder Impact Level should be ordinal because it is one of high, medium, or low, so is ordered. The Rationale of our choice should be categorical because there is no order relationship within this column. I would say the # of employees should be quantitative data instead of ordinal. Because the population is a continuous data. The Address, Email, and Phone number are all categorical data since they are all non-ordered data. Both Facebook Handle and Instagram Handle are like a yes/no data, so they should all be categorical data. Stakeholder impact is a metric made by student fro Northeastern University's business school who orginally created this dataset. After interviewing business leaders and members from the Mass Ave Coalition, they created a stakeholder impact metric which determines how useful a business is to residents and how much business they attract. More specifically, this metric is based off of: business size, number of employees, business type, how needed it is for the community, and if it encourages business.
The Residential data includes columns: House Number, Street Name, Unit Number, Last Name, First Name, Residential Exemption is Yes/No, and Residential Exemption is No - Address. House Number is ordinal data because although numerical, it does not make sense or give any additional insights to perform mathematical calculations using the numbers. It is also ordinal instead of categorical because house numbers are typically ordered with those closer in value being closer in location. Street Name is categorical data, it is not ordinal because there is no structure in which to order addresses aside from location, which cannot be put on a linear scale in this case. Unit Number is ordinal for the same reason House Number is ordinal. Last Name and First Name are categorical. Residential Exemption is Yes/No is categorical as it has no order, and there is no universally accepted distance between Yes and No. Residential Exemption is No - Address is categorical for the same reason Street Name is categorical. There is an additional dataset available that contains the locations of all vacant lots in the Mass Ave Neighborhood. While we did not have time to include this data in our visualizations because it was not updated with enough time to use it, the data exists and has been updated in the fall of 2022 and can be used for future exploration.
| Index / ID | Domain Task | Analytic Task | Search Task | Analyze Task |
|---|---|---|---|---|
| 1 | Looking at a bar chart, which types of businesses impact community members the most? | Compare | Browse | Present |
| 2 | Looking at a scatterplot, do businesses with more employees have a larger stakeholder impact? | Correlate | Locate | Present |
| 3 | Looking at a bar chart, does the business with Facebook handle have a higher impact level than those without? | Compare | Browse | Present |
| 4 | Looking at a map, are certain types of businesses/vacant properties located in specific regions of Mass Ave? | Compare | Lookup | Present |
For the task with ID #1, the visualization will be developed to understand which types of businesses affect community members in Mass Ave (Table 1). This visualization will help Mass Ave Coalition understand what affects their community members which can be used to understand how different changes in Mass Ave will affect community members. For example, if there is a cafe closing, this visualization can help the Coalition understand how it will affect the community and if they need to prioritize actions to prevent the closing of the cafe before it happens. This visualization will be a present type of consumption since we will identify the types of businesses that affect stakeholders the most and communicate those findings. The primary consumers of the visualization will be the Mass Ave Coalition members.
For the task with ID #2, the visualization will be developed in order to try and see if the number of employees correlates or has any sort of relation to the stakeholder impact of the business (Table 1). This information leads to the high level task on presenting the data and making sense of the results. The primary consumer of our visualization will be the Mass Ave Coalition as well as any business that may be interested in improving stakeholder impact. Businesses will hopefully be able to decide if they need to add more employees if it truly has a positive correlation with stakeholder impact.
For the task with ID #3, the visualization will be developed to see if the Facebook handle will have influence on the stakeholder’s impact level, that is, to see if the business with Facebook handle has a higher impact level than those without the Facebook handle (Table 1). The visualization is to present the data and help Coalition to see if there is a correlation. So the visualization will be a present type of consumption, and the primary consumer will be the Mass Ave Coalition as well as the businesses. Because if the impact level increases by having Facebook handle, the businesses without it can make a consideration to have it.
For the task with ID #4, the visualization will be created to help the viewer understand where types of businesses and vacant properties are located in Mass Ave (Table 1). I think the stakeholders in this case could help the Mass Ave Coalition better understand how to create full utilization of the buildings in the community. This would create a stronger community as well. Another possible example would be that it can help with homelessness. If there are lots of vacant buildings, then they might be better used to host those who don’t have a place to stay. We would be presenting the data since we want to communicate data through a visualization.
By observing the bar chart of average impact score per business type, the “Education” type is obviously the one that has the highest impact level, and “Stores/Retail Services” has the lowest average impact level. Convenience stores and grocery stores are contained in “Stores/Retail Services”, which are the two business types I thought would have “High” impact level because I think people need to get daily supplements with high frequency. So this result is surprising. The second bar chart shows the result that the businesses with Facebook have higher impact level than those without Facebook, which is expected. However, the average impact score of them are both in between medium and low level, and only with a small difference, which I would infer that the relationship between having the Facebook handle and the impact level is not strong. Then, by observing the outcome of the two interactive charts, I cannot conclude if there is a correlation between the number of employees and the impact level because I don’t see an apparent pattern on those two charts, although the class of businesses with employee number less than 25 has the lowest average impact score.
For our final design, we decided to make visualizations of tasks that related to the stakeholder impact level. The first task should be the average stakeholder impact level per business type. For the business types in this chart, we condensed and summarized the types into 8 categories, because the business types in the original dataset were too many to display all of them in one bar chart. The second task should be the average stakeholder impact level of businesses with or without Facebook. Because both x-axes of the first two visualizations are categorical data, using bar charts is more reasonable. Also we choose to average the stakeholder impact level to make the impact level of each category more intuitional. To calculate the average impact level, we decided to assign numbers to each impact level (e.g. score of high level is 5, score of medium level is 3, and 1 is the score of low level), then sum all scores of the businesses belong to that specific category, and finally divided by the total number of businesses of that category. The last task is the stakeholder impact level with respect to the number of employees of each business. We were going to use a scatter plot because the number of employees is continuous, and colored the points by business categories. However, after we plot the scatter plot, it is hard to find if there is a correlation between the impact level and employee number. So we decided to add a bar chart with classed employee number (i.e. the employee number class of “0-25”, “26-50”, “51-75”, and “>75”) and the average impact level score, which we think could have clearer and more directly view of the correlation.
In our final design, we are going to implement brushing, linking, and filtering to allow the user to click to select the business types or drag an area on the map to see the further information of impact levels, that is, after the user select a group of businesses, the three charts would display only the selected data, and the geographic map would also shows the selected businesses only. By having these interactive components, the user can have the respective impact level information of the businesses they are interested in, besides the overview of all businesses. The reason we choose this visualization is because it includes the text that can help the user understand our visualizations more. And this could make the visualizations stick together instead of piece by piece look. For the final sketches, we are going to use Altair and Tableau.