Month: November 2023

Temporal Analysis of Violations

A valuable perspective in examining the data set is the temporal analysis of recorded violations. To do this, it is necessary to study how the frequency and characteristics of violations change over time. Grouping the data by inspection dates allows trends in both compliance and non-compliance to be identified. For example, you can investigate whether certain types of violations occur more often in certain months or seasons. In addition, delving into the time intervals between successive inspections of each site provides insight into the effectiveness of corrective actions implemented by companies. Visual tools such as line charts or heat maps can effectively illustrate temporal patterns in violations.

This week, I plan to analyze the dataset available at:

https://data.boston.gov/dataset/active-food-establishment-licenses

Approach 1 for Data Analysis: Inspection Results Overview

Within the dataset, which encompasses information about diverse food establishments, with a particular emphasis on restaurants, a thorough examination can reveal insights into their adherence to health and safety standards. The dataset comprises details like business names, license information, inspection results, and specific violations observed during inspections. One method of dissecting this information involves creating a comprehensive overview of inspection results for each establishment. This might entail computing the percentage of inspections resulting in a pass, fail, or other status. Furthermore, uncovering patterns in the types of violations documented and their occurrence across different establishments can offer valuable insights. Visual aids such as pie or bar charts can effectively communicate the distribution of inspection results and the most frequently observed violations.

Concluding my analysis of this dataset, I focused on Business Growth and Collaboration aspects.

To support business growth, it is important to understand key factors such as company size, service offerings, and collaboration opportunities. By researching companies such as “IMMAD, LLC” in Forensic Research or “Sparkle Clean Boston LLC” in Cleantech/Greentech research, specific niches with growth potential are identified. Strategic implementation of targeted marketing and innovation in these areas can pave the way for expansion.  In addition, the recognition of companies open to cooperation is crucial to promote a mutually beneficial environment. For example, Boston Property Buyers and Presidential Properties, both active in the real estate industry, offer opportunities for joint ventures, shared resources, and a stronger market presence through strategic partnerships. Finally, companies that do not have a digital presence or do not have complete data, labeled “Not Yet” and “N/A” provide opportunities for improvement. Implementing digital strategies, such as building a website or optimizing contact information, can improve visibility and reach, which contributes to the overall success of the business.

I continued the analysis in the same material, delved into digital presence and communication.  The dataset provides an overview of companies’ online presence, including websites, email addresses and phone numbers. Understanding the digital landscape is critical in today’s business environment. For example, companies like “Boston Chinatown Tours” and “Interactive Construction Inc.” are websites that provide opportunities for digital marketing, customer engagement and e-commerce. Evaluating the effectiveness of these online platforms and optimizing them for user experience can improve business visibility and customer engagement. In addition, a critical aspect is the analysis of contact information such as email addresses and phone numbers, which play an important role in communication strategies. Companies such as “Eye Adore Threading” and “Alexis Frobin Acupuncture” have multiple points of contact to ensure accessibility to potential clients. Using data-driven communication strategies such as email marketing or SMS campaigns can improve customer engagement and retention.  Examining the “Other Information” field to indicate whether the business is “minority” or “immigrant” can affect marketing stories. Incorporating these aspects into digital communication can have a positive impact on different target groups, fostering a sense of community and inclusion.

Businesses in this dataset are classified based on important criteria such as Business Name, Type, Physical Location/Address, Zipcode, Website, Phone Number, Email, and Other Information. The first step in the study is to classify organisations by kind, such as “Advocacy for Special Kids, LLC” and “HAI Analytics” under Education, “Alexis Frobin Acupuncture” and “Eye Adore Threading” under Healthcare, and “CravenRaven Boutique” and “All Fit Alteration” under Retail. This classification aids in comprehending the various industries.

Another critical step is to investigate the geographical dispersion of firms. Analysing physical locations and zip codes exposes business clusters in specific places, such as the 2116 zip code’s concentration of services with businesses such as “Boston Sports Leagues” and “All Things Visual.” This analysis of spatial distribution aids in targeted marketing and resource allocation.

In addition, examining the “Other Information” section, particularly data such as “Minority-owned” and “Immigrant-owned,” reveals socioeconomic insights. This data is critical in identifying firms that promote diversity and inclusion by providing strategic options for community development and economic empowerment by assisting minority and immigrant-owned enterprises.

In addition, examining the “Other Information” section, particularly data such as “Minority-owned” and “Immigrant-owned,” reveals socioeconomic insights. This data is critical in identifying firms that promote diversity and inclusion by providing strategic options for community development and economic empowerment by assisting minority and immigrant-owned enterprises.

 

 

Various data analysis methods can be used to analyse the Hyde Park data over time. Begin by examining population trends over time to identify significant changes in each demographic category. Bar charts can be useful for depicting changes in age distribution and demonstrating how the population structure has evolved. Pie charts and bar graphs can effectively represent educational attainment levels, providing clear visuals of the community’s educational progress. Analysing nativity and race/ethnicity data entails observing changes in the demographic makeup using percentage distribution methods. Gender-based labour force participation can be represented graphically to reveal changing workforce trends. For housing tenure, pie charts or bar graphs can be used to show the shifting balance of owner-occupied and renter-occupied housing.

 

Today I looked at the Excel sheet’s second “Back Bay” page https://data.boston.gov/dataset/neighborhood-demographics.

The Back Bay dataset provides insights into the neighborhood’s evolution over time, allowing for a comprehensive analysis of various demographic aspects. Population fluctuations are notable, with a decline until 1990 followed by relative stability. Age distribution highlights shifts in the percentage of residents across different age groups, with the 20-34 age bracket increasing from 32% in 1950 to 54% in 1980. Educational attainment shows shifting proportions of people with varying levels of education, most notably a significant increase in those with a Bachelor’s Degree or higher from 20% in 1950 to 81% in 2010.

I’m currently looking through the dataset on Analyse Boston, specifically the “Allston” sheet within the “neighborhoodsummaryclean_1950-2010” Excel file, which can be found at https://data.boston.gov/dataset/neighborhood-demographics. The dataset provides an in-depth look at demographic and socioeconomic trends in Allston over several decades. Notably, there is clear population growth between 1950 and 2010. The data on age distribution reveals interesting patterns, such as shifts in the percentage of residents across various age groups over time. Data on educational attainment reflect changes in the population’s education levels, most notably a significant increase in the percentage of people with a Bachelor’s degree or higher. The nativity data reveals the percentage of foreign-born residents, indicating changes in immigration patterns.

This is Project 2 for MTH 522 at the University of Massachusetts Dartmouth.

Project Title:

Spatial and Demographic Analysis of Police Shootings Related to Police Station Locations in the United Statesproject 2 mth

In today’s analysis

Data Loading: Imported police shooting data from an Excel spreadsheet into a Pandas Data Frame.

Justification Criteria: A function was defined to decide if the use of force was justified, taking into account the threat types and weaponry involved.

Data Transformation: Applied the justification function to the dataset, resulting in the creation of a new column reflecting the force’s justification status.

Data Filtering: The data was filtered to include only instances involving people of the Black, White, Hispanic, and Asian race groupings.

Gender Separation: The filtered data was separated by gender to analyze incidences affecting both males and females.

Calculation of Occurrences and Percentages: For each ethnic group, the occurrences and percentages of ‘False’ justified force instances were calculated.