mokesh 522

After reviewing all datasets in the Analyze Boston, We have found a lot of null values and missing values in the different datasets. My teammates and I  have finalized the ” MOVING TRUCK PERMITS” dataset.

The basic python  code is

1. Different types of crimes in different areas:

• Group the data by street and analyze the number of individual crime types on each street.
• Visualize the results using bar charts or other suitable graphs.

2. Most common types of crime, time of day, and day on certain streets:

• Filter the data for each street and analyze the most common crime types, days, and hours.
• Use bar charts, pie charts, or heat maps for visualization.

3. The number of certain crimes in certain places:

• Conduct a temporal analysis to identify trends in specific crime types over time.
• Use line charts or other time series.

4. Over time, the number of common crimes increases:

• Analyze the general trend of common crimes across the dataset.
• Consider creating a time series chart to visualize changes.

5. General residential areas with crime:

• Group the data by neighborhoods to identify areas with higher crime rates.
• Visualize the results with maps or bar graphs.

6. Time analysis:

• Analyze data based on temporal factors such as month, day of the week, and time.
• Identify patterns and trends over time with appropriate visualizations.

7. Map Display:

• Use the latitude and longitude data to draw the map.
• Color-code or size-code data points based on the frequency of crime in each location.

8. Correlation analysis:

• Use statistical methods to identify relationships between different variables (eg, time of day, day, month) and types of crime.
• Visualize correlations using correlation matrices or scatter plots.

9. Analysis of shooting data:

• Analyze shot data separately, and identify patterns and correlations with other variables.
• Visualize image events on a map and examine temporal patterns.

10. Forecasting models:

• Depending on the nature of the data set, you can build predictive models to predict future crimes or classify incidents into different categories.
• Common algorithms are decision trees, random forests or neural networks.

Geospatial Analysis of Violations

Geospatial examination of the dataset can provide valuable information about the geographic distribution of health disorders in different locations. Using the latitude and longitude data available for each facility, a map can be created that visually shows the concentration of violations in specific geographic areas. The purpose of this analysis is to find clusters of non-compliant facilities or areas with consistently high or low levels of compliance. In addition, adding demographic or economic information to the map can reveal relationships between the socioeconomic context of an area and compliance with the health and safety standards of food businesses. Geospatial tools and visualizations such as heat maps or choropleth maps can be used to comprehensively describe the spatial distribution of violations.

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.