Introduction
In this blog, we introduce a new project focusing on the project requirement titled "Crime Data Analysis and Visualization Project." The requirements encompass various aspects such as data selection from police.uk website, data merging, handling missing values, and structuring for analysis, along with exploring different analysis angles like temporal trends, statistical summaries, and spatial patterns. In the solution approach section, we delve into the specifics of the data used, the processes employed including Python's Pandas library for data manipulation, statistical analysis techniques, and visualization tools such as matplotlib and Plotly Express. We discuss the techniques applied and the insights gained through our analysis, culminating in the output section where we showcase screenshots of key outputs generated from the project.
Project Requirement
Assessment Task:
Using the Crime Data available on the https://data.police.uk website, you are tasked with writing a PYTHON program (or a set of Python programs if you prefer) which Reports, Analyses, and Visualises these datasets.
You are advised to work through the introductory Coursework Tutorial on Blackboard (entitled PYTHON CW Tutorial 1.docx) which will introduce you to the Crime Data (and how these data can be used in Microsoft Excel and Access). By understanding the nature of the crime datasets, you will be able to formulate some ideas as to what might be worth investigating, analysing and visualising in Python).
The Coursework is open-ended, in that it is entirely up to you which datasets you use (e.g.1. South Wales Police; Gwent Police; Metropolitan Police; etc. – or multiple police force data); (e.g.2. August 2021; Summer 2021; 2020; 2018-2021; pre- and post-lockdown or combinations of these). However, it is expected that you will use data from more than one month OR more than one Police Force
The coursework is open-ended in terms of what you do with the data, but you will be assessed in terms of:
The Reporting of the Data Sets (for example, being able to give an overview of the data by month/season/year/Police Force; such as total crimes; break down of crimes; comparison of crimes; or other attributes such as location or outcomes); (Can you identify some interesting facts?)
The Analysis of the Data Sets (Extension of (a) above, but will consider further statistical reporting of the data, such as normalised results by total crime (% of all crime), or even population (crimes per 1,000 people). You may need to source additional data to help you with this. In addition, you may wish to test some hypotheses as part of your analysis, e.g. is crime increasing through time? Is burglary more prevalent in Summer or Winter? Does South Wales Police data correlate with other Police force data for the same time frame? What crimes increased or decreased during the Pandemic Lockdown?)
The Visualisation of the Data Sets (for example, using matplotlib to re-inforce the analysis you have undertaken; e.g. appropriate graphs or visualisation strategy appropriate to the message you wish to get across. As a specific example, the Pie Charts of the breakdown of crimes for August 2021 might be compared between South Wales and the Metropolitan Police).
Advanced Analysis and/or Visualisation. You may wish to explore for yourself some of the other capabilities of Python using the many freely available libraries/extensions. This could be advanced statistical or numerical modelling; or the use of the basemap extension of matplotlib to produce some crime maps; or even crime heat or hotspot maps; or the development of a graphical user interface (e.g. TkInter) for your software.
Documentation of your work and annotation of your code. All of your work must be fully documented in a Word or PDF file; and all of your code and datasets must be supplied in a folder For example, the documentation should focus on each aspect of your software which you wish to highlight, e.g. if your program tests a hypothesis, then clearly state what it is; how you went about testing and implementing this; and the results, including any graphs. If you have used any additional libraries or extensions, then the documentation should clearly state this (and the source / implementation instructions), Make sure that all datasets used for any code are also highlighted in the documentation. Or any pre-conditions as to the pathname for a file. All of your code must be fully annotated, especially the “neat” or complex features. The results of your code should also be fully presented in your documentation, in case the code cannot be executed. High resolution graphical output should be used. However, if you refer to output such as PDFs or animations, these can be included separately in your submission, but include the pathname to the output.
Solution Approach
In this section, let's delve into the methods and techniques used in our crime data analysis project. We followed a systematic approach to gather, process, analyze, and visualize the crime data.
Data Collection and Preparation
We began by gathering crime data from various police forces using Python's Pandas library. This involved merging CSV files, handling missing values, and structuring the data for analysis.
Exploratory Data Analysis
We conducted exploratory data analysis to understand the nature of the crime data. This included examining null values, total crime records, and the distribution of crime types.
Statistical Analysis
We computed statistical summaries such as total crimes over the specified period, the count of last outcome categories, and the percentage of total crimes.
Temporal Analysis
We analyzed temporal trends by computing total crimes in different years (2019, 2020, 2021), seasonal variations (winter 2018-19, summer 2019), and monthly crime counts.
Visualization Techniques
We utilized matplotlib to create various visualizations including line charts depicting monthly crime trends, bar graphs comparing crime rates between years, and percentage breakdowns of crime types.
Spatial Analysis
To understand spatial patterns, we generated heatmaps using Plotly Express. These heatmaps illustrated crime density across geographical locations, providing insights into areas with higher crime rates.
Through this comprehensive analysis and visualization, we gained valuable insights into crime trends, seasonal variations, and geographical hotspots. These findings can be instrumental in making data-driven decisions for crime prevention and law enforcement strategies.
Output
In addition to showcasing our expertise in crime data analysis and visualization, we at codersarts are dedicated to providing comprehensive support and assistance to individuals and teams working on similar projects. Our team of experienced data scientists, analysts, and technical writers is ready to lend a helping hand to those facing challenges or seeking guidance in their projects. Whether you need assistance with data collection and preparation, exploratory data analysis, statistical analysis, visualization techniques, or any other aspect of your project, we are here to help. Feel free to reach out to us for personalized support and expert advice. Your success is our priority, and we look forward to collaborating with you on your data-driven endeavors.
If you require any assistance with the project discussed in this blog, or if you find yourself in need of similar support for other projects, please don't hesitate to reach out to us. Our team can be contacted at any time via email at contact@codersarts.com.