Infographic representation of crimes in India

In this blog I will show the visualizations of the crimes related to dacoity, theft, robbery and burglary happened in India between 2001-2012.

I have done this observation with the help of jupyter notebook, pandas, matplotlib and folium to process the data and to create visualizations and maps.

You can read more about data visualization and importance here.

It is just for non-commercial learning purpose and all the data is taken from data.gov.in

So let’s get started.This data set contains all the crimes related to stealing in every state of India between 2001-2012 along with the place of occurrence.Crimes related to stealing have been divided into 4 parts-

  • Dacoity
  • Theft
  • Robbery
  • Burglary

We have a state wise data set for each year from 2001-2012 as shown below-

dataset

First of all, I filtered the data set into various formats to observe various parameters-

Total Number of cases reported in India, 2001-2012

I converted the data set into year vs total number of cases format and I got the graph like this –

cases in India

But no interesting pattern can be observed here as all of the data is almost same for each year, except the bars for number of theft cases reported in 2006 and 2012 are little higher than other years. But this also does not give us any interesting observation to drill this down further.

So let’s move on to next observation.

Total number of cases reported in India state wise, 2001-2012

strealing-cases-state-wise

Now, if you observe carefully you can see that Maharashtra tops the number of cases in almost every segment but theft cases are unusually higher in Maharashtra.

State wise cases of theft, robbery, dacoity and burglary

Lets explore this further by breaking down the categories into different graphs.

Observations

Maharashtra tops the list in 3 out of 4 categories. Only in dacoity, Bihar tops the list and Maharashtra is on the second number.

Some of the North Eastern states like Manipur, Mizoram, Meghalaya, Nagaland has very low level of crime(stealing) followed by Himalachal Pradesh and Andaman island.

Dacoity have had a large impact in the state such as Bihar, Assam, Gujrat, Jharkhand, Maharashtra, Odisha, West Bengal and Uttar Pradesh in north-central India.
May be because of difficult terrains and forests present in these regions.

Choropleth maps

Till now yo have seen bar graph visualization. Here is one more way of visualizing this type of data which is much more intuitive than bar graphs. It is called Choropleth maps.

Here is state wise representation of number theft and dacoity cases in India.

In choropleth map area with higher number is shown in dark and color keeps on getting lighter as the numbers decreases.

I have also observed various other parameters using this data set, but I won’t discuss them here. Instead if you are interested in data analysis then you can explore them yourself. This data set is very simple in terms of structure and information. You can find these types of datasets very useful in learning data analysis.

In the second part of this blog, I will demonstrate the techniques and process I followed to analyse the data set which will include –

  • Reading data
  • Processing data
  • Creating graphs
  • Creating maps

If you like this post then please share. I will try to bring more interesting data sets to you in future.
Thank you for reading 🙂

2 Responses

  1. May 18, 2020

    […] recommend you to check this post to view more types of visualizations with a real dataset […]

  2. June 25, 2020

    […] map represents the number of cases of theft that happened during 2001-2012 in each state of India. The higher the measurement the darker the […]

Leave a Reply

%d bloggers like this: