mathematics

Our team is currently working on a project using the ACLED (Armed Conflict Location & Event Data) dataset, which tracks political violence and protests around the world.

We started by cleaning the data, fixing missing values, formatting issues, and making sure everything was ready for analysis. After that, we moved on to data processing, where we filtered by region and year, converted categories into numbers, and created new features like conflict intensity.

Now, we’re preparing for analysis and modeling.

As part of our coursework, our team is currently working on Project 2, which is based on the ACLED (Armed Conflict Location & Event Data) dataset. And also , our team came together to clarify some aspects of the project and to ensure we’re aligned on the next steps. We also had a productive discussion with our professor, where we presented our questions and sought guidance.

Here are some questions :

• Which Indian states or cities have the highest concentration of protests?

• What are the key reasons behind protests recorded in the dataset?

• Which states or regions in India have the highest number of violent events recorded?

• Have violent incidents increased or decreased over the past decade?

In this week’s session, we explored spatial homogeneity by comparing uniform randomness to Poisson randomness.

In a Uniform Random Pattern, each point has an equal chance of being placed anywhere in the area, and the points are evenly distributed across . Where as in Poisson Random Pattern it is more complex, and the distribution of points may vary across, but it still follows certain statistical rules.

Questions :

• How does economic development correlate with conflict intensity?
• Which sectors of society (students, farmers, workers, political groups) are leading the protests?
• Are there seasonal trends in the occurrence of events? Do certain times of the year have more incidents?

 

In this class, we were introduced to a new dataset, the Armed Conflict Location and Event Data (ACLED), focusing on the regions of India and the USA. I am currently going through this dataset.

Here are some questions:

• Which Indian states or cities have the highest concentration of protests?

• What are the key reasons behind protests recorded in the dataset?

• Which states or regions in India have the highest number of violent events recorded?

• Have violent incidents increased or decreased over the past decade?

 

This week, our team of four successfully completed the Project 1. The project was executed as planned, and all objectives were achieved within the timeline.

 

In our recent class, we discussed the Mahnoor Project.

Our team also discussed the report in detail. We have already started working on the next phase of the project, which involves tackling the following question:

1)identifying regions with similar patterns of police cases based on socio-economic and environmental factors
2)Analyzing how these factors influence cases over time (seasonality in crimes related to temperature)
3)Analyzing how age factors correlate with cases(most affect age groups)
4)identifying per capita income correlate for crime
5)Most cases by a Police station or police .

We have made considerable progress and are currently halfway through.

In second lecture we discussed various questions

• Which police department was involved in the most fatal shootings?
• What percentage of individuals who were unarmed showed signs of mental illness?
• What is the distribution of fatal police shootings by state?
• Is there a significant difference in the use of body cameras by police departments across different states, and how does this impact the reporting of fatal incidents?
• Analyzing the correlation between age group and crime rates to determine the most affected demographics.

The first lecture of Advanced Mathematical Statistics (MTH-522) taught by Dr. Gary Davis was  engaging. We had a great conversation about the course, assignments, projects, we shared a lot of interesting topics. I also learned about WordPress site where we can post our journals.

The professor presented dataset of Washington – Post – police – shootings.  I found that there are common datapoints related to age, gender, city, race. This data indicates different weapons, locations, people with different age etc. I analyzed the dataset using Microsoft Excel and came up with interesting questions:

• Which state had the most incidents ?
• What is the age range of individuals involved in the police encounter?
• How many individuals fled the scene during the incidents?
• What type of weapons were involved in the incident?
• In which city the highest number of incidents occur?

However, I’m still enthusiastic about learning more from the dataset.

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