A Power BI Dashboard on Cyber Crimes Against Women in India 2017–2022

Research Questions for Power BI Report 

Open Dataset Cyber Crimes Against Women

Here are key questions that will guide both the data model and visual design:

✅ Step-by-Step Implementation in Power BI

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🔹 Step 1: Load Dataset

1. Open Power BI Desktop

2. Click Home > Get Data > Excel (or CSV)

3. Select your dataset and load it

4. Click “Transform Data” to open Power Query

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🔹 Step 2: Data Transformation in Power Query (Unpivoting)

Your data is currently in wide format (years as columns). Unpivot it for time-series analysis.

✅ Transformation Steps:

1. Select year columns: 2017 to 2022

2. Right-click > Unpivot Columns

3. Rename columns:

   • Attribute → Year

   • Value → Cases

4. Ensure data types:

   • States → Text

   • Year → Whole Number

   • Cases → Whole Number

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🧪 Optional M Query (if needed)

Unpivot M Code (automatically generated):

= Table.UnpivotOtherColumns(Source, {"States"}, "Year", "Cases")

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🔹 Step 3: Close & Apply

Click Close & Apply to bring transformed data into Power BI.

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🧮 Step 4: DAX Measures (Basic)

➕ Total Cases

TotalCases = SUM('CyberCrimes'[Cases])

➕ Percent Share by State

PercentOfTotal = 
DIVIDE(SUM('CyberCrimes'[Cases]), CALCULATE(SUM('CyberCrimes'[Cases]), ALL('CyberCrimes')))

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🎨 Step 5: Power BI Report Design

Trend Analysis of Cyber Crimes Against Women in India (2017–2022): A Power BI Dashboard Approach"

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📌 Step 5 Onward: Research Questions + Power BI Report Design

🔍 RQ1: Which states have the highest number of cyber crimes against women overall (2017–2022)?

Purpose: Identify high-risk regions
Visual Type: Bar Chart or Map
Steps:

1. Use a Bar Chart

2. Axis: States

3. Values: SUM of Cases

4. Sort by descending

5. Apply a Top N filter (e.g., Top 10 states)

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🔍 RQ2: How have cyber crimes against women changed over the years nationally?

Purpose: Analyze national trend over time
Visual Type: Line Chart
Steps:

1. Use a Line Chart

2. Axis: Year

3. Values: SUM of Cases

4. Title: "Cyber Crimes Against Women (2017–2022)"

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🔍 RQ3: What is the trend in cyber crimes for individual states over time?

Purpose: Explore time trends state-wise
Visual Type: Line Chart (with legend by state)
Steps:

1. Use a Line Chart

2. Axis: Year, Legend: State

3. Values: SUM of Cases

4. Add a slicer for selecting specific states

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🔍 RQ4: Which states contributed the most to national cyber crime numbers in a specific year?

Purpose: Percentage share per state
Visual Type: Pie or Donut Chart
Steps:

1. Add a Slicer for Year

2. Use a Donut Chart

3. Legend: States, Values: SUM of Cases

4. Add Tooltip for percentage display

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🔍 RQ5: How do cyber crime numbers compare across years in each state?

Purpose: State-level time comparison
Visual Type: Clustered Column Chart or Matrix
Steps:

1. Use a Matrix

2. Rows: State, Columns: Year

3. Values: SUM of Cases

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🔍 RQ6: Which state saw the highest growth/decline in cyber crimes from 2017 to 2022?

Purpose: Growth trend detection
Visual Type: Table with Calculated Column or Line Chart
Steps:

1. Add a custom column in Power Query:

   Pivot table → calculate 2022 - 2017 value
   

2. Create a bar chart of (2022 - 2017) values

3. Sort by increase/decrease

4. Color code bars (positive/negative)

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🔍 RQ7: What are the top 5 states with consistent high incidents over all years?

Purpose: Identify chronic high-risk zones
Visual Type: Bar Chart
Steps:

1. Create a Bar Chart

2. Axis: States

3. Values: AVERAGE(Cases) over 2017–2022

4. Filter: Top 5 average

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🔍 RQ8: What is the state-wise contribution (%) to the national total each year?

Purpose: Yearly percentage contribution
Visual Type: 100% Stacked Bar Chart
Steps:

1. Use a 100% Stacked Bar Chart

2. Axis: Year

3. Legend: States

4. Values: Cases

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🔍 RQ9: What are the total number of cases year-wise across India?

Purpose: Identify peak years
Visual Type: Card + Column Chart
Steps:

1. Create a Card visual:

   • Measure:

     TotalCases = SUM('CyberCrimes'[Cases])
     

2. Create a Column Chart:

   • Axis: Year

   • Value: SUM of Cases

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🔍 RQ10: Drilldown – View district or city-wise trends (if future data available)

Purpose: Deep geographical insights
Visual Type: Hierarchical Map or Drillthrough Page
Steps:

1. Add hierarchy: Country > State > City

2. Use a Map or Table

3. Add drillthrough filters

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🧩 Optional Slicers for Interactivity

• Slicer for Year (single/multi-select)

• Slicer for State

• Slicer for Case Range (e.g., low, medium, high via buckets)

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🔍 Example DAX Measures

TotalCases = SUM('CyberCrimes'[Cases])

AverageCases = AVERAGE('CyberCrimes'[Cases])

PercentOfTotal = 
DIVIDE(
    SUM('CyberCrimes'[Cases]), 
    CALCULATE(SUM('CyberCrimes'[Cases]), ALL('CyberCrimes'))
)

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View Power BI Report