Project Objective

• Purpose: Use QGIS to create a heatmap that visualizes the spatial distribution of terrorism incidents in southern Thailand for the year 2020, with the goal of identifying hotspots.

• Skills Demonstrated: GIS analysis, spatial data visualization, and integration of geolocated data with conflict analysis.

Dataset Description

• Source: Global Terrorism Database (GTD)
  • Time Period: Thailand, 2020
  • Columns:
    • latitude: Geolocation of the incidents.
    • longitude: Geolocation of the incidents.
    • nkill: Number of people killed in the incidents.

Dataset Preparation

• Simplify the dataset:

  • The original Global Terrorism Database (GTD) was simplified by retaining only the essential columns: latitude, longitude, and nkill (number of killed). Additionally, the dataset was filtered to include only incidents that occurred in Thailand and further refined to include data only from the year 2020.

• Clean the dataset:

  • There is no empty cell in both of the latitude and the longitude columns.
  • For simplicity, the missing values in the nkill column are filled with 0.

• Save the dataset as a CSV file:

  • The cleaned and simplified dataset was saved in CSV format using XLS to CSV converter to facilitate file format compatibility with QGIS.

Data Import and Setup

• Open QGIS and Import CSV:

  • Launched QGIS.
  • Go to Layer > Add Layer > Add Delimited Text Layer.
  • Select the CSV file and set latitude and longitude as coordinates.
    • Specify the X field as longitude and the Y field as latitude.

• Coordinate Reference System (CRS) Setting:

  • Ensure CRS is set to WGS 84 (EPSG:4326) for proper geolocation.

Before Heatmap Creation: Basic Map Creation

• Data Point Visualization:

  • Before creating heatmap, it is necessary to add data points using the latitude and longitude columns. These data points are later used in the calculations to generate the heatmap.
    • Latitude and Longitude:
      • Role: latitude and longitude columns provide the geographic coordinates for each data point. These coordinates determine where each data point is placed on the map.
      • Impact on Heatmap: These two columns of the dataset influence the spatial distribution of data points.
  • How to add data points to QGIS:
    • Layers Panel > Properties > Symbology > select appropriate symbol

• Base Map Addition:

  • Added OpenStreetMap (OSM) for context.
    • OSM provides a detailed basemap that gives context to the added data points. This context is useful for visually verifying the distribution of the points before switching to more specific province-level shapefiles.
    • How to add OSM to QGIS:
      • Browser Panel > XYZ Tiles: OpenStreetMap > double-click it > verify that OpenStreetMap appears in the Layers Panel

Heatmap Creation

• Heatmap Creation:
  • Install the Density Analysis Plugin:
    • Go to Plugins > Manage and Install Plugins… > Search for “Density Analysis” > Click Install Plugin > Close the dialog once installation is complete
  • Create the Heatmap:
    • Open the Processing Toolbox > Select Styled Heatmap (Kernel Density Estimation)
  • Configure Parameters:
    • Input Point Layer: Choose the dataset file (.csv file)
    • Cell/Pixel Dimension: Set to 0.1 (for higher resolution)
    • Kernel Radius: Set to 4.5 km (to define the influence area of each data point)
    • Color Ramp: Choose Reds (for easy visualization)
    • Interpolation: Set to Linear
    • Mode: Select Continuous
    • Weight Field: the nkill column
      • Role: The nkill field represents the number of people who are killed in the terrorist attacks. It acts as a weight for each data point.
      • Impact on Heatmap: The intensity (darkness or lightness) of the heatmap is influenced by this weight. Areas with higher nkill values will contribute more to the heatmap's intensity, making those regions darker.
  • Run the Analysis:
    • Click Run to generate the heatmap

Findings, Interpretation and Implications

• Description of the heatmap:

  

  | | Number of populations | Number of terrorism incidents | Number of death (nkill) | Death rate per 100,000 population | | --- | --- | --- | --- | --- | | Songkhla | 1,428,609 | 2 | 1 | 0.07 | | Yala | 538,602 | 4 | 2 | 0.37 | | Pattani | 726,035 | 19 | 10 | 1.38 | | Narathiwat | 804,429 | 14 | 5 | 0.62 |

  • Description of the areas:
    • The orange area represents Songkhla province.
    • The blue area represents Yala province.
    • The grey area represents Pattani province.
    • The green area represents Narathiwat province.
  • Description of conflict density:
    • The darker shades of red indicate areas with a higher number of deaths from terrorism incidents, while lighter shades of red represent areas with a fewer number of deaths from terrorist attacks.