7 The Evolution of Global Political Structures and Their Spatial Dimensions

7.1 Introduction

The evolution of global political structures is deeply intertwined with spatial dimensions, influencing the formation and transformation of nation-states, empires, and international organizations. Geography, borders, and territory have long been fundamental in shaping political power and governance. This chapter explores these spatial dynamics, offering a comprehensive analysis of how they have historically and contemporarily influenced global political structures, with a focus on the role of human population sizes through the ages.

7.2 Historical Evolution of Population Sizes and Political Structures

Population Growth and Political Structures

Human population sizes have been a critical factor in the development of political structures throughout history. As populations grew, the need for organized governance and territorial control became more pronounced, leading to the establishment of complex political entities.

Early Civilizations and Population Concentration: The emergence of early civilizations, such as those in Mesopotamia, Egypt, and the Indus Valley, was closely linked to the concentration of populations in fertile regions. These areas supported larger populations, necessitating more sophisticated forms of governance.
Empire Building and Population Dynamics: The rise of empires, such as the Roman, Persian, and Ottoman Empires, was driven by the need to manage growing populations and access more resources. This led to the development of intricate administrative structures and the establishment of borders.
Nation-States and Population Distribution: The transition from empires to nation-states in the post-medieval period was influenced by changes in population distribution and density. The Peace of Westphalia in 1648 marked the beginning of the modern state system, where territorial sovereignty became a fundamental principle, often defined by natural borders that corresponded to population centers.

The Impact of Colonialism and Global Governance

Global political structures have undergone significant changes over time, driven by colonialism, economic interests, and ideological shifts. The legacy of colonial borders and the emergence of global governance systems like the United Nations and the European Union illustrate the profound impact of spatial and demographic considerations on political evolution.

Colonialism and Its Aftermath: Colonial powers imposed artificial spatial divisions on territories, often disregarding existing cultural and ethnic boundaries. These colonial borders have had lasting impacts, contributing to post-colonial conflicts and shaping the political landscapes of many nations today.
Global Governance: The formation of international organizations and treaties has increasingly transcended national borders, addressing global issues such as trade, security, and human rights. The establishment of the United Nations in 1945 and the European Union in 1993 are key examples of how political structures have evolved to manage international relations on a global scale.

7.3 Mapping History: Analyzing Historical Administrative Data Using SUNGEO and GIS Tools

Understanding the evolution of political structures requires analyzing historical administrative data, which is often complicated by shifts in borders and governance systems. The SUNGEO Historical GIS Boundaries dataset and Geographic Information Systems (GIS) provide powerful tools for addressing these challenges.

Working with Historical GIS Boundaries

Historical GIS boundaries allow researchers to analyze how administrative regions have changed over time. By using the SUNGEO package, researchers can download, visualize, and align historical boundaries with contemporary data, facilitating more accurate historical analyses.

QGIS Steps for Mapping Historical Boundaries

Load Historical and Contemporary Data into QGIS:
- Import the historical boundary dataset (SHGIS.geojson) into QGIS.
- Import the contemporary boundary dataset for comparison.
Visualize the Boundaries:
- Use the “Symbology” settings in QGIS to differentiate between historical and contemporary boundaries by color.
- Overlay the two datasets to visually inspect how boundaries have shifted over time.
Analyze Boundary Changes:
- Use the “Difference” tool in QGIS (found under Vector > Geoprocessing Tools) to identify areas where boundaries have changed.
- Create maps showing the difference between historical and contemporary boundaries.

R Code for Historical GIS Analysis

Here is a practical exercise in R to work with historical GIS data:

# Install and load necessary packages
install.packages("SUNGEO", dependencies = TRUE)
install.packages("sf")
install.packages("ggplot2")

library(SUNGEO)
library(sf)
library(ggplot2)

# Download and load historical GIS data
download.file("https://example.com/path_to_SHGIS.geojson", destfile = "SHGIS.geojson")
historical_boundaries <- st_read("SHGIS.geojson")

# Visualize historical boundaries
plot(st_geometry(historical_boundaries), main = "Historical Administrative Boundaries", col = "lightblue", border = "black")

# Enhanced visualization using ggplot2
ggplot(data = historical_boundaries) +
  geom_sf(fill = "lightblue", color = "black") +
  theme_minimal() +
  labs(title = "Historical Administrative Boundaries", caption = "Source: SUNGEO Historical GIS")

Application: Mapping the Evolution of Political Boundaries

Using the above tools, researchers can map the evolution of political boundaries over time, providing insights into how historical events, population movements, and governance changes have shaped contemporary political landscapes.

7.4 Mapping Political Risks: Geolocated Political Data for Risk Assessment

Geolocated political data has become increasingly vital for risk assessment in fields such as international relations, security studies, and humanitarian aid. By examining spatial patterns in political events, conflicts, and social movements, researchers and policymakers can anticipate and respond to emerging risks more effectively.

Analyzing Geolocated Political Data

Geolocated political data includes information on election results, conflict zones, and protest movements that can be geographically pinpointed. Tools like GIS enable the visualization and analysis of these spatial patterns, providing deeper insights into the dynamics of political risks.

QGIS Steps for Mapping Political Risks

Load Geolocated Data into QGIS:
- Import geolocated datasets (e.g., conflict zones, election results) into QGIS.
Create Heatmaps:
- Use the “Heatmap” tool (found under Raster > Heatmap) to visualize concentrations of political events.
Overlay Data Layers:
- Overlay different data layers (e.g., protest locations and election outcomes) to analyze correlations between political risks and geographic factors.

R Code for Political Risk Analysis

# Load necessary packages
library(sf)
library(ggplot2)

# Load geolocated political data (hypothetical example)
political_data <- st_read("path_to_political_data.geojson")

# Create a basic map of political events
ggplot(data = political_data) +
  geom_sf(aes(fill = event_type), color = "black") +
  theme_minimal() +
  labs(title = "Geolocated Political Events", caption = "Source: Example Dataset")

Application: Populism and Economic Impact

In studying the rise of populist leadership and its economic impact, spatial analysis can reveal correlations between political trends and regional economic performance. By mapping these trends, researchers can gain insights into the spatial dynamics of populism and its effects.

# Aligning economic and political data spatially using SUNGEO
economic_data <- st_read("path_to_economic_data.geojson")
populism_data <- st_read("path_to_populism_data.geojson")

# Spatial join between economic data and populism data
joined_data <- st_join(economic_data, populism_data, join = st_intersects)

# Visualizing the relationship between populism and economic performance
ggplot(data = joined_data) +
  geom_sf(aes(fill = economic_performance), color = "black") +
  facet_wrap(~populism, scales = "free") +
  theme_minimal() +
  labs(title = "Economic Performance under Populist Regimes", caption = "Source: Example Dataset")

7.5 Mapping Health: Spatial Dynamics of Pandemics

Pandemics have always had significant spatial dimensions, spreading across regions with varying impacts depending on geographic, social, and political factors. Understanding pandemics from a spatial perspective is crucial for developing effective public health responses.

Historical and Contemporary Pandemics

Historical Pandemics: The Black Death and the Spanish Flu are examples of pandemics that were shaped by geography. Mapping the spread of these diseases helps illustrate the role of trade routes, population density, and movement in pandemic dynamics.
Contemporary Pandemics: The COVID-19 pandemic highlighted the importance of global connectivity in disease spread. GIS tools were crucial in tracking the virus and informing public health strategies, allowing for targeted interventions and resource allocation.

Spatial Analysis in Pandemic Response

GIS and spatial data have become indispensable in managing pandemics. By mapping the spread of diseases, identifying hotspots, and analyzing the effectiveness of interventions, spatial analysis provides essential insights for public health planning and response.

QGIS Steps for Pandemic Mapping

Import Health Data into QGIS:
- Load datasets containing COVID-19 case numbers, vaccination rates, or other relevant health data.
Create Time-Series Maps:
- Use QGIS’s “Temporal Controller” to animate the spread of disease over time.
Analyze Hotspots:
- Use the “Cluster and Outlier Analysis” tool (found under Vector > Analysis Tools) to identify areas with high concentrations of cases.

R Code for Pandemic Mapping

# Load necessary packages
library(sf)
library(ggplot2)

# Load pandemic data (hypothetical example)
pandemic_data <- st_read("path_to_pandemic_data.geojson")

# Visualizing the spread of the pandemic over time
ggplot(data = pandemic_data) +
  geom_sf(aes(fill = case_count), color = "black") +
  theme_minimal() +
  labs(title = "Pandemic Spread Over Time", fill = "Case Count",
       caption = "Source: Example Dataset") +
  facet_wrap(~date, scales = "free") +
  scale_fill_viridis_c()

Application: Pandemic Mapping Using EpiBibR

EpiBibR is a powerful tool for pandemic mapping that integrates spatial data to track the dynamics of disease outbreaks. By using spatial analysis, researchers can assess the spread of diseases, identify vulnerable regions, and evaluate the effectiveness of public health interventions.

Workflow for Using EpiBibR in R

# Install and load EpiBibR and other necessary packages
install.packages("EpiBibR", dependencies = TRUE)
library(EpiBibR)
library(sf)
library(ggplot2)

# Load pandemic data into R
pandemic_data <- st_read("path_to_pandemic_data.geojson")

# Use EpiBibR to perform advanced spatial analysis
# Example: Basic mapping of disease spread
plot(pandemic_data["cases"], main = "Pandemic Spread", col = "red")

# Advanced: Temporal analysis using time-series plots
ggplot(data = pandemic_data) +
  geom_sf(aes(fill = cases), color = "black") +
  theme_minimal() +
  labs(title = "Pandemic Case Counts Over Time", fill = "Cases") +
  facet_wrap(~time_period, scales = "free") +
  scale_fill_gradient(low = "yellow", high = "red")

Using GIS to Improve Public Health Responses

Spatial analysis using GIS allows public health officials to track the spread of pandemics in real-time, identify potential hotspots, and allocate resources more effectively. This approach is crucial in managing public health crises, ensuring that interventions are targeted and timely.

7.6 Conclusion

The spatial dimensions of global political structures, population dynamics, and public health are critical to understanding how power, governance, and societal outcomes are shaped by geography. This chapter has provided a comprehensive overview of how historical, political, and health-related phenomena are interconnected with spatial factors. By leveraging tools like QGIS and R, researchers and policymakers can analyze these spatial dimensions with precision, gaining insights that are essential for addressing complex global challenges.

From mapping the evolution of political boundaries to assessing the spatial dynamics of pandemics and political risks, the integration of GIS and spatial analysis in research offers a powerful means to understand and navigate the complexities of our interconnected world. As the importance of location and spatial relationships continues to grow in global affairs, the ability to effectively utilize these tools will be indispensable in both academic research and practical applications.

This comprehensive approach, merging historical analysis with modern spatial tools, provides a foundation for future research in understanding the evolving landscape of global politics, population dynamics, and public health.

7.7 Exercise 1: Hands-On: Locating Nearest Facility with Origin-Destination Matrix in QGIS

In this hands-on section, we will explore how to use QGIS to perform network-based analyses, specifically creating an Origin-Destination (OD) Matrix. This method is widely used in logistics, urban planning, and service optimization. By calculating the shortest path between origin and destination points, you can identify the closest facilities, such as finding the nearest warehouse to a customer or the nearest health facility to a given address.

In this exercise, we will calculate the shortest travel distance from various addresses to the nearest mental health facilities in Washington, DC, using QGIS and the QNEAT3 plugin.

Objectives:

Use QGIS to create an Origin-Destination (OD) Matrix.
Extract random points from a dataset for analysis.
Run SQL queries on QGIS layers using virtual layers.
Analyze shortest paths between origin and destination points using a road network.

Step 1: Data Setup and Plugin Installation

Download the Required Data
- The District of Columbia provides open datasets for a variety of uses. For this tutorial, download the following shapefiles from the DC Open Data Catalog:
  - Roadway Block (road network data)
  - Address Points (representing addresses in the city)
  - Community-Based Service Providers (representing mental health facilities)
- You can download a copy of the data directly from these links:
Install the QNEAT3 Plugin
- QGIS provides the QNEAT3 plugin, which enables network analysis, including the calculation of distance matrices. To install it:
  - Go to Plugins > Manage and Install Plugins.
  - In the search bar, type QNEAT3 and select it from the list.
  - Click Install and close the dialog.

Step 2: Loading and Filtering the Data

Load the Community-Based Service Providers Layer
- In the Browser Panel, locate the Community_Based_Service_Providers.shp file from the downloaded data.
- Drag the shapefile onto the canvas to load it.
- This layer contains points representing service centers across the city.
Filter for Adult Service Providers
- We only need to focus on centers that provide services for adults. To filter:
  - Right-click on the Community_Based_Service_Providers layer in the Layers Panel and select Filter.
  - In the Query Builder dialog, enter the following expression:
```
"PROVIDER_T" IN ('Adult','Adult & Child')
```
  - Click Run to apply the filter. Only facilities providing services to adults will remain on the map.
Load the Roadway Block and Address Points Layers
- Similarly, load the Roadway_Block.shp and Address_Points.shp layers by dragging them from the Browser Panel onto the canvas.
- These layers represent the road network and addresses, respectively.
Randomly Sample 1000 Address Points
- The Address_Points layer contains many points representing addresses throughout the city. To simplify the analysis, we will select a random subset of 1000 addresses:
  - Go to Processing > Toolbox.
  - Search for the Random extract algorithm (located under Vector Selection).
  - In the dialog, select Address_Points as the input layer, set the method to Number of features, and enter 1000 in the Number/percentage of features field.
  - Save the output as address_point_subset.shp in your working directory and click Run.
Rename the Layers
- Rename the newly created random subset layer as origin_points by right-clicking on it in the Layers panel and selecting Rename Layer.
- Similarly, rename the Community_Based_Service_Providers layer to destination_points for clarity in the upcoming steps.

Step 3: Creating the Origin-Destination Matrix

Open the QNEAT3 Distance Matrix Tool
- In the Processing Toolbox, search for the OD Matrix from Layers as Line (m:n) algorithm under the QNEAT3 section.
- This tool calculates the shortest distance between each origin point (address) and destination point (health facility) using the road network.
Configure the Distance Matrix Algorithm
- In the OD Matrix from Layers as Line dialog:
  - Select Roadway_Block as the Network layer (this represents the road network).
  - Select origin_points as the From-Points layer and OBJECTID as the unique point ID.
  - Select destination_points as the To-Points layer and OBJECTID as the unique point ID.
  - Set the Optimization criterion to Shortest Path to calculate the shortest travel distance between origin and destination points.
Handle Road Directions
- Many streets in Washington, DC, are one-way, so we need to specify the correct traffic directions:
  - Expand the Advanced Parameters section and select SUMMARYDIR as the Direction field.
  - Enter the following values:
    - OB for outbound (forward direction)
    - IB for inbound (backward direction)
    - BD for two-way streets (both directions)
Run the Algorithm
- Click Run to generate the origin-destination matrix. The algorithm will calculate the shortest path between each origin and destination point, considering the road directions.
- Once complete, a new table layer named Output OD Matrix will appear in the Layers panel.

Step 4: Analyzing the Results

Inspect the OD Matrix
- Right-click the Output OD Matrix layer in the Layers panel and select Open Attribute Table.
- The table will contain 67,000 rows (1000 destination points for each of the 67 origin points), with the total_cost column representing the distance between each origin and destination point in meters.
Find the Nearest Facility
- We want to identify the nearest service provider for each origin point. To do this, we will run a SQL query to find the facility with the shortest distance for each origin.
- Go to Processing > Toolbox and search for the Execute SQL tool under Vector general.
Create the SQL Query
- In the SQL Query dialog:
  - In Additional input data sources, select Output OD Matrix.
  - Click the Summation icon to enter the SQL query.
  - Enter the following query to find the minimum distance for each origin:
```
SELECT origin_id, destination_id, MIN(total_cost) AS shortest_distance, geometry
FROM input1
GROUP BY origin_id
```
  - Click Run.
Review the Results
- A new layer called SQL Output will be added to the Layers panel.
- This layer contains the shortest distances between each origin and its nearest facility. Right-click the layer and open the attribute table to see the results.
- The shortest_distance field represents the minimum distance from each origin point to its nearest health facility.

Conclusion

In this exercise, you learned how to create an Origin-Destination Matrix in QGIS to locate the nearest facility using the shortest path. You explored how to: - Filter data to focus on relevant points of interest. - Randomly sample data for analysis. - Use the QNEAT3 plugin to calculate shortest paths between origin and destination points. - Run SQL queries on virtual layers to refine the results.

These techniques can be applied to various scenarios, from logistics and delivery optimization to emergency response planning and urban service analysis.

7.8 Exercise 2: Mapping Political Risks Using QGIS

In this hands-on section, we will explore how to create and visualize heatmaps using QGIS. Heatmaps are a powerful way to represent the density of point data, making them an ideal tool for visualizing phenomena such as crime hotspots, traffic incidents, or population density. In this exercise, we will work with crime location data for Surrey, UK, to create a heatmap and explore advanced styling options.

Objectives:

Learn how to load CSV data and basemaps into QGIS.
Create and style a heatmap using QGIS’s heatmap renderer.
Incorporate weighted data into the heatmap to account for the severity of events.
Generate a raster heatmap using the Kernel Density Estimation algorithm for advanced customization.

Step 1: Data Setup and Loading the Basemap

Download the Crime Data
- The dataset we will use contains crime locations in Surrey, UK, available in CSV format from data.police.uk. You can download a copy directly from the following link:
- Download the crime dataset or 2019-02-surrey-street.csv.
- Unzip the downloaded archive to extract the CSV file.
Add a Basemap
- In QGIS, from the Browser panel on the left, scroll down and find the XYZ Tiles section.
- Expand it and drag the OpenStreetMap tile layer to the canvas. This will load a street map of the world as your basemap.
Import the CSV Data
- Click the Open Data Source Manager button (a yellow database icon) on the toolbar.
- In the dialog that appears, select the Delimited Text tab.
- Click the … button next to File name and browse to select the 2019-02-surrey-street.csv file you extracted earlier.
- QGIS should automatically detect the Longitude and Latitude fields under the Geometry Definition section. Make sure the Geometry CRS is set to EPSG:4326 - WGS 84.
- Check the data in the Sample data panel to ensure it has been read correctly and click Add, then Close the dialog.
Visualize the Data
- After adding the CSV file, you should now see two layers in the Layers panel: the OpenStreetMap basemap and the crime points layer (2019-02-surrey-street).
- Right-click the 2019-02-surrey-street layer and select Zoom to Layer to fit the crime points into the map view.
- You can zoom and pan around the map to explore the data. The crime points are dense, making it difficult to interpret crime concentration. This is where the heatmap comes in.

Step 2: Creating a Basic Heatmap

Now that we have the crime data loaded, we can visualize it using a heatmap to show areas with high concentrations of crime.

Open the Layer Styling Panel
- Select the 2019-02-surrey-street layer in the Layers panel and click the Open the Layer Styling Panel button (paintbrush icon) located above the Layers panel.
Change the Renderer to Heatmap
- In the Symbology tab of the Layer Styling panel, change the Renderer from Single Symbol to Heatmap. This will instantly convert the point data into a heatmap visualization, with higher densities appearing more prominently.
Adjust the Color Ramp
- The default color ramp for the heatmap is a grayscale, which isn’t ideal for highlighting density. In the Color ramp dropdown, select the Reds color ramp. This will display areas of high crime density in red and lower densities in lighter shades.
Set the Radius
- The Radius parameter determines how far each crime point influences the surrounding area. For this dataset, assume each crime has an influence up to 5 kilometers (5000 meters). Since the project’s CRS is in EPSG:3857 (meters), set the Radius to 5000 meters.
- The heatmap renderer in QGIS uses a Quartic kernel function by default, which spreads out the influence of points over the defined radius.
Adjust Opacity
- To make the heatmap blend better with the basemap, you can adjust its opacity. Scroll down to the Layer Rendering section at the bottom of the Layer Styling panel and set the Opacity to 60%. This will allow the basemap to be visible underneath the heatmap, providing context.

Step 3: Adding Weights to the Heatmap

In some cases, simply mapping point density is not enough. You may want to give different weights to each point based on the severity of the event. For example, violent crimes may have more influence on the heatmap than minor offenses. Let’s add a weight field to account for crime severity.

Open the Attribute Table
- Right-click the 2019-02-surrey-street layer and select Open Attribute Table.
- You will see a field called Crime type that categorizes the type of crime (e.g., Violence, Robbery, etc.).
Use the Field Calculator
- Close the Attribute Table and open the Field Calculator from the Attributes Toolbar (abacus icon).
- In the Field Calculator dialog, check Create virtual field. Name the new field weight and set the Output field type to Whole number (integer).
Define a Weight Expression
- We will use a conditional CASE statement to assign a weight to each crime type. More violent crimes should have higher weights. Enter the following expression:
```
CASE
WHEN "Crime type" LIKE 'Violence%' THEN 10
WHEN "Crime type" LIKE 'Criminal%' THEN 5
ELSE 1
END
```
- This expression assigns a weight of 10 to violent crimes, 5 to criminal damage, and 1 to less severe crimes. Click OK to create the new virtual field.
Apply the Weight Field to the Heatmap
- Go back to the Layer Styling panel. In the Heatmap settings, you will see an option called Weight points by.
- Select the newly created weight field from the dropdown. The heatmap will update to account for the weight of each crime point, giving more influence to severe crimes like violence.

Step 4: Advanced Heatmap Creation Using Kernel Density Estimation

The heatmap renderer is great for visualization, but if you need more control (e.g., custom kernels, dynamic radii), you can use QGIS’s Heatmap (Kernel Density Estimation) tool to generate a raster layer. This allows for advanced customization and can generate a permanent raster output.

Reproject the Crime Layer
- Before creating a heatmap raster, you need to ensure the crime points layer is in a projected coordinate system (CRS). Go to Processing > Toolbox.
- Search for Reproject layer and select Vector general > Reproject layer.
- In the Reproject layer dialog, set the Target CRS to EPSG:27700 OSGB 1936 / British National Grid (a projected CRS for the UK). Click Run.
Run the Heatmap Algorithm
- In the Processing Toolbox, search for Heatmap (Kernel Density Estimation) under Interpolation.
- In the Heatmap (Kernel Density Estimation) dialog:
  - Set the Reprojected layer as the input layer.
  - Set Radius to 5000 meters and Weight from field to weight.
  - Set Pixel size X and Pixel size Y to 50 meters for finer resolution.
  - Leave the Kernel shape as Quartic (default).
  - Click Run.
Style the Output Raster
- Once the heatmap is generated, a new raster layer called OUTPUT will be added to the Layers panel.
- The default rendering is in grayscale. To change it, open the Layer Styling panel for the OUTPUT layer.
- Change the Render type to Singleband pseudocolor and select the Reds color ramp to display the raster as a heatmap.
Add a Legend
- Unlike heatmaps created using the Heatmap renderer, this raster output has a legend by default. This is useful if you want to add the heatmap to a map layout for printing or presentation.

Conclusion

In this hands-on exercise, you learned how to create and style heatmaps in QGIS to visualize dense point data such as crime locations. You explored two methods: 1. Heatmap Renderer for quick visualization with interactive styling. 2. Kernel Density Estimation for creating more customizable and permanent raster outputs.

These techniques are widely applicable to a range of datasets, from crime mapping to environmental analysis, allowing you to interpret and present spatial patterns more effectively.

# The Evolution of Global Political Structures and Their Spatial Dimensions ## Introduction The evolution of global political structures is deeply intertwined with spatial dimensions, influencing the formation and transformation of nation-states, empires, and international organizations. Geography, borders, and territory have long been fundamental in shaping political power and governance. This chapter explores these spatial dynamics, offering a comprehensive analysis of how they have historically and contemporarily influenced global political structures, with a focus on the role of human population sizes through the ages. ## Historical Evolution of Population Sizes and Political Structures ### Population Growth and Political Structures Human population sizes have been a critical factor in the development of political structures throughout history. As populations grew, the need for organized governance and territorial control became more pronounced, leading to the establishment of complex political entities. - **Early Civilizations and Population Concentration**: The emergence of early civilizations, such as those in Mesopotamia, Egypt, and the Indus Valley, was closely linked to the concentration of populations in fertile regions. These areas supported larger populations, necessitating more sophisticated forms of governance. - **Empire Building and Population Dynamics**: The rise of empires, such as the Roman, Persian, and Ottoman Empires, was driven by the need to manage growing populations and access more resources. This led to the development of intricate administrative structures and the establishment of borders. - **Nation-States and Population Distribution**: The transition from empires to nation-states in the post-medieval period was influenced by changes in population distribution and density. The Peace of Westphalia in 1648 marked the beginning of the modern state system, where territorial sovereignty became a fundamental principle, often defined by natural borders that corresponded to population centers. ### The Impact of Colonialism and Global Governance Global political structures have undergone significant changes over time, driven by colonialism, economic interests, and ideological shifts. The legacy of colonial borders and the emergence of global governance systems like the United Nations and the European Union illustrate the profound impact of spatial and demographic considerations on political evolution. - **Colonialism and Its Aftermath**: Colonial powers imposed artificial spatial divisions on territories, often disregarding existing cultural and ethnic boundaries. These colonial borders have had lasting impacts, contributing to post-colonial conflicts and shaping the political landscapes of many nations today. - **Global Governance**: The formation of international organizations and treaties has increasingly transcended national borders, addressing global issues such as trade, security, and human rights. The establishment of the United Nations in 1945 and the European Union in 1993 are key examples of how political structures have evolved to manage international relations on a global scale. ## Mapping History: Analyzing Historical Administrative Data Using SUNGEO and GIS Tools Understanding the evolution of political structures requires analyzing historical administrative data, which is often complicated by shifts in borders and governance systems. The SUNGEO Historical GIS Boundaries dataset and Geographic Information Systems (GIS) provide powerful tools for addressing these challenges. ### Working with Historical GIS Boundaries Historical GIS boundaries allow researchers to analyze how administrative regions have changed over time. By using the SUNGEO package, researchers can download, visualize, and align historical boundaries with contemporary data, facilitating more accurate historical analyses. #### QGIS Steps for Mapping Historical Boundaries 1. **Load Historical and Contemporary Data into QGIS**: - Import the historical boundary dataset (`SHGIS.geojson`) into QGIS. - Import the contemporary boundary dataset for comparison. 2. **Visualize the Boundaries**: - Use the "Symbology" settings in QGIS to differentiate between historical and contemporary boundaries by color. - Overlay the two datasets to visually inspect how boundaries have shifted over time. 3. **Analyze Boundary Changes**: - Use the "Difference" tool in QGIS (found under Vector > Geoprocessing Tools) to identify areas where boundaries have changed. - Create maps showing the difference between historical and contemporary boundaries. #### R Code for Historical GIS Analysis Here is a practical exercise in R to work with historical GIS data: ```r # Install and load necessary packages install.packages("SUNGEO", dependencies = TRUE) install.packages("sf") install.packages("ggplot2") library(SUNGEO) library(sf) library(ggplot2) # Download and load historical GIS data download.file("https://example.com/path_to_SHGIS.geojson", destfile = "SHGIS.geojson") historical_boundaries <- st_read("SHGIS.geojson") # Visualize historical boundaries plot(st_geometry(historical_boundaries), main = "Historical Administrative Boundaries", col = "lightblue", border = "black") # Enhanced visualization using ggplot2 ggplot(data = historical_boundaries) + geom_sf(fill = "lightblue", color = "black") + theme_minimal() + labs(title = "Historical Administrative Boundaries", caption = "Source: SUNGEO Historical GIS") ``` ### Application: Mapping the Evolution of Political Boundaries Using the above tools, researchers can map the evolution of political boundaries over time, providing insights into how historical events, population movements, and governance changes have shaped contemporary political landscapes. ## Mapping Political Risks: Geolocated Political Data for Risk Assessment Geolocated political data has become increasingly vital for risk assessment in fields such as international relations, security studies, and humanitarian aid. By examining spatial patterns in political events, conflicts, and social movements, researchers and policymakers can anticipate and respond to emerging risks more effectively. ### Analyzing Geolocated Political Data Geolocated political data includes information on election results, conflict zones, and protest movements that can be geographically pinpointed. Tools like GIS enable the visualization and analysis of these spatial patterns, providing deeper insights into the dynamics of political risks. #### QGIS Steps for Mapping Political Risks 1. **Load Geolocated Data into QGIS**: - Import geolocated datasets (e.g., conflict zones, election results) into QGIS. 2. **Create Heatmaps**: - Use the "Heatmap" tool (found under Raster > Heatmap) to visualize concentrations of political events. 3. **Overlay Data Layers**: - Overlay different data layers (e.g., protest locations and election outcomes) to analyze correlations between political risks and geographic factors. #### R Code for Political Risk Analysis ```r # Load necessary packages library(sf) library(ggplot2) # Load geolocated political data (hypothetical example) political_data <- st_read("path_to_political_data.geojson") # Create a basic map of political events ggplot(data = political_data) + geom_sf(aes(fill = event_type), color = "black") + theme_minimal() + labs(title = "Geolocated Political Events", caption = "Source: Example Dataset") ``` ### Application: Populism and Economic Impact In studying the rise of populist leadership and its economic impact, spatial analysis can reveal correlations between political trends and regional economic performance. By mapping these trends, researchers can gain insights into the spatial dynamics of populism and its effects. ```r # Aligning economic and political data spatially using SUNGEO economic_data <- st_read("path_to_economic_data.geojson") populism_data <- st_read("path_to_populism_data.geojson") # Spatial join between economic data and populism data joined_data <- st_join(economic_data, populism_data, join = st_intersects) # Visualizing the relationship between populism and economic performance ggplot(data = joined_data) + geom_sf(aes(fill = economic_performance), color = "black") + facet_wrap(~populism, scales = "free") + theme_minimal() + labs(title = "Economic Performance under Populist Regimes", caption = "Source: Example Dataset") ``` ## Mapping Health: Spatial Dynamics of Pandemics Pandemics have always had significant spatial dimensions, spreading across regions with varying impacts depending on geographic, social, and political factors. Understanding pandemics from a spatial perspective is crucial for developing effective public health responses. ### Historical and Contemporary Pandemics - **Historical Pandemics**: The Black Death and the Spanish Flu are examples of pandemics that were shaped by geography. Mapping the spread of these diseases helps illustrate the role of trade routes, population density, and movement in pandemic dynamics. - **Contemporary Pandemics**: The COVID-19 pandemic highlighted the importance of global connectivity in disease spread. GIS tools were crucial in tracking the virus and informing public health strategies, allowing for targeted interventions and resource allocation. ### Spatial Analysis in Pandemic Response GIS and spatial data have become indispensable in managing pandemics. By mapping the spread of diseases, identifying hotspots, and analyzing the effectiveness of interventions, spatial analysis provides essential insights for public health planning and response. #### QGIS Steps for Pandemic Mapping 1. **Import Health Data into QGIS**: - Load datasets containing COVID-19 case numbers, vaccination rates, or other relevant health data. 2. **Create Time-Series Maps**: - Use QGIS’s "Temporal Controller" to animate the spread of disease over time. 3. **Analyze Hotspots**: - Use the "Cluster and Outlier Analysis" tool (found under Vector > Analysis Tools) to identify areas with high concentrations of cases. #### R Code for Pandemic Mapping ```r # Load necessary packages library(sf) library(ggplot2) # Load pandemic data (hypothetical example) pandemic_data <- st_read("path_to_pandemic_data.geojson") # Visualizing the spread of the pandemic over time ggplot(data = pandemic_data) + geom_sf(aes(fill = case_count), color = "black") + theme_minimal() + labs(title = "Pandemic Spread Over Time", fill = "Case Count", caption = "Source: Example Dataset") + facet_wrap(~date, scales = "free") + scale_fill_viridis_c() ``` ### Application: Pandemic Mapping Using EpiBibR EpiBibR is a powerful tool for pandemic mapping that integrates spatial data to track the dynamics of disease outbreaks. By using spatial analysis, researchers can assess the spread of diseases, identify vulnerable regions, and evaluate the effectiveness of public health interventions. #### Workflow for Using EpiBibR in R ```r # Install and load EpiBibR and other necessary packages install.packages("EpiBibR", dependencies = TRUE) library(EpiBibR) library(sf) library(ggplot2) # Load pandemic data into R pandemic_data <- st_read("path_to_pandemic_data.geojson") # Use EpiBibR to perform advanced spatial analysis # Example: Basic mapping of disease spread plot(pandemic_data["cases"], main = "Pandemic Spread", col = "red") # Advanced: Temporal analysis using time-series plots ggplot(data = pandemic_data) + geom_sf(aes(fill = cases), color = "black") + theme_minimal() + labs(title = "Pandemic Case Counts Over Time", fill = "Cases") + facet_wrap(~time_period, scales = "free") + scale_fill_gradient(low = "yellow", high = "red") ``` ### Using GIS to Improve Public Health Responses Spatial analysis using GIS allows public health officials to track the spread of pandemics in real-time, identify potential hotspots, and allocate resources more effectively. This approach is crucial in managing public health crises, ensuring that interventions are targeted and timely. ## Conclusion The spatial dimensions of global political structures, population dynamics, and public health are critical to understanding how power, governance, and societal outcomes are shaped by geography. This chapter has provided a comprehensive overview of how historical, political, and health-related phenomena are interconnected with spatial factors. By leveraging tools like QGIS and R, researchers and policymakers can analyze these spatial dimensions with precision, gaining insights that are essential for addressing complex global challenges. From mapping the evolution of political boundaries to assessing the spatial dynamics of pandemics and political risks, the integration of GIS and spatial analysis in research offers a powerful means to understand and navigate the complexities of our interconnected world. As the importance of location and spatial relationships continues to grow in global affairs, the ability to effectively utilize these tools will be indispensable in both academic research and practical applications. This comprehensive approach, merging historical analysis with modern spatial tools, provides a foundation for future research in understanding the evolving landscape of global politics, population dynamics, and public health. ## Exercise 1: Hands-On: Locating Nearest Facility with Origin-Destination Matrix in QGIS In this hands-on section, we will explore how to use QGIS to perform network-based analyses, specifically creating an **Origin-Destination (OD) Matrix**. This method is widely used in logistics, urban planning, and service optimization. By calculating the shortest path between origin and destination points, you can identify the closest facilities, such as finding the nearest warehouse to a customer or the nearest health facility to a given address. In this exercise, we will calculate the shortest travel distance from various addresses to the nearest mental health facilities in Washington, DC, using QGIS and the **QNEAT3** plugin. ### Objectives: - Use QGIS to create an Origin-Destination (OD) Matrix. - Extract random points from a dataset for analysis. - Run SQL queries on QGIS layers using virtual layers. - Analyze shortest paths between origin and destination points using a road network. #### Step 1: Data Setup and Plugin Installation 1. **Download the Required Data** - The District of Columbia provides open datasets for a variety of uses. For this tutorial, download the following shapefiles from the DC Open Data Catalog: - **Roadway Block** (road network data) - **Address Points** (representing addresses in the city) - **Community-Based Service Providers** (representing mental health facilities) - You can download a copy of the data directly from these links: - [Roadway_Block-shp.zip](https://example.com/Roadway_Block-shp.zip) - [Address_Points.zip](https://example.com/Address_Points.zip) - [Community_Based_Service_Provider.zip](https://example.com/Community_Based_Service_Provider.zip) 2. **Install the QNEAT3 Plugin** - QGIS provides the **QNEAT3** plugin, which enables network analysis, including the calculation of distance matrices. To install it: - Go to **Plugins** > **Manage and Install Plugins**. - In the search bar, type **QNEAT3** and select it from the list. - Click **Install** and close the dialog. #### Step 2: Loading and Filtering the Data 1. **Load the Community-Based Service Providers Layer** - In the **Browser Panel**, locate the **Community_Based_Service_Providers.shp** file from the downloaded data. - Drag the shapefile onto the canvas to load it. - This layer contains points representing service centers across the city. 2. **Filter for Adult Service Providers** - We only need to focus on centers that provide services for adults. To filter: - Right-click on the **Community_Based_Service_Providers** layer in the **Layers Panel** and select **Filter**. - In the **Query Builder** dialog, enter the following expression: ```r "PROVIDER_T" IN ('Adult','Adult & Child') ``` - Click **Run** to apply the filter. Only facilities providing services to adults will remain on the map. 3. **Load the Roadway Block and Address Points Layers** - Similarly, load the **Roadway_Block.shp** and **Address_Points.shp** layers by dragging them from the **Browser Panel** onto the canvas. - These layers represent the road network and addresses, respectively. 4. **Randomly Sample 1000 Address Points** - The **Address_Points** layer contains many points representing addresses throughout the city. To simplify the analysis, we will select a random subset of 1000 addresses: - Go to **Processing** > **Toolbox**. - Search for the **Random extract** algorithm (located under **Vector Selection**). - In the dialog, select **Address_Points** as the input layer, set the method to **Number of features**, and enter **1000** in the **Number/percentage of features** field. - Save the output as `address_point_subset.shp` in your working directory and click **Run**. 5. **Rename the Layers** - Rename the newly created random subset layer as **origin_points** by right-clicking on it in the Layers panel and selecting **Rename Layer**. - Similarly, rename the **Community_Based_Service_Providers** layer to **destination_points** for clarity in the upcoming steps. #### Step 3: Creating the Origin-Destination Matrix 1. **Open the QNEAT3 Distance Matrix Tool** - In the **Processing Toolbox**, search for the **OD Matrix from Layers as Line (m:n)** algorithm under the **QNEAT3** section. - This tool calculates the shortest distance between each origin point (address) and destination point (health facility) using the road network. 2. **Configure the Distance Matrix Algorithm** - In the **OD Matrix from Layers as Line** dialog: - Select **Roadway_Block** as the **Network layer** (this represents the road network). - Select **origin_points** as the **From-Points layer** and **OBJECTID** as the unique point ID. - Select **destination_points** as the **To-Points layer** and **OBJECTID** as the unique point ID. - Set the **Optimization criterion** to **Shortest Path** to calculate the shortest travel distance between origin and destination points. 3. **Handle Road Directions** - Many streets in Washington, DC, are one-way, so we need to specify the correct traffic directions: - Expand the **Advanced Parameters** section and select **SUMMARYDIR** as the **Direction field**. - Enter the following values: - **OB** for outbound (forward direction) - **IB** for inbound (backward direction) - **BD** for two-way streets (both directions) 4. **Run the Algorithm** - Click **Run** to generate the origin-destination matrix. The algorithm will calculate the shortest path between each origin and destination point, considering the road directions. - Once complete, a new table layer named **Output OD Matrix** will appear in the Layers panel. #### Step 4: Analyzing the Results 1. **Inspect the OD Matrix** - Right-click the **Output OD Matrix** layer in the **Layers panel** and select **Open Attribute Table**. - The table will contain 67,000 rows (1000 destination points for each of the 67 origin points), with the **total_cost** column representing the distance between each origin and destination point in meters. 2. **Find the Nearest Facility** - We want to identify the nearest service provider for each origin point. To do this, we will run a SQL query to find the facility with the shortest distance for each origin. - Go to **Processing** > **Toolbox** and search for the **Execute SQL** tool under **Vector general**. 3. **Create the SQL Query** - In the **SQL Query** dialog: - In **Additional input data sources**, select **Output OD Matrix**. - Click the **Summation** icon to enter the SQL query. - Enter the following query to find the minimum distance for each origin: ```r SELECT origin_id, destination_id, MIN(total_cost) AS shortest_distance, geometry FROM input1 GROUP BY origin_id ``` - Click **Run**. 4. **Review the Results** - A new layer called **SQL Output** will be added to the Layers panel. - This layer contains the shortest distances between each origin and its nearest facility. Right-click the layer and open the attribute table to see the results. - The **shortest_distance** field represents the minimum distance from each origin point to its nearest health facility. ### Conclusion In this exercise, you learned how to create an **Origin-Destination Matrix** in QGIS to locate the nearest facility using the shortest path. You explored how to: - Filter data to focus on relevant points of interest. - Randomly sample data for analysis. - Use the **QNEAT3** plugin to calculate shortest paths between origin and destination points. - Run SQL queries on virtual layers to refine the results. These techniques can be applied to various scenarios, from logistics and delivery optimization to emergency response planning and urban service analysis. ## Exercise 2: Mapping Political Risks Using QGIS In this hands-on section, we will explore how to create and visualize **heatmaps** using QGIS. Heatmaps are a powerful way to represent the density of point data, making them an ideal tool for visualizing phenomena such as crime hotspots, traffic incidents, or population density. In this exercise, we will work with crime location data for Surrey, UK, to create a heatmap and explore advanced styling options. ### Objectives: - Learn how to load CSV data and basemaps into QGIS. - Create and style a heatmap using QGIS’s heatmap renderer. - Incorporate weighted data into the heatmap to account for the severity of events. - Generate a raster heatmap using the **Kernel Density Estimation** algorithm for advanced customization. #### Step 1: Data Setup and Loading the Basemap 1. **Download the Crime Data** - The dataset we will use contains crime locations in Surrey, UK, available in CSV format from [data.police.uk](https://data.police.uk). You can download a copy directly from the following link: - **[Download the crime dataset](https://data.police.uk/)** or [**2019-02-surrey-street.csv**](https://data.police.uk/). - Unzip the downloaded archive to extract the CSV file. 2. **Add a Basemap** - In QGIS, from the **Browser** panel on the left, scroll down and find the **XYZ Tiles** section. - Expand it and drag the **OpenStreetMap** tile layer to the canvas. This will load a street map of the world as your basemap. 3. **Import the CSV Data** - Click the **Open Data Source Manager** button (a yellow database icon) on the toolbar. - In the dialog that appears, select the **Delimited Text** tab. - Click the **…** button next to **File name** and browse to select the `2019-02-surrey-street.csv` file you extracted earlier. - QGIS should automatically detect the **Longitude** and **Latitude** fields under the **Geometry Definition** section. Make sure the **Geometry CRS** is set to `EPSG:4326 - WGS 84`. - Check the data in the **Sample data** panel to ensure it has been read correctly and click **Add**, then **Close** the dialog. 4. **Visualize the Data** - After adding the CSV file, you should now see two layers in the **Layers panel**: the OpenStreetMap basemap and the crime points layer (`2019-02-surrey-street`). - Right-click the **2019-02-surrey-street** layer and select **Zoom to Layer** to fit the crime points into the map view. - You can zoom and pan around the map to explore the data. The crime points are dense, making it difficult to interpret crime concentration. This is where the heatmap comes in. #### Step 2: Creating a Basic Heatmap Now that we have the crime data loaded, we can visualize it using a heatmap to show areas with high concentrations of crime. 1. **Open the Layer Styling Panel** - Select the **2019-02-surrey-street** layer in the Layers panel and click the **Open the Layer Styling Panel** button (paintbrush icon) located above the Layers panel. 2. **Change the Renderer to Heatmap** - In the **Symbology** tab of the Layer Styling panel, change the **Renderer** from **Single Symbol** to **Heatmap**. This will instantly convert the point data into a heatmap visualization, with higher densities appearing more prominently. 3. **Adjust the Color Ramp** - The default color ramp for the heatmap is a grayscale, which isn’t ideal for highlighting density. In the **Color ramp** dropdown, select the **Reds** color ramp. This will display areas of high crime density in red and lower densities in lighter shades. 4. **Set the Radius** - The **Radius** parameter determines how far each crime point influences the surrounding area. For this dataset, assume each crime has an influence up to 5 kilometers (5000 meters). Since the project’s CRS is in **EPSG:3857** (meters), set the **Radius** to **5000 meters**. - The heatmap renderer in QGIS uses a **Quartic kernel function** by default, which spreads out the influence of points over the defined radius. 5. **Adjust Opacity** - To make the heatmap blend better with the basemap, you can adjust its opacity. Scroll down to the **Layer Rendering** section at the bottom of the Layer Styling panel and set the **Opacity** to **60%**. This will allow the basemap to be visible underneath the heatmap, providing context. #### Step 3: Adding Weights to the Heatmap In some cases, simply mapping point density is not enough. You may want to give different weights to each point based on the severity of the event. For example, violent crimes may have more influence on the heatmap than minor offenses. Let’s add a weight field to account for crime severity. 1. **Open the Attribute Table** - Right-click the **2019-02-surrey-street** layer and select **Open Attribute Table**. - You will see a field called `Crime type` that categorizes the type of crime (e.g., Violence, Robbery, etc.). 2. **Use the Field Calculator** - Close the Attribute Table and open the **Field Calculator** from the Attributes Toolbar (abacus icon). - In the **Field Calculator** dialog, check **Create virtual field**. Name the new field **weight** and set the **Output field type** to **Whole number (integer)**. 3. **Define a Weight Expression** - We will use a conditional **CASE** statement to assign a weight to each crime type. More violent crimes should have higher weights. Enter the following expression: ```r CASE WHEN "Crime type" LIKE 'Violence%' THEN 10 WHEN "Crime type" LIKE 'Criminal%' THEN 5 ELSE 1 END ``` - This expression assigns a weight of 10 to violent crimes, 5 to criminal damage, and 1 to less severe crimes. Click **OK** to create the new virtual field. 4. **Apply the Weight Field to the Heatmap** - Go back to the **Layer Styling panel**. In the Heatmap settings, you will see an option called **Weight points by**. - Select the newly created **weight** field from the dropdown. The heatmap will update to account for the weight of each crime point, giving more influence to severe crimes like violence. #### Step 4: Advanced Heatmap Creation Using Kernel Density Estimation The heatmap renderer is great for visualization, but if you need more control (e.g., custom kernels, dynamic radii), you can use QGIS’s **Heatmap (Kernel Density Estimation)** tool to generate a raster layer. This allows for advanced customization and can generate a permanent raster output. 1. **Reproject the Crime Layer** - Before creating a heatmap raster, you need to ensure the crime points layer is in a projected coordinate system (CRS). Go to **Processing** > **Toolbox**. - Search for **Reproject layer** and select **Vector general > Reproject layer**. - In the **Reproject layer** dialog, set the **Target CRS** to **EPSG:27700 OSGB 1936 / British National Grid** (a projected CRS for the UK). Click **Run**. 2. **Run the Heatmap Algorithm** - In the **Processing Toolbox**, search for **Heatmap (Kernel Density Estimation)** under **Interpolation**. - In the **Heatmap (Kernel Density Estimation)** dialog: - Set the **Reprojected** layer as the input layer. - Set **Radius** to **5000 meters** and **Weight from field** to **weight**. - Set **Pixel size X** and **Pixel size Y** to **50 meters** for finer resolution. - Leave the **Kernel shape** as **Quartic** (default). - Click **Run**. 3. **Style the Output Raster** - Once the heatmap is generated, a new raster layer called **OUTPUT** will be added to the Layers panel. - The default rendering is in grayscale. To change it, open the **Layer Styling panel** for the OUTPUT layer. - Change the **Render type** to **Singleband pseudocolor** and select the **Reds** color ramp to display the raster as a heatmap. 4. **Add a Legend** - Unlike heatmaps created using the Heatmap renderer, this raster output has a legend by default. This is useful if you want to add the heatmap to a map layout for printing or presentation. ### Conclusion In this hands-on exercise, you learned how to create and style heatmaps in QGIS to visualize dense point data such as crime locations. You explored two methods: 1. **Heatmap Renderer** for quick visualization with interactive styling. 2. **Kernel Density Estimation** for creating more customizable and permanent raster outputs. These techniques are widely applicable to a range of datasets, from crime mapping to environmental analysis, allowing you to interpret and present spatial patterns more effectively.