Exercises

Introduction to the Chapter on Geopolitical Risks and Geospatial Data Science

In an increasingly interconnected world, the ability to understand and respond to geopolitical risks is critical for policymakers. The U.S. Department of State, recognizing the strategic importance of geospatial data, has integrated advanced geospatial data science into its decision-making processes to address various dimensions of global affairs, including economic stability, political dynamics, social issues, and military resilience. This chapter explores how geospatial data science is leveraged by the U.S. government to map and analyze these geopolitical risks, offering insights into the practical applications of this technology in the context of international relations.

The U.S. Department of State’s Geospatial Data Strategy (GDS) is a prime example of how the integration of geospatial technologies has been institutionalized to enhance data-driven policy decisions. The GDS outlines strategic goals aimed at increasing data sharing and accessibility, ensuring the reliability of geospatial data, and developing a sustained geospatial workforce within the Department. These efforts are aligned with broader federal initiatives, including the National Spatial Data Infrastructure (NSDI) and the Federal Data Strategy (FDS), which emphasize the importance of geospatial data as a component of national infrastructure and policy-making【5†source】.

Synthesis of U.S. Positions on Geospatial Data Usage

The U.S. Department of State employs geospatial data across a broad spectrum of activities to support its mission. Geospatial data are utilized for decision-making based on geospatial analysis, managing mission workflows, visualizing geographic contexts, and monitoring resources. For instance, the Department’s Bureau of Intelligence and Research (INR) uses geospatial data to assess global instability and predict outcomes in conflict zones. The Bureau of Diplomatic Security (DS) relies on geospatial technologies to monitor and secure U.S. diplomatic missions worldwide. The implementation of the Geospatial Data Act (GDA) of 2018 further underscores the Department’s commitment to improving geospatial data sharing, interoperability, and governance【5†source】.

The strategic goals outlined in the GDS focus on four main objectives: enhancing geospatial data sharing and accessibility, ensuring reliable geospatial tools, promoting geospatial data awareness to sustain a capable workforce, and establishing a robust reporting structure to comply with statutory requirements. These goals are essential for the Department to maintain its leadership in using geospatial data for diplomacy and national security.

Proposed Exercise for Students

Title: Mapping Geopolitical Risks Using QGIS and R

Objective: To explore how geospatial data can be used to identify and analyze geopolitical risks by mapping U.S. diplomatic activities, conflict zones, and areas of economic interest.

Exercise Instructions:

Data Collection: Access the State Department’s geospatial data available at State Department Data. Download datasets related to diplomatic missions, conflict zones, and economic activities.
Data Preparation: Using QGIS, import the datasets and prepare the data layers. Clean the data to ensure all locations are correctly geocoded.
Data Visualization: Create maps that highlight U.S. diplomatic missions and their proximity to conflict zones. Use different symbology to represent various levels of geopolitical risks.
Spatial Analysis: Use R to perform a spatial analysis that overlays economic data with conflict zones and diplomatic missions. Analyze the correlation between economic interests and the presence of U.S. diplomatic missions in high-risk areas.
Scenario Simulation: Students are encouraged to simulate potential geopolitical scenarios (e.g., an emerging conflict in a region of economic interest) and visualize the impact on U.S. diplomatic strategies using both QGIS and R.
Presentation: Students will present their findings, focusing on how geospatial data science can inform U.S. foreign policy and mitigate geopolitical risks.

Note: The exercise should be approached with creativity and a sense of exploration. Students may also consider incorporating humor into their presentations, such as visualizing a fictitious “diplomatic crisis” involving a non-existent country, to make the exercise more engaging.

This introduction and exercise are designed to immerse students in the real-world applications of geospatial data science, emphasizing the strategic role of this technology in U.S. diplomacy.

Step-by-Step Guide: Mapping Geopolitical Risks Using QGIS and R

Objective

This guide will help you map geopolitical risks by analyzing U.S. diplomatic activities, conflict zones, and areas of economic interest using QGIS and R. By following these steps, you will gain hands-on experience in geospatial data science, relevant for understanding the practical applications of these tools in assessing global risks.

Step 1: Data Collection

Access the Data:

Visit the State Department’s Geospatial Data page.
Download relevant datasets such as U.S. diplomatic missions, global conflict zones, and economic activities.

Prepare Your Workspace:

Create a project folder on your computer where you will store all the downloaded data and QGIS project files.

Step 2: Data Preparation in QGIS

Open QGIS:

Launch QGIS on your computer.
Create a new project and save it in your project folder.

Load the Datasets:

Click on Layer > Add Layer > Add Vector Layer.
Browse to the location of your downloaded datasets and load them into QGIS.

Check and Clean the Data:

Verify that all the datasets are correctly loaded. Ensure that the geographic locations are correctly mapped.
If any dataset needs cleaning (e.g., missing coordinates), use the Processing Toolbox in QGIS to correct errors.

Reproject the Data (if necessary):

To ensure all layers are in the same coordinate reference system (CRS), right-click on each layer and select Set CRS > Set Layer CRS.
Reproject layers if needed to match your project’s CRS using Vector > Data Management Tools > Reproject Layer.

Step 3: Data Visualization in QGIS

Symbolize the Layers:

Right-click on a layer (e.g., diplomatic missions) and select Properties.
Under the Symbology tab, choose an appropriate style (e.g., different colors or symbols to represent different types of missions or risk levels).

Create Thematic Maps:

Create thematic maps by categorizing conflict zones by intensity or economic activities by sector.
Use the Categorized or Graduated symbology options under Properties > Symbology to achieve this.

Add Labels:

For better map readability, label key features such as capital cities, high-risk zones, or important economic areas.
Right-click the layer, select Properties > Labels, and configure the labeling options.

Step 4: Spatial Analysis in R

Set Up R and RStudio:

Install R and RStudio if not already installed.
Install necessary packages: sf, rgdal, sp, ggplot2, dplyr, tmap.
Use the following command in R to install these packages:

install.packages(c("sf", "rgdal", "sp", "ggplot2", "dplyr", "tmap"))

Import Data into R:

Load your datasets into R using the st_read() function from the sf package.

library(sf)
missions <- st_read("path_to_your_data/diplomatic_missions.shp")
conflicts <- st_read("path_to_your_data/conflict_zones.shp")
economics <- st_read("path_to_your_data/economic_activities.shp")

Perform Spatial Overlay:

Use spatial overlay techniques to combine datasets, such as intersecting diplomatic missions with conflict zones to identify high-risk locations.

high_risk_missions <- st_intersection(missions, conflicts)

Analyze Correlations:

Use dplyr to analyze correlations between economic activities and conflict zones.

library(dplyr)
analysis <- economics %>% 
                st_join(conflicts) %>% 
                group_by(sector) %>% 
                summarize(conflict_count = n())

Visualize Results:

Use ggplot2 or tmap to visualize the results of your spatial analysis.

     library(ggplot2)
     ggplot(high_risk_missions) +
       geom_sf() +
       ggtitle("High-Risk Diplomatic Missions")

Step 5: Scenario Simulation

Hypothetical Scenario:

Create a hypothetical scenario where a new conflict emerges in an economically significant area.
Modify the conflict zones layer in QGIS to include this new area and visualize the impact.

Analyze Impact:

In R, re-run the spatial analysis to see how the new conflict affects the correlation between economic activities and conflict zones.

Presentation of Findings:

Prepare a presentation summarizing your findings. Include maps and analysis results to show how geospatial data can inform U.S. foreign policy decisions.

Step 6: Presentation and Reflection

Prepare Your Maps and Visuals:

Export your maps from QGIS as high-resolution images.
Save your R visualizations as images using ggsave().

Create a Presentation:

Combine your maps and analysis into a PowerPoint or other presentation format.
Present your findings, discussing the role of geospatial data science in understanding and mitigating geopolitical risks.

Reflect on the Exercise:

Discuss with your peers the challenges and insights gained from using QGIS and R in this context. Consider the real-world implications of your analysis.

References

U.S. Department of State. (2021). Geospatial Data Strategy. Retrieved from DoS Geospatial Data Strategy
R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from https://www.R-project.org/

This guide provides a structured approach to using QGIS and R for geospatial analysis, allowing students to explore the practical applications of geospatial data science in the field of international relations and policy-making.

# Exercises {.unnumbered} ## Introduction to the Chapter on Geopolitical Risks and Geospatial Data Science In an increasingly interconnected world, the ability to understand and respond to geopolitical risks is critical for policymakers. The U.S. Department of State, recognizing the strategic importance of geospatial data, has integrated advanced geospatial data science into its decision-making processes to address various dimensions of global affairs, including economic stability, political dynamics, social issues, and military resilience. This chapter explores how geospatial data science is leveraged by the U.S. government to map and analyze these geopolitical risks, offering insights into the practical applications of this technology in the context of international relations. The U.S. Department of State’s Geospatial Data Strategy (GDS) is a prime example of how the integration of geospatial technologies has been institutionalized to enhance data-driven policy decisions. The GDS outlines strategic goals aimed at increasing data sharing and accessibility, ensuring the reliability of geospatial data, and developing a sustained geospatial workforce within the Department. These efforts are aligned with broader federal initiatives, including the National Spatial Data Infrastructure (NSDI) and the Federal Data Strategy (FDS), which emphasize the importance of geospatial data as a component of national infrastructure and policy-making【5†source】. ## Synthesis of U.S. Positions on Geospatial Data Usage The U.S. Department of State employs geospatial data across a broad spectrum of activities to support its mission. Geospatial data are utilized for decision-making based on geospatial analysis, managing mission workflows, visualizing geographic contexts, and monitoring resources. For instance, the Department’s Bureau of Intelligence and Research (INR) uses geospatial data to assess global instability and predict outcomes in conflict zones. The Bureau of Diplomatic Security (DS) relies on geospatial technologies to monitor and secure U.S. diplomatic missions worldwide. The implementation of the Geospatial Data Act (GDA) of 2018 further underscores the Department’s commitment to improving geospatial data sharing, interoperability, and governance【5†source】. The strategic goals outlined in the GDS focus on four main objectives: enhancing geospatial data sharing and accessibility, ensuring reliable geospatial tools, promoting geospatial data awareness to sustain a capable workforce, and establishing a robust reporting structure to comply with statutory requirements. These goals are essential for the Department to maintain its leadership in using geospatial data for diplomacy and national security. ## Proposed Exercise for Students Title: Mapping Geopolitical Risks Using QGIS and R Objective: To explore how geospatial data can be used to identify and analyze geopolitical risks by mapping U.S. diplomatic activities, conflict zones, and areas of economic interest. Exercise Instructions: 1. Data Collection: Access the State Department’s geospatial data available at [State Department Data](https://www.state.gov/data/). Download datasets related to diplomatic missions, conflict zones, and economic activities. 2. Data Preparation: Using QGIS, import the datasets and prepare the data layers. Clean the data to ensure all locations are correctly geocoded. 3. Data Visualization: Create maps that highlight U.S. diplomatic missions and their proximity to conflict zones. Use different symbology to represent various levels of geopolitical risks. 4. Spatial Analysis: Use R to perform a spatial analysis that overlays economic data with conflict zones and diplomatic missions. Analyze the correlation between economic interests and the presence of U.S. diplomatic missions in high-risk areas. 5. Scenario Simulation: Students are encouraged to simulate potential geopolitical scenarios (e.g., an emerging conflict in a region of economic interest) and visualize the impact on U.S. diplomatic strategies using both QGIS and R. 6. Presentation: Students will present their findings, focusing on how geospatial data science can inform U.S. foreign policy and mitigate geopolitical risks. Note: The exercise should be approached with creativity and a sense of exploration. Students may also consider incorporating humor into their presentations, such as visualizing a fictitious "diplomatic crisis" involving a non-existent country, to make the exercise more engaging. This introduction and exercise are designed to immerse students in the real-world applications of geospatial data science, emphasizing the strategic role of this technology in U.S. diplomacy. ## Step-by-Step Guide: Mapping Geopolitical Risks Using QGIS and R ### Objective This guide will help you map geopolitical risks by analyzing U.S. diplomatic activities, conflict zones, and areas of economic interest using QGIS and R. By following these steps, you will gain hands-on experience in geospatial data science, relevant for understanding the practical applications of these tools in assessing global risks. ### Step 1: Data Collection 1. Access the Data: - Visit the [State Department’s Geospatial Data](https://www.state.gov/data/) page. - Download relevant datasets such as U.S. diplomatic missions, global conflict zones, and economic activities. 2. Prepare Your Workspace: - Create a project folder on your computer where you will store all the downloaded data and QGIS project files. ### Step 2: Data Preparation in QGIS 1. Open QGIS: - Launch QGIS on your computer. - Create a new project and save it in your project folder. 2. Load the Datasets: - Click on Layer > Add Layer > Add Vector Layer. - Browse to the location of your downloaded datasets and load them into QGIS. 3. Check and Clean the Data: - Verify that all the datasets are correctly loaded. Ensure that the geographic locations are correctly mapped. - If any dataset needs cleaning (e.g., missing coordinates), use the Processing Toolbox in QGIS to correct errors. 4. Reproject the Data (if necessary): - To ensure all layers are in the same coordinate reference system (CRS), right-click on each layer and select Set CRS > Set Layer CRS. - Reproject layers if needed to match your project's CRS using Vector > Data Management Tools > Reproject Layer. ### Step 3: Data Visualization in QGIS 1. Symbolize the Layers: - Right-click on a layer (e.g., diplomatic missions) and select Properties. - Under the Symbology tab, choose an appropriate style (e.g., different colors or symbols to represent different types of missions or risk levels). 2. Create Thematic Maps: - Create thematic maps by categorizing conflict zones by intensity or economic activities by sector. - Use the Categorized or Graduated symbology options under Properties > Symbology to achieve this. 3. Add Labels: - For better map readability, label key features such as capital cities, high-risk zones, or important economic areas. - Right-click the layer, select Properties > Labels, and configure the labeling options. ### Step 4: Spatial Analysis in R 1. Set Up R and RStudio: - Install R and RStudio if not already installed. - Install necessary packages: `sf`, `rgdal`, `sp`, `ggplot2`, `dplyr`, `tmap`. - Use the following command in R to install these packages: ```{r, eval = FALSE} install.packages(c("sf", "rgdal", "sp", "ggplot2", "dplyr", "tmap")) ``` 2. Import Data into R: - Load your datasets into R using the `st_read()` function from the `sf` package. ```{r, eval = FALSE} library(sf) missions <- st_read("path_to_your_data/diplomatic_missions.shp") conflicts <- st_read("path_to_your_data/conflict_zones.shp") economics <- st_read("path_to_your_data/economic_activities.shp") ``` 3. Perform Spatial Overlay: - Use spatial overlay techniques to combine datasets, such as intersecting diplomatic missions with conflict zones to identify high-risk locations. ```{r, eval = FALSE} high_risk_missions <- st_intersection(missions, conflicts) ``` 4. Analyze Correlations: - Use `dplyr` to analyze correlations between economic activities and conflict zones. ```{r, eval = FALSE} library(dplyr) analysis <- economics %>% st_join(conflicts) %>% group_by(sector) %>% summarize(conflict_count = n()) ``` 5. Visualize Results: - Use `ggplot2` or `tmap` to visualize the results of your spatial analysis. ```{r, eval = FALSE} library(ggplot2) ggplot(high_risk_missions) + geom_sf() + ggtitle("High-Risk Diplomatic Missions") ``` ### Step 5: Scenario Simulation 1. Hypothetical Scenario: - Create a hypothetical scenario where a new conflict emerges in an economically significant area. - Modify the conflict zones layer in QGIS to include this new area and visualize the impact. 2. Analyze Impact: - In R, re-run the spatial analysis to see how the new conflict affects the correlation between economic activities and conflict zones. 3. Presentation of Findings: - Prepare a presentation summarizing your findings. Include maps and analysis results to show how geospatial data can inform U.S. foreign policy decisions. ### Step 6: Presentation and Reflection 1. Prepare Your Maps and Visuals: - Export your maps from QGIS as high-resolution images. - Save your R visualizations as images using `ggsave()`. 2. Create a Presentation: - Combine your maps and analysis into a PowerPoint or other presentation format. - Present your findings, discussing the role of geospatial data science in understanding and mitigating geopolitical risks. 3. Reflect on the Exercise: - Discuss with your peers the challenges and insights gained from using QGIS and R in this context. Consider the real-world implications of your analysis. ### References - U.S. Department of State. (2021). *Geospatial Data Strategy*. Retrieved from [DoS Geospatial Data Strategy](https://www.state.gov/data/) - R Core Team. (2024). *R: A language and environment for statistical computing*. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from [https://www.R-project.org/](https://www.R-project.org/) This guide provides a structured approach to using QGIS and R for geospatial analysis, allowing students to explore the practical applications of geospatial data science in the field of international relations and policy-making.