Assessment

Assessment for the course consists of two activities designed to develop both practical skills and analytical interpretation.

Assignment 1: Programmed Infographics Map

Type: Coursework
Weight: 40% of the final mark
Submission: Electronic submission only

In this assignment, students will produce a map using programming. The task is intended to assess the ability to work with data, develop a clear map design and communicate a spatial pattern or issue through a well-structured visual output. The map should demonstrate both technical competence and an effective overall narrative.

Assignment brief

Title: A Programmed Infographics Map: Traffic Injury Patterns in Montevideo
Software: The assessment should be completed in R

For this assignment, you will create a programmed map based on the Montevideo traffic injuries dataset used in the course. The brief is designed to draw on skills developed in the early chapters, especially spatial data wrangling, coordinate reference systems, point data handling, aggregation, and thematic map design. The aim is to move from raw event records to a clear and analytically defensible spatial visualisation.

The assignment will be evaluated on three main pillars:

Data processing
Map assemblage
Design and narrative

The data for the assignment should be drawn from the materials used in the course, especially:

data/montevideo-traffic-injuries-2022.csv
the supporting Montevideo context layers used in the course such as roads, coastline, or other local reference layers where appropriate

Your task is to produce one final programmed map that communicates a meaningful spatial pattern in the traffic injuries data. You may choose the specific representation, but it should be based on methods introduced in the course. For example, you might map event locations directly, aggregate them to a regular analytical geography, or visualise spatial concentration using a smoothed or density-based surface. Whatever choice you make, the map should be justified in relation to the data structure and the question you want the map to answer.

The main steps of the assignment are:

Import and inspect the raw traffic injuries data.
Convert the coordinate columns into a spatial object with an appropriate CRS.
Decide on a suitable analytical unit. For example, you may work with crash events rather than person-level records, or aggregate events into a grid or other surface.
Create a final map that communicates the spatial distribution or concentration of traffic injuries in Montevideo.
Use layout, legend, labels, colour, and contextual layers carefully so that the map is readable and analytically clear.

You should submit an HTML version of a .qmd document with integrated R code. The submission should include:

your annotated code
your final map output
a short written discussion addressing the analytical choices behind the map

In your written discussion, you should address questions such as:

What is the spatial unit represented in your final map?
Which CRS did you use and why?
What does the map show about the spatial pattern of traffic injuries in Montevideo?
Why is your chosen mapping strategy appropriate for this dataset and question?

The goal of the assignment is not only to produce an attractive map, but to show that you can move from raw spatial data to a defensible visual argument using the workflow and concepts introduced in the course.

Marking criteria

The programmed map will be assessed using the following broad criteria:

Data processing: appropriate data import, cleaning, transformation, and handling of the spatial structure of the dataset
Methodological appropriateness: a suitable choice of spatial unit, CRS, aggregation or surface method, and map representation for the question being asked
Map assemblage: effective integration of layers, legend, labels, layout, and supporting context
Design and narrative: a map that communicates a clear analytical message rather than only displaying data
Interpretation and justification: a concise written discussion that explains the main analytical choices and what the final map shows
Presentation quality: a polished and readable submission with well-organised code and a coherent final visual output

High-quality submissions will not simply map the raw points without reflection. They will justify the analytical design, use visual elements carefully, and show how the chosen representation helps communicate a meaningful spatial pattern in the Montevideo traffic injuries data.

Assignment 2: Computational Essay

Type: Coursework
Weight: 60% of the final mark
Submission: Electronic submission only

A computational essay is an essay whose narrative is supported by code and computational results included within the essay itself. This piece of assessment is equivalent to 4,000 words in total weight. However, this total includes not only ordinary text, but also code, maps, figures and tables. For this reason, the format requirements are more specific than for a conventional essay.

Format requirements

Maximum of 1,000 words of ordinary text
References and code do not count towards the word count
Students must answer the specified questions within the narrative
The questions should be incorporated into a broader analysis
Up to five maps or figures
Up to one table

What a computational essay includes

A computational essay combines three elements:

Ordinary text
Computer code
Computer output such as maps, tables and figures

These elements should work together as part of one coherent analysis. The aim is not only to run techniques, but also to explain what is being done, justify key choices and interpret the substantive meaning of the results.

Assignment brief

Title: A Computational Essay: Spatial Structure and Inequality in Uruguay
Software: The assessment should be completed in R

For this assignment, you will produce a computational essay based on the Uruguay materials used throughout the course. The purpose is to show that you can move from a spatial question to a coherent analytical argument using reproducible code, appropriate visualisation and careful interpretation. This assessment asks you to build a broader piece of analysis that integrates data, method and narrative.

The assignment is designed to draw on the later parts of the course in particular, including origin-destination data, areal indicators, spatial dependence and spatial heterogeneity. Your essay should demonstrate that you can connect a substantive question about Uruguay to the structure of the available data and to the methods introduced in the course.

The assignment will be evaluated on three main pillars:

Data preparation and reproducibility
Analytical design and implementation
Interpretation, critical reflection and communication

The data for the assignment should be drawn primarily from the course repository, especially:

data/fb-activity-spaces/activity_space_distributions_20260209_t_to_z.csv
data/risk-assessment-indicators/URY_ADM2_access.csv
data/risk-assessment-indicators/URY_ADM2_demographics.csv
data/risk-assessment-indicators/URY_ADM2_facilities.csv
data/risk-assessment-indicators/URY_ADM2_rural_population.csv
data/derived/ury_adm2_population_worldpop_2020.csv
the Uruguay ADM2 boundary layers provided in the repository

You may also draw selectively on other course materials where they help answer your question, but the essay should remain clearly anchored in the datasets and analytical workflows covered in the Uruguay course.

Your computational essay should have two parts.

Marking criteria

The computational essay will be assessed using the following broad criteria:

Reproducible workflow: clear and well-organised code, appropriate data preparation, and a transparent analytical sequence
Methodological appropriateness: suitable choice of spatial units, CRS, visualisation, and analytical method for the question being asked
Analytical depth: effective use of one advanced course pathway, with evidence of understanding rather than mechanical application
Interpretation and argument: a clear narrative that explains what the results show, how they answer the question, and why they matter
Critical reflection: explicit discussion of data quality, assumptions, limitations, and the scope of the conclusions that can be drawn
Presentation quality: a polished computational essay with readable maps, figures, table output where relevant, and concise academic writing

High-quality submissions will not simply reproduce chapter code. They will adapt the course workflow to a focused question, justify key analytical decisions, and use code, output, and narrative together to make a coherent argument.

Part 1: Common core

All students should complete the following common elements:

Define a clear spatial question about Uruguay that can be answered with the course data.
Import, inspect and prepare the relevant data in a reproducible workflow.
State the spatial unit or units used in the analysis and justify the CRS where projection matters.
Produce descriptive maps or figures that establish the main geography of your problem.
Discuss the opportunities and limitations of the data, including what the data represent and what they do not capture.

In this first part, you should show that you understand the structure of the dataset you are using. For example, you may need to explain the difference between point events, origin-destination flows, and area-based indicators, or clarify whether your variables refer to counts, shares or interaction intensities.

Part 2: Choose one analytical pathway

For the second part of the essay, choose one of the following three options and develop it into the main analytical contribution of your essay.

Option A: Spatial interaction in Uruguay

Use the Meta activity spaces data to analyse origin-destination structure across Uruguay. Your essay should move from preparing the OD data to interpreting what the observed flow pattern suggests about territorial structure and distance.

Possible elements include:

constructing an ADM2 origin-destination table
measuring distances between origins and destinations
mapping selected strong flows
analysing how interaction strength changes with distance
fitting and interpreting a simple gravity-style interaction model

Questions to address within your narrative may include:

What do the strongest observed flows reveal about the geography of everyday interaction in Uruguay?
How important is distance in structuring movement between places?
What are the strengths and limitations of using activity-space data as a representation of spatial interaction?
What does your model or descriptive analysis suggest about territorial concentration, connectivity or regional structure?

Option B: Spatial dependence in Uruguay

Use the Uruguay risk assessment indicators to examine the geography of hospital accessibility across ADM2 areas. Your essay should move from descriptive mapping to formal assessment of spatial clustering and, where appropriate, spatial modelling.

Possible elements include:

constructing the hospital_access_30min_share outcome
mapping the outcome and selected covariates
creating a neighbourhood structure and spatial weights matrix
assessing global and local spatial autocorrelation
comparing a non-spatial regression with a spatial regression approach

Questions to address within your narrative may include:

What does the spatial distribution of hospital accessibility reveal about territorial inequality in Uruguay?
Is the pattern spatially clustered, and how do you know?
How does accounting for spatial dependence change your interpretation of the relationship between accessibility and its covariates?
What are the policy implications of the spatial pattern you identify?

Option C: Spatial heterogeneity in Uruguay

Use the Uruguay risk assessment indicators to examine whether relationships vary across space rather than remaining constant across the whole country. Your essay should use the later course material to move beyond one national average relationship.

Possible elements include:

constructing the same accessibility outcome and covariates used in Chapters 05 and 06
defining meaningful regional contexts or regimes
estimating and comparing a baseline model, a spatial fixed-effects model, and a spatial regimes model
mapping and interpreting spatially varying intercepts or coefficients
discussing what these differences reveal about uneven territorial processes

Questions to address within your narrative may include:

Do the same covariates appear to operate similarly across Uruguay, or do relationships vary by region?
What do fixed effects or regimes add that a single national model misses?
Which places appear most distinctive once observed covariates are taken into account?
Why does spatial heterogeneity matter for interpretation or policy?

The main steps of the assignment are:

Formulate a focused question that can be answered with one of the course analytical pathways.
Import and prepare the relevant Uruguay data.
Produce a concise set of well-designed maps, figures, and at most one table.
Implement one advanced analytical workflow from the course.
Interpret your results critically and explain the limitations of your approach.

You should submit an HTML version of a .qmd document with integrated R code. The submission should include:

your annotated code
your maps, figures, and optional table
a coherent written discussion that answers the relevant analytical questions through the narrative

In your written discussion, you should address questions such as:

What is the spatial question of the essay and why is the chosen dataset appropriate for it?
What spatial unit or interaction structure is being analysed?
Which CRS and analytical choices were used, and why?
What does your analysis show about spatial structure, inequality, or regional variation in Uruguay?
What are the main limitations of the data and method you selected?

The goal of the assignment is not only to apply a technique correctly, but to show that you can use spatial data and reproducible computation to make a clear, defensible argument about a real territorial pattern in Uruguay.

# Assessment {.unnumbered} Assessment for the course consists of two activities designed to develop both practical skills and analytical interpretation. ## Assignment 1: Programmed Infographics Map - Type: Coursework - Weight: 40% of the final mark - Submission: Electronic submission only In this assignment, students will produce a map using programming. The task is intended to assess the ability to work with data, develop a clear map design and communicate a spatial pattern or issue through a well-structured visual output. The map should demonstrate both technical competence and an effective overall narrative. ### Assignment brief **Title:** A Programmed Infographics Map: Traffic Injury Patterns in Montevideo **Software:** The assessment should be completed in `R` For this assignment, you will create a programmed map based on the Montevideo traffic injuries dataset used in the course. The brief is designed to draw on skills developed in the early chapters, especially spatial data wrangling, coordinate reference systems, point data handling, aggregation, and thematic map design. The aim is to move from raw event records to a clear and analytically defensible spatial visualisation. The assignment will be evaluated on three main pillars: - Data processing - Map assemblage - Design and narrative The data for the assignment should be drawn from the materials used in the course, especially: - `data/montevideo-traffic-injuries-2022.csv` - the supporting Montevideo context layers used in the course such as roads, coastline, or other local reference layers where appropriate Your task is to produce one final programmed map that communicates a meaningful spatial pattern in the traffic injuries data. You may choose the specific representation, but it should be based on methods introduced in the course. For example, you might map event locations directly, aggregate them to a regular analytical geography, or visualise spatial concentration using a smoothed or density-based surface. Whatever choice you make, the map should be justified in relation to the data structure and the question you want the map to answer. The main steps of the assignment are: 1. Import and inspect the raw traffic injuries data. 2. Convert the coordinate columns into a spatial object with an appropriate CRS. 3. Decide on a suitable analytical unit. For example, you may work with crash events rather than person-level records, or aggregate events into a grid or other surface. 4. Create a final map that communicates the spatial distribution or concentration of traffic injuries in Montevideo. 5. Use layout, legend, labels, colour, and contextual layers carefully so that the map is readable and analytically clear. You should submit an HTML version of a `.qmd` document with integrated `R` code. The submission should include: - your annotated code - your final map output - a short written discussion addressing the analytical choices behind the map In your written discussion, you should address questions such as: 1. What is the spatial unit represented in your final map? 2. Which CRS did you use and why? 3. What does the map show about the spatial pattern of traffic injuries in Montevideo? 4. Why is your chosen mapping strategy appropriate for this dataset and question? The goal of the assignment is not only to produce an attractive map, but to show that you can move from raw spatial data to a defensible visual argument using the workflow and concepts introduced in the course. ### Marking criteria The programmed map will be assessed using the following broad criteria: - **Data processing:** appropriate data import, cleaning, transformation, and handling of the spatial structure of the dataset - **Methodological appropriateness:** a suitable choice of spatial unit, CRS, aggregation or surface method, and map representation for the question being asked - **Map assemblage:** effective integration of layers, legend, labels, layout, and supporting context - **Design and narrative:** a map that communicates a clear analytical message rather than only displaying data - **Interpretation and justification:** a concise written discussion that explains the main analytical choices and what the final map shows - **Presentation quality:** a polished and readable submission with well-organised code and a coherent final visual output High-quality submissions will not simply map the raw points without reflection. They will justify the analytical design, use visual elements carefully, and show how the chosen representation helps communicate a meaningful spatial pattern in the Montevideo traffic injuries data. ## Assignment 2: Computational Essay - Type: Coursework - Weight: 60% of the final mark - Submission: Electronic submission only A computational essay is an essay whose narrative is supported by code and computational results included within the essay itself. This piece of assessment is equivalent to 4,000 words in total weight. However, this total includes not only ordinary text, but also code, maps, figures and tables. For this reason, the format requirements are more specific than for a conventional essay. ### Format requirements - Maximum of 1,000 words of ordinary text - References and code do not count towards the word count - Students must answer the specified questions within the narrative - The questions should be incorporated into a broader analysis - Up to five maps or figures - Up to one table ### What a computational essay includes A computational essay combines three elements: 1. Ordinary text 2. Computer code 3. Computer output such as maps, tables and figures These elements should work together as part of one coherent analysis. The aim is not only to run techniques, but also to explain what is being done, justify key choices and interpret the substantive meaning of the results. ### Assignment brief **Title:** A Computational Essay: Spatial Structure and Inequality in Uruguay **Software:** The assessment should be completed in `R` For this assignment, you will produce a computational essay based on the Uruguay materials used throughout the course. The purpose is to show that you can move from a spatial question to a coherent analytical argument using reproducible code, appropriate visualisation and careful interpretation. This assessment asks you to build a broader piece of analysis that integrates data, method and narrative. The assignment is designed to draw on the later parts of the course in particular, including origin-destination data, areal indicators, spatial dependence and spatial heterogeneity. Your essay should demonstrate that you can connect a substantive question about Uruguay to the structure of the available data and to the methods introduced in the course. The assignment will be evaluated on three main pillars: - Data preparation and reproducibility - Analytical design and implementation - Interpretation, critical reflection and communication The data for the assignment should be drawn primarily from the course repository, especially: - `data/fb-activity-spaces/activity_space_distributions_20260209_t_to_z.csv` - `data/risk-assessment-indicators/URY_ADM2_access.csv` - `data/risk-assessment-indicators/URY_ADM2_demographics.csv` - `data/risk-assessment-indicators/URY_ADM2_facilities.csv` - `data/risk-assessment-indicators/URY_ADM2_rural_population.csv` - `data/derived/ury_adm2_population_worldpop_2020.csv` - the Uruguay ADM2 boundary layers provided in the repository You may also draw selectively on other course materials where they help answer your question, but the essay should remain clearly anchored in the datasets and analytical workflows covered in the Uruguay course. Your computational essay should have two parts. ### Marking criteria The computational essay will be assessed using the following broad criteria: - **Reproducible workflow:** clear and well-organised code, appropriate data preparation, and a transparent analytical sequence - **Methodological appropriateness:** suitable choice of spatial units, CRS, visualisation, and analytical method for the question being asked - **Analytical depth:** effective use of one advanced course pathway, with evidence of understanding rather than mechanical application - **Interpretation and argument:** a clear narrative that explains what the results show, how they answer the question, and why they matter - **Critical reflection:** explicit discussion of data quality, assumptions, limitations, and the scope of the conclusions that can be drawn - **Presentation quality:** a polished computational essay with readable maps, figures, table output where relevant, and concise academic writing High-quality submissions will not simply reproduce chapter code. They will adapt the course workflow to a focused question, justify key analytical decisions, and use code, output, and narrative together to make a coherent argument. ### Part 1: Common core All students should complete the following common elements: 1. Define a clear spatial question about Uruguay that can be answered with the course data. 2. Import, inspect and prepare the relevant data in a reproducible workflow. 3. State the spatial unit or units used in the analysis and justify the CRS where projection matters. 4. Produce descriptive maps or figures that establish the main geography of your problem. 5. Discuss the opportunities and limitations of the data, including what the data represent and what they do not capture. In this first part, you should show that you understand the structure of the dataset you are using. For example, you may need to explain the difference between point events, origin-destination flows, and area-based indicators, or clarify whether your variables refer to counts, shares or interaction intensities. ### Part 2: Choose one analytical pathway For the second part of the essay, choose **one** of the following three options and develop it into the main analytical contribution of your essay. #### Option A: Spatial interaction in Uruguay Use the Meta activity spaces data to analyse origin-destination structure across Uruguay. Your essay should move from preparing the OD data to interpreting what the observed flow pattern suggests about territorial structure and distance. Possible elements include: - constructing an ADM2 origin-destination table - measuring distances between origins and destinations - mapping selected strong flows - analysing how interaction strength changes with distance - fitting and interpreting a simple gravity-style interaction model Questions to address within your narrative may include: 1. What do the strongest observed flows reveal about the geography of everyday interaction in Uruguay? 2. How important is distance in structuring movement between places? 3. What are the strengths and limitations of using activity-space data as a representation of spatial interaction? 4. What does your model or descriptive analysis suggest about territorial concentration, connectivity or regional structure? #### Option B: Spatial dependence in Uruguay Use the Uruguay risk assessment indicators to examine the geography of hospital accessibility across ADM2 areas. Your essay should move from descriptive mapping to formal assessment of spatial clustering and, where appropriate, spatial modelling. Possible elements include: - constructing the `hospital_access_30min_share` outcome - mapping the outcome and selected covariates - creating a neighbourhood structure and spatial weights matrix - assessing global and local spatial autocorrelation - comparing a non-spatial regression with a spatial regression approach Questions to address within your narrative may include: 1. What does the spatial distribution of hospital accessibility reveal about territorial inequality in Uruguay? 2. Is the pattern spatially clustered, and how do you know? 3. How does accounting for spatial dependence change your interpretation of the relationship between accessibility and its covariates? 4. What are the policy implications of the spatial pattern you identify? #### Option C: Spatial heterogeneity in Uruguay Use the Uruguay risk assessment indicators to examine whether relationships vary across space rather than remaining constant across the whole country. Your essay should use the later course material to move beyond one national average relationship. Possible elements include: - constructing the same accessibility outcome and covariates used in Chapters `05` and `06` - defining meaningful regional contexts or regimes - estimating and comparing a baseline model, a spatial fixed-effects model, and a spatial regimes model - mapping and interpreting spatially varying intercepts or coefficients - discussing what these differences reveal about uneven territorial processes Questions to address within your narrative may include: 1. Do the same covariates appear to operate similarly across Uruguay, or do relationships vary by region? 2. What do fixed effects or regimes add that a single national model misses? 3. Which places appear most distinctive once observed covariates are taken into account? 4. Why does spatial heterogeneity matter for interpretation or policy? The main steps of the assignment are: 1. Formulate a focused question that can be answered with one of the course analytical pathways. 2. Import and prepare the relevant Uruguay data. 3. Produce a concise set of well-designed maps, figures, and at most one table. 4. Implement one advanced analytical workflow from the course. 5. Interpret your results critically and explain the limitations of your approach. You should submit an HTML version of a `.qmd` document with integrated `R` code. The submission should include: - your annotated code - your maps, figures, and optional table - a coherent written discussion that answers the relevant analytical questions through the narrative In your written discussion, you should address questions such as: 1. What is the spatial question of the essay and why is the chosen dataset appropriate for it? 2. What spatial unit or interaction structure is being analysed? 3. Which CRS and analytical choices were used, and why? 4. What does your analysis show about spatial structure, inequality, or regional variation in Uruguay? 5. What are the main limitations of the data and method you selected? The goal of the assignment is not only to apply a technique correctly, but to show that you can use spatial data and reproducible computation to make a clear, defensible argument about a real territorial pattern in Uruguay.