Lecture 01

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# Lecture 01: Spatial Data

## Theory and Tools (a.k.a. GIS Tools Lab.)

### <img src="figs/unibo_logo_white.png" style="width: 15%" /> Bruno Conte

### 18/Sep/2023

---

# Spatial data in economics: this course

- Introduce students to conceptual and practical aspects of **spatial data**
 - What is spatial (geographical) data?
 - How is it used in **research in economics**?
 - Which tools (i.e. computer systems/languages) do we need to work with it?
 
- Main goal: concepts + tools = practice with real-world data
 - Concepts: types and formats of spatial data
 - Tools: programming in ``R`` and ``RStudio``
 
- Course's **main philosophy**: a course by an economist working with spatial data
 - Rather than a course by a spatial data's specialist!
 
---

# Spatial data in economics: this course

This course is about how we, (in principle) **economists**, can use spatial data to empirically answer reserch questions of our interest.

.pull-left[
**You will learn**
- What is spatial data and its applications in economic research

- Basic `R` programming

- Most common spatial data operations

- Introductory (spatial) data visualization
]

.pull-right[
**You will not learn**
- All state-of-art GIS tools available in `R`

- To write an efficient `R` code*

- To handle big data*

- To solve every possible problem
]

.footnote[
[*] This is up to you.
]

---

# Spatial data in economics: this course

## Good references

1. Donaldson, D. and Storeygard, A., 2016. The view from above: Applications of satellite data in economics. *Journal of Economic Perspectives*, *30(4)*, pp.171-98.

2. Lovelace, R., Nowosad, J. and Muenchow, J., 2019. Geocomputation with R. Chapman and Hall/CRC.

3. Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009

4. Wickham, H. and Grolemund, G., 2016. R for data science: import, tidy, transform, visualize, and model data. " O'Reilly Media, Inc.".

---

# Spatial data in economics: schedule

1. Introduction to (spatial) data and programming in `R` &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [18.Sep.2023]
    - Introduction to spatial data and examples in economics
    - Basic `R` programming: set up and practice

2. Spatial data basics: vector data + assignment &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [21.Sep.2023]

3. Basic operations with vector data + assignment &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [25.Sep.2023]

4. Geometry operations and miscelanea + follow-up &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [28.Sep.2023]

5. Raster data and operations + assignment &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [02.Oct.2023]

6. Take-home exam &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [03.Nov.2023]

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# Spatial data in economics: evaluation

1. Class participation (10%)

2. Practical assignments (3 x 10%, in teams)

3. Take-home exam (60%, `pdf` by email):
 - Research idea: spatial data + economics = **research question**
 
 - Replication of tasks: data + tools = empirical motivation
 
 - Make sure that you register to it (on almaesami)!

- Any questions?

---

# Spatial data in economics: logistics

- Classes: every Tue from 17(:15)-20:00 pm
    - 1 hour theory + 15' break + 1 hour practice (hands-in)

- Course material: [webpage](https://brunoconteleite.github.io/02-gis-unibo/)

- Office hours: Tue from 16:00-17h00 (write me an email!)
    
- Potentially mixed backgroud: **be patient**!

## Hands-in sessions:

- Your own computer + `RStudio`:
  - We will set it up together!

- Any questions?

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# Getting started: what is Spatial Data?

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# Europe in 1789 (before the French Revolution)

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background-image: url(https://www.edmaps.com/europe_1812_excelsior_d.jpg)
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# Europe in 1812 (before the French Invasion of Russia)

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background-image: url(https://www.edmaps.com/europe_1815_excelsior_d.jpg)
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# Europe in 1815 (after the Congress of Vienna)

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background-image: url(https://hsat.space/wp-content/uploads/2020/08/brazil_planet_-6.35-53.55-768x512-1.jpg)
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# Satellite picture of fires (and deforestation) in the Brazilian Amazon

???
Source: https://hsat.space/satellites-deforestation/

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background-image: url(https://maaproject.org/wp-content/uploads/2021/01/maaproject.org-maap-132-amazon-deforestation-hotspots-2020-HS2-PFL-Amz-Biog2020-GLAD-Confirm-2-5-10-v3-Eng.jpg)
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# Deforestation in the Brazilian Amazon

???
Source: https://maaproject.org/2021/amazon-hotspots-2020/

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background-image: url(https://www.researchgate.net/publication/343842128/figure/fig1/AS:928229292511232@1598318517654/A-comparison-of-the-inter-and-intra-urban-variability-of-slums-Image-a-shows-a-typical.ppm)
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# Urban slums in India

???
Source: https://www.researchgate.net/figure/A-comparison-of-the-inter-and-intra-urban-variability-of-slums-Image-a-shows-a-typical_fig1_343842128

---

# What is Spatial Data?

- Data/information that has a geographical attribute
 - **Much more** than coordinates on a standard dataset
 
 - Polygons, areas, distances, height, overlaying, intersections, ...

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- Common aspect: **unstructured data** (i.e. unconventional data format)

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<img src="https://previews.123rf.com/images/in8finity/in8finity1506/in8finity150600031/41436580-earth-globe-icons-set-.jpg" style="width: 40%" />
]

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- Our goal: manipulate it into the **structure** required by research

]

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# What is GIS?

- GIS = Geographic Information Systems
 - (old) Systems used to manipulate/process spatial data (**1980's**)
 
 - 1990's: rise of user-friendly, **desktop softwares** (ArcGIS, QGIS)
 
 - Data Science revolution: full integration of GIS tools into data-processing pipelines; i.e. computer routines that process (potentially spatial) data in modern languages (e.g. `R`)
 
- **Examples:**
 - Firm processing purchases across branches
 - Is revenue larger in branches *closer to public transportation?*
 
 - HR firm allocating seasonal workers across plants
 - Choose workers based on residence (reduce commuting time)?

---

# Why not GIS in desktop, user-friendly, softwares?

Require human interaction (e.g. clicking, moving files) to structure spatial data.

.pull-left[
.center[**GIS in 1980's:**

<img src="https://upload.wikimedia.org/wikipedia/commons/4/42/SYMAP_-_LAB-LOG_1980.png" style="width: 80%" />
]
]

.pull-right[
.center[**ArcGIS/QGIS:**]

<img src="https://i.imgur.com/nuSANEH.jpg" style="width: 120%" />
]

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# How is Spatial Data used in Economics?

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# Spatial Data in Economics

- **Motivation:** research questions that requires structuring spatial data.
 - Spatial data = unstructured
 
 - GIS tools: manipulating spatial data to the required structure
 
- Applications in economic research:
 - Cholera in London (Snow, 1856)
 
 - Colonial institutions and development in Peru (Dell, 2010)
 
 - Railroads and welfare in India (Donaldson, 2018)
 
 - Climate change and urbanization in Africa (Henderson et al., 2017)

---

# Application 01: John Snow's Cholera Maps in Soho (London)

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.center[
<img src="https://www.researchgate.net/profile/Maaruf-Lawal/publication/333521032/figure/fig1/AS:764738510278658@1559339277494/John-Snow-map-of-cholera-death-in-London-Gilbert-EW-1958-There-are-three-important.png" style="width: 90%" />
]
]

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- **Cholera outbreak** in mid 19th century

- Former theory: transmission by air

- John Snow's hypothesis: germ-contaminated water
 - Different rates between locations with different water suppliers
 
 - Higher rates for those supplied by (polluted) Thames River
 
- Snow's finding: revolution on public sanitation

]

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# Application 02: Long-term consequences of the Mita (colonial) system in Peru

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.center[
<img src="figs/mita01.png" style="width: 100%" />
]
]

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- Spanish empire required **forced labor** to work on silver mines (Potosí)

- Workers from high lands (Mita regions): resistent to the harsh mine conditions

- Mita boundaries: regions that provided more/less conscripts (discontinuously!)
 
- **Dell's findings:** long-lasting development differences
 - Economic channels: land ownership inequality, less public services, ...

]

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# Application 02: Long-term consequences of the Mita (colonial) system in Peru

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.center[
<img src="figs/mita02.png" style="width: 80%" />
]
]

.pull-right[
- Spanish empire required **forced labor** to work on silver mines (Potosí)

- Workers from high lands (Mita regions): resistent to the harsh mine conditions

]

---

# Application 03: Transportation integration and welfare in India

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.center[
<img src="figs/donaldson.png" style="width: 100%" />
]
]

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- Vast **expansion of railroad network** in British colonial India

- Standard trade theory: welfare gains from market integration
 - Lack of evidence within countries

- **Donaldson's findings:** improved trade conditions increased welfare
 - Integrated remote areas (reduced price gaps, more trade flows)
 
 - Welfare gains (real income) from intraregional trade

]

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# Application 04: Climate change and urbanization in Africa

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.center[
<img src="figs/henderson01.png" style="width: 100%" />
]
]

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- **Increased dryness in Africa** (worse conditions for agriculture)

- Question: do agents in affected rural regions migrate to cities?

- **Henderson et al.'s findings:** depends on industry composition of cities
 - Increased size (**nightlights**) in manufacturing, exporting cities
 
 - Opposite evidence for market towns (service providers to agriculture)
 
 - Importance of structural change!
]

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# Application 04: Climate change and urbanization in Africa

.pull-left[
.center[
<img src="figs/henderson02.png" style="width: 100%" />
]
]

.pull-right[
- **Increased dryness in Africa** (worse conditions for agriculture)

- Question: do agents in affected rural regions migrate to cities?

]

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# Application 04: Climate change and urbanization in Africa

.pull-left[
.center[
<img src="figs/henderson03.png" style="width: 100%" />
]
]

.pull-right[
- **Increased dryness in Africa** (worse conditions for agriculture)

- Question: do agents in affected rural regions migrate to cities?

]

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# How to work with spatial data in R?

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# Working with data (including spatial) in R

- What is `R`? 
 - Computer language for statistical computing and graphics
 
 - Open source, free access
 
 - Developers' community (CRAN)
 - Development of **libraries** (packages) for specific applications
 
- `RStudio`: integrated development environment (IDE)
 - User-friendlier environment to work with `R`
 
---

# Working with data (including spatial) in R

- Data work in `R`: one of its many capabilities
 - Producing documents, slides, or webpages
 
- **Our approach:**
 1. Basic programming and data work in `R` `\(\rightarrow\)` **content of today!**
 
 2. Working with spatial data in `R` (GIS tools)
 
- Important: focus on basics
 - Introduction of basic concepts/tools rather than a presentation of **all possible** functions
 
 - Every GIS application is a challenge on its own
 - The goal is to introduce the basics so that you can apply (and improve) them on your own application!

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# R Basics

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# Basics of programming and data work in R

Open `01_class01.R` on your own computer, where we will cover the following topics. The subsequent slides here are for reference only.

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- **Concepts covered:**
 1. `R` basics: environment, main elements (vectors, lists, `data.frame`), libraries
 
 2. Basic data wrangling with ``dplyr``
 - Filtering, mutating, merging
 
 3. Data visualization with `ggplot2`
]

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- **Setting up `R` (or in `RStudio`)**:

```r
# # Install packages (only first time)
*# install.packages('data.table')
*# install.packages('tidyverse')

# Load them:
library(data.table)
library(dplyr)
```
Note: warning messages are OK!

]

---

# Basics of programming and data work in R (1/3)

- `R` is versatile working environment
  - Can handle **different** elements (e.g. datasets, images, texts) contempotaneously
  
- Setting the **local** environment: working directory

```r
getwd() # tells you the current wd
```

```
## [1] "/Users/brunoconteleite/Dropbox/Teaching/02-gis-unibo"
```

- Types of elements in `R` environment:
  - Vectors, `data.frame()`, `list()`, among (many) others
  
  - To check (or clean) current environment: `ls()` (or `rm()`)

---

# Basics of programming and data work in R (2/3)

- **Data wrangling:** manipulating raw data with `dplyr`
 - Creating new variables, filtering datasets, arranging, merging, reshaping
 
- Pipe syntax: uses `%>%` operator. Example if merging datasets:

```r
df <- merge.data.table(a,b,by = 'Month') # is equivalent to:
df <- a %>% 
 left_join(b,by = 'Month')
```

- Same reasoning with many other `dplyr` data-wrangling functions; e.g. `mutate()`, `filter()`, `select()`, `summarise()`, `arrange()`

- Check wiki [here](https://dplyr.tidyverse.org/)

---

# Basics of programming and data work in R (3/3)

- **Data visualization** in `R` with `ggplot()`. Syntax that maps data `\(\rightarrow\)` geometry `\(\rightarrow\)` visuals

.pull-left[

```r
library(ggplot2)
try( # ignore this
p <- ggplot(data = data) +
 geom_GEOM(mapping = aes(MAPPINGS)) +
 THEME()
)
# Example
p <- ggplot(data = airquality) +
 geom_point(mapping = aes(Wind,Temp, color = Month)) +
 theme_bw()
```
- Check wiki [here](https://ggplot2.tidyverse.org/)
]

.pull-right[
.center[
<img src="00_class01_files/figure-html/unnamed-chunk-5-1.png" width="75%" />
]
]

---

# Hands-in: your turn! (1/2)

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- Distribution (histogram) of CO2 uptake across plants in US/Canada

- Distinguish plants by state (Quebec/Mississipi)

- Extra: play with different `theme()` parameters of `ggplot()`

- Use the `datasets::CO2` data!

]

.pull-right[
![](00_class01_files/figure-html/unnamed-chunk-6-1.png)

]

---

# Hands-in: your turn! (2/2)

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- Icome vs. Murder rates across US states (scatter plot). Use `state.x77` dataset

- Distinguish between high/low density states
  - High density = (Population/Area) > median: use `mutate()`

- Extra: additional geom layer with non-linear relationship? Use `geom_smooth()`

- Can you remove outliers (i.e. states with Income higher than 6,000)? Use `filter()`

]

.pull-right[
![](00_class01_files/figure-html/unnamed-chunk-7-1.png)

]

---
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# Hands-in: your turn! (2/2)

.pull-left[
- Icome vs. Murder rates across US states (scatter plot). Use `state.x77` dataset

- Distinguish between high/low density states
  - High density = (Population/Area) > median: use `mutate()`

- Extra: additional geom layer with non-linear relationship? Use `geom_smooth()`

- Can you remove outliers (i.e. states with Income higher than 6,000)? Use `filter()`

]

.pull-right[
![](00_class01_files/figure-html/unnamed-chunk-8-1.png)

]

---
count: false

# Hands-in: your turn! (2/2)

.pull-left[
- Icome vs. Murder rates across US states (scatter plot). Use `state.x77` dataset

- Distinguish between high/low density states
  - High density = (Population/Area) > median: use `mutate()`

- Extra: additional geom layer with non-linear relationship? Use `geom_smooth()`

- Can you remove outliers (i.e. states with Income higher than 6,000)? Use `filter()`

]

.pull-right[
![](00_class01_files/figure-html/unnamed-chunk-9-1.png)

]

---

# References

- Dell, M., 2010. The persistent effects of Peru's mining mita. *Econometrica*, *78(6)*, pp.1863-1903.

- Donaldson, D., 2018. Railroads of the Raj: Estimating the impact of transportation infrastructure. *American Economic Review*, *108(4-5)*, pp.899-934.

- Henderson, J.V., Storeygard, A. and Deichmann, U., 2017. Has climate change driven urbanization in Africa?. *Journal of development economics*, *124*, pp.60-82.

- Snow, J., 1856. On the mode of communication of cholera. *Edinburgh medical journal*, *1(7)*, p.668.