Lecture 06

class: center, middle

# Lecture 06: Geospatial Data Sciences

# and Economic Spatial Models

## <img src="figs/bse_primary_logo.png" style="width: 35%" /> Bruno Conte

## 11-12/Feb/2026

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# Geospatial Data and Spatial Models: Schedule

1. ~~Introduction to (spatial) data and programming in `R`~~ &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; **[08/Jan/2026]**

2. ~~Week 2-4: Vector spatial data~~ &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; **[14 - 29/Jan/2026]**

3. Week 5-7: Raster spatial data + (basic) interactive tools &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; **[05 - 19/Feb/2026]**
    - ~~Week 5: Raster basics and unary operations using `sf` and `terra`~~
    - Week 6: Vector-raster operations
    - Week 7: Interactive tools with `leaflet` and APIs

4. Week 8-10: Spatial models and applications with data &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; **[25/Feb - 12 Mar/2026]**

5. 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;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; **[27/Mar/2026]**

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# Main references for this class

1. Lovelace, R., Nowosad, J. and Muenchow, J., 2019. <g>**Geocomputation with R.**</g> Chapman and Hall/CRC.
 - Chapters 2.3, 3.3, 4.3, 5.3, and 6

2. Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446

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

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# Raster basics: loading, creating, and operations with rasters

.pull-left[

- ~~Creating and~~ **loading rasters** with `terra` (external files)

- ~~<r>Unary</r> operations:~~
 - ~~Cropping~~
 - ~~Vectorization~~
 
]
.pull-right[

- <g>Vector-raster</g> operations:
 - Data extraction
 - Zonal statistics
 - Rasterize
 - Distances over networks using rasters

]

Requires **additional libraries** on top of `sf`

1. `terra`: contains most of the <r>**raster-related functions**</r>

2. `exactextractr`: performs high-performance <y>zonal statistics</y>

3. `gdistance`: used to calculate <g>distances over raster</g>

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