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README.Rmd
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README.Rmd
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---
title: "Urban Analytics: Supporting Materials"
output:
html_document:
keep_md: true
---
When writing the Urban Analytics book we made a deliberate choice not to embed code within the narrative of each chapter. The objective of the book was to be illustrative of a series of themes from this emerging field rather than the process of _doing_ urban analytics. We felt that by featuring code in the book would break up the flow of our discussion and illustrations. More practically, a second concern we have with books that heavily embed code is that as a learning resource these are difficult to manage in terms of errors, updates and sharing.
As such, we have written the book in an accessible style that is pitched at a wide audience that would include academics, professionals, undergraduate and postgraduates. We weave a range of real-world urban analytic applications throughout the text, framing these within a GIScience context. The nine chapters of the book cover those themes we think are most pertinent in contemporary urban analytics and include:
1. Questioning the city through urban analytics
2. Sensing the city
3. Urban Data Infrastructure
4. Visualizing the city
5. Cities and Context
6. Explaining the city
7. Generative urban systems
8. Cities as networks and flows
9. The future of urban analytics
## Supporting materials and curriculum
The chapters of the book cover a broad range of content around a set of themes, however, from a pedagogic perspective, they do not contain the introductory skills training that would be required to implement independent urban analytics. As such, we developed this supporting web resource to contain a wealth of materials that link to each of the book chapters where appropriate, and provide both PowerPoint and practical lab materials that could be used as the core of a module or short course on urban analytics.
We have designed these resources around 15 blocks of activity, which could be reconfigured as necessary, and include both PowerPoint slides and practical labs that are implemented within the statistical programming language R.
### Lectures with chapter links
Each [lecture](/Slides/) is linked to a book chapter. At the end of each book chapter we have also included some mini assessments that might form the basis of seminar questions, formative or summative assignments; alongside some further reading.
```{r echo=FALSE}
outline <- read.csv("overview_table.csv")
```
```{r echo=FALSE, warning= FALSE, message=FALSE}
library(knitr)
```
```{r echo=FALSE, results='asis'}
options(knitr.table.format = "html")
kable(outline[1:15,])
```
### Practical labs with chapter links
The practical labs are designed to take an average of two hours to complete, although, this will depend greatly on student prior experience and how much the student experiments with the code. The resources used within the practical are all public domain, and for many could be re-created for specific local contexts.
```{r echo=FALSE, results='asis'}
kable(outline[16:nrow(outline),],row.names = FALSE)
```
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