# People and Resources Links

This is my general repository of resources and people I find myself using or repeatedly pointing people towards.

# Urban Soundscapes

Urban Soundscape Studies pull from a wide range of disciplines, some less known than others. When trying to describe these influences to people, I find myself consistently referring to the same people and references as *examplars*^{1} for these various approaches. Mostly these are the sort of references I give people who ask me for an introduction to the different branches of urban soundscape research. Many of these researchers have also appeared on my podcast, *The Rest Is Just Noise* so check out their interviews there as well!

## General Resources

*Soundscapes: Humans and Their Acoustic Environment*, eds. Schulte-Fortkamp, Fiebig, Sisneros, Popper, & Fay (2023).

## Soundscape Engineering

**ME!**Of course, all of my works and publications can be found here. For my broader concepts, recommendations, and tools for soundscape engineering I would point people to the following specifically:

## Landscape Architecture

- Dr Gunnar Cerwen, Landscape Archictect at Swedish University of Agricultural Sciences.
- Dr Usue Ruiz Arana, Landscape Architect at Newcastle University

## History and Sociology

- Dr Edda Bild
*Considering Sound in Planning and Designing Public Spaces: A review of theory and applications and a proposed framework for integrating research and practice*, Journal of Planning Literature (2016). DOI: 10.1177/0885412216662001.*Sound as City Maker: Developing participatory collaborative process to work with sound as an urban resource. The case of Mr Visserplein (Amsterdam, NL)*. The Bloomsbury Handbook of Sonic Methodologies (2020). ISBN: 9781501338779*The Rest Is Just Noise:*Sonic Appropriation and the UK Policing Bill

*The Soundscape of Modernity*, Emily Thompson (2004).

# Statistics

- Statistical Modeling, Causal Inference, and Social Science, blog from Andrew Gelman, mostly focussed on Bayesian and MLM methods.

## Multilevel Modelling

*Multilevel (Hierarchical) Modeling: What it can and cannot do*. Gelman (2006). DOI: 10.1198/004017005000000661

## Generative Additive Models

## Teaching Statistics

- Common statistical tests are linear models (or: how to teach stats), blog post by Jonas Kristoffer Lindeløv
- Statistical Thinking for the 21st Century, a great new textbook by Russel A. Poldrack which I have used as the primary resource for my new undergrad statistics course.
- Statistical analysis: (1) Plotting the data, (2) Constructing and fitting models, (3) Plotting data along with fitted models, (4) Further modeling and data collection and Any graph should contain the seeds of its own destruction, blog posts from Andrew Gelman on the statistical modeling workflow

## Footnotes

Note: These really are just examples I tend to go to. I certainly don’t pretend this is every influential reference in the field or even necessarily who I consider better than any other researchers. In fact I will mostly highlight lesser known people that don’t tend to be the first points of entry for the field (i.e. I probably won’t include Schafer, Jian Kang, or Schult-Fortkamp since there’s just too much from them to include). I also apologise to any scholar who feels their work has been miscategorised.↩︎