Dr Jacob Macdonald

My research focuses broadly around the application and methods of urban and environmental economics and data science. I am particularly interested in the interplay and spillovers of natural (environmental) urban amenities, the built environment and neighbourhood dynamics. This work emphasizes quantitative methods and leveraging big, novel, and geographic data.

I have a background in spatial statistics, econometrics, data visualization, machine learning and capturing measures of the urban environment through remote sensing and secondary administrative data sources. I’m a broad champion of open data/ software, open (and accessible) science communication, and making use of increasingly available and new sources of data.

My current work looks to better understand how the built urban environment and amenities in a local area can influence broader economic, socio-demographic or environmental processes. This falls generally along the following streams.

1) Measuring and Valuing Urban Amenities and Spillovers:

This area looks at how to best capture, measure and incorporate features of the urban environment and amenities into spatial statistics and models. I am particularly interested in using quasi-experimental policy evaluation for valuing the impact of urban green and blue amenities and their spillover effects on local hazards like flooding or pollution risks.

2) Spatial and Temporal Patterns of Local Housing, Employment and Retail:

Using big, geographic data sources can help to better understand detailed variations and similarities in the overall economic vitality and homogeneity of markets across urban areas. I’ve worked extensively with geographic housing, employment and retail data to better understand spatial patterns in local economic and consumer behaviour, identifying, mapping and delineating small area neighbourhoods and urban zones.

3) Patterns of Human Activity and Interaction in the Urban Area:

As new forms of granular location data over time capture high detailed patterns of mobility and urban movement, a wide range of work can explore how the local population interact with the built and urban environment. Mobility patterns and spatio-temporal urban data not only helps inform on the relative attractiveness of certain spaces (e.g. parks and open spaces), but also can help in better understanding how our behaviour influences dynamics like congestion or pollution.