Home » Case Study

GOLIATH: Geographies of Lifestyle, Activity, Transport and Health (Case Study)

Time lapse photo causing horizontal light lines above a road and in front of a lit up office block (night time)

GOLIATH: Geographies of Lifestyle, Activity, Transport and Health
(Case Study)

Consumer data can provide insight in to a wide range of human activity, but there is a trade-off between privacy and utility of the data.

Project overview

Consumer data collected by commercial providers have huge potential for a range of research purposes but can be challenging to access as they are often held in secure environments. Secure handling of these datasets is crucial, as consumer data contains sensitive attributes (e.g. address) or commercially sensitive data (e.g. they have been purchased or contain licenced information). This project provides a proof of concept for creating enhanced and aggregated versions of consumer datasets for research purposes, and a dashboard for exploring those data.

Data and methods

Taking securely held consumer datasets within the Consumer Data Research Centre (CDRC), the objective of the project was to produce non-disclosive and aggregated versions of the data whilst maintaining the unique characteristics and value of those data. An R Shiny app visualising the aggregated data has been developed to showcase the utility of non-disclosive datasets for research purposes. Based on a randomised sample of Whenfresh/Zoopla consumer data, key matrices such as median price and affordability are calculated for different property types at the Middle Layer Super Output Areas (MSOA) level. Additionally, open data is used to calculate further metrics, for example, the attractiveness of an area based on Census flow data. The next steps include improving the efficiency, loading and updating times of the R Shiny app so that it can be populated with additional datasets.

Key findings

Using existing data, especially anonymised and aggregated consumer data, this research project can be seen as a proof of concept for an ‘alternative’ or ‘big data’ census. Different data types, e.g. time series, static, and origin-destination flow data, have successfully been combined and can be explored by the user in a dashboard (Figure 1).

Figure 1 – Screenshot of GOLIATH dashboard

Value of the research

The prototype R Shiny app forms the basis for further work in providing a dashboard for exploring local area statistics. Moving forward, other consumer data could be included as part of GOLIATH, for example, transport and lifestyle datasets. Utilising consumer data in addition to traditional census counts contributes to efforts to create an ‘alternative’ or ‘big data’ census.


  • Devised methods for the aggregation and calculation of metrics for secure consumer data
  • Developed a prototype R Shiny App for the visualisation of spatially disaggregated information

Research theme

Urban analytics


Maike Gatzlaff
LIDA Data Scientist Intern

Dr Nik Lomax
Co-Director of the Consumer Data Research Centre

Professor Mark Birkin
Co-Director of the Leeds Institute for Data Analytics

Dr Will James
Research Fellow, University of Leeds


The Consumer Data Research Centre


The data for this research have been provided by the Consumer Data Research Centre, an ESRC Data Investment, under project ID CDRC [Project Number], ES/L011840/1; ES/L011891/1.

Measuring Ambient Populations during COVID-19 (Case Study)

Graph showing footfall data results

Measuring Ambient Populations during COVID-19 in Leeds City Centre
(Case Study)

The COVID-19 pandemic led to lockdowns being implemented all over the world, including in the UK.  The aims of the project were to investigate relevant data sources for modelling the ambient population of Leeds City Centre during COVID-19 and analysing the impacts that lockdown policies had on urban footfall.  The research builds on previous work undertaken with Leeds City Council by intersecting key dates from the English lockdowns and integrating these into machine learning models to assess the importance of different aspects of lockdowns. It also predicts what “business as usual” may have been like had there been no pandemic.

Analysis notebooks and scripts can be accessed at https://github.com/tbalbone31

Data and methods

Leeds City Council have been collecting footfall data for more than a decade. The data were wrangled and aggregated to create a history going back to 2008.  These data were then analysed alongside key lockdown dates to determine where trends in urban footfall intersected, raising questions about what aspects of these policies might have had the most impact.  The data cover a relatively small geographical area of Leeds City Centre and only reflect pedestrian traffic going past the locations identified by the cameras.  There are issues with data quality, such as potential double counting, periods of time with missing data and inconsistent file formatting, however it covers a large temporal scale and many problems can be worked around.

Google COVID-19 Community Mobility data was analysed as a potential alternative data source to the Council data.  It shows changes in mobility from a baseline for six different destinations (see the website for more details).  The smallest relevant spatial coverage is the Leeds City Region.  This was considered too large to isolate any changes impacting the city centre, making comparison of trends difficult.

The Council footfall data were resampled to show daily counts on which analysis was then conducted.  Visual analysis was undertaken to identify footfall trends over the course of the pandemic against key dates pertaining to the implementation and lifting of certain COVID-19 restrictions. These key dates were decided from research into when major legislation came into force or government announcements about restrictions were made.  The questions generated from initial analysis were then explored by creating a series of machine learning models using Random Forest Regression in the Python SciKit Learn package. 

The first model included a series of input variables to represent different aspects of society that had restrictions placed on them alongside other external conditions (such as weather, school/bank holidays, day of week, etc).  Variable importance was used to identify what (if any) aspects of lockdown might be significant in predicting future changes in footfall. The second model omitted any lockdown related inputs and was designed to make predictions on what “business as usual” might have been like had the pandemic not happened. 

Due to the inherently ordered nature of time series data, both models were validated using a method known as “Walk-Forward Validation” instead of the default Cross-validation included in SciKit Learn and often used on Random Forest Ensembles.  The implementation of Walk-Forward validation allows the model to be retrained after every prediction on the validation dataset, essentially “walking forward” through the time series.  This avoids potential data leakage because of the randomised nature of Cross-validation.

Key findings

The chart below shows the resampled footfall data intersecting with key dates from COVID-19 restrictions.

Key dates are shown as a dotted line with a number relating to a key.  Red zones indicate “official” lockdowns whilst orange represents periods where a variety of restrictions were in place but in the process of being lifted/introduced individually.  A summary of how this impacted footfall is below:

  • Footfall started to drop immediately after the announcement on 16th March 2020, no official restrictions implemented.
  • After non-essential shops and schools reopened on 15th June 2020, footfall started to rise again.
  • Footfall continues to rise through summer until around 22nd September 2020 when some restrictions were announced.
  • Footfall rises whilst Leeds is in tier 2 and 3, potentially because gatherings are only permitted in public spaces.
  • The second and third lockdowns drive footfall back down again until restrictions begin to ease again in April 2021.

The first machine learning model was intended to explore whether any lockdown variables would be significant in predicting future changes in footfall.  Variable importance (top 10) is shown below.

The most important lockdown-related features were indoor entertainment and non-essential retail.  Whilst this is only an initial model and not a definitive conclusion, it does help indicate what aspects of lockdown might have impacted pedestrian traffic in the city centre more than others.

The second model was designed to test how useful the data would be in predicting what “business as usual” may have been like.

There was little difference between error scores across different numbers of trees, so a compromise of the best score and least processing power (500 trees) was chosen.  The model predictions using this hyperparameter are shown below.

Results from this initial model are by no means definitive, however the potential to quantify how much footfall has been lost exists.  For example:

  • Average daily footfall in the lead up to Christmas (taken as 30th November to 24th December 2020) was approximately 36% lower than predicted.
  • Average daily footfall over the school holidays was approximately 63% lower than predicted.
  • Approximate footfall loss for individual Bank Holidays was also calculated.  Most recorded over 90% lower than predicted values except for the August Bank Holiday which was around 22% lower.

Value of the research

Initial analysis has already been delivered to Leeds City Council.  An aggregated dataset of footfall camera data has been created and is available on the Consumer Data Research Centre (CDRC) Data Store for future research.  The initial models developed can be used and refined by future researchers and develop more accurate predictions, whilst more specific time series packages can be explored.


  • Urban footfall and ambient population was significantly impacted by COVID-19 lockdown policies (as was intended).
  • Closure of Indoor Entertainment and Non-Essential retail appear to be the most important lockdown-related factors in predicting footfall change.
  • Consideration must be given to how time series data is processed in classic machine learning models such as Random Forests.

Research theme

Urban analytics


Tom Albone – Data Scientist Intern (LIDA)

Dr Nick Malleson – Professor of Spatial Science

Professor Alison Heppenstall – Professor in Geocomputation

Dr Vikki Houlden – Lecturer in Urban Data Science

Dr Patricia Ternes – Research Fellow


Leeds City Council


Consumer Data Research Centre