Parkcast [beta]: a web app for forecasting the busyness of parks during the pandemic

COVID-19 Hackathon 4: Visualising risk (Team: parksinthepandemic)

Logo by Thomas Gwynfor Sanford (@SandyGwyn on Twitter - follow him!). He's a talented graphic designer and film maker!

Summary

With the onset of COVID-19 in the UK, the importance of recreational park use was brought into sharp focus as one of very few recreational opportunities that was permissible during the initial lockdown period. On the one hand, this potentially presents opportunities for maintaining good health in otherwise isolated times. On the other, a lack of alternative recreational opportunities could mean that parks become busier hotspots for recreation and therefore present risks to the transmission of COVID-19.

We have created an interactive app (https://mattlloydjones.shinyapps.io/parksinthepandemic/) that allows the public to visualise the risks and benefits of using parks over time and space. The app allows users to visualise and experimentally forecast how busy parks (including local parks, national parks, public beaches, marinas, dog parks, plazas, and public gardens) in their area will be over the next few days. The app visualises openly-available, aggregated and anonymised mobile device data from Google (https://www.google.com/covid19/mobility/) that tracks (with user’s consent) how many people are in parks each day in districts of England. Our key innovation is to:

• Combine the Google park busyness data with Met Office weather data and UK Government data relating to park usage and access to green spaces
• Build a random forest regression model to understand how weather, park usage and access to greenspace is related to park busyness (as defined by Google)
• Use OpenWeather forecast data to forecast how busy parks will be in the future (next few days) - based on the weather and social science data, in an interactive app.

Please see our website https://drmattg.github.io/PiP_GH_pages/ for details on our Rationale, how we meet the SMART criteria, the team and the app.

Please also our Youtube video (https://youtu.be/V4fkM7vKO0E) showing how the app works:

How to run the app

Instructions for running the app online (easier but less stable)

1. Go to https://mattlloydjones.shinyapps.io/parksinthepandemic/
2. Interact with the dropdowns to select the time period, district, whether you would like to view all of the historical data with no prediction or whether you would like to view each weekday with a prediction of the next weekday. NOTE: It WILL crash the Shiny servers when you do this a few times - this will be fixed in due course - for now, just reload the page in the bottom left when it crashes, and it will load the data you want! Sorry!

Instructions for locally running the app (harder but more stable)

1. Download R & RStudio and install the R packages (see Dependencies below) needed to run the app using install.packages().
2. Clone the repository - clone/download all the files in this repository to your local computer.
3. Open RStudio, set working directory to source file location and run ‘downloadOSFdata.R’ in the cloned repo directory.
4. Open RStudio, set working directory to source file location and run ‘app.R’ (set working directory to source file location first)

Dependencies:

stats
graphics
grDevices utils
datasets
methods
base

randomForest_4.6-14
conflicted_1.0.4
here_0.1
osfr_0.2.8
remotes_2.1.1.9000
htmltools_0.5.0
htmlwidgets_1.5.1
dashboardthemes_1.1.1 shinythemes_1.1.2
shinydashboard_0.7.1
forcats_0.5.0
stringr_1.4.0
dplyr_1.0.0
purrr_0.3.4
readr_1.3.1
tidyr_1.1.0
tibble_3.0.2
ggplot2_3.3.2
tidyverse_1.3.0
data.table_1.12.8
rgdal_1.5-12
sp_1.4-2
shiny_1.5.0*

Our Approach: Back-end

Our back-end workflow is complicated, but has 3 broad stages, with 3 sub-stages:

1. Matching Met Office data to Google data
2. Matching Government (Natural England and OS Greenspace data) to Google data
3. Building a model and forecasting park busyness from it

1. Matching Met Office data to Google data

• Our first challenge was to create R code for reading in the open datasets made available by Google and the Met Office as part of their COVID-19 response. The Google data for England is downloaded and extracted from the global CSV file available at https://www.google.com/covid19/mobility/ through the function read.googlemobility.R. The Met Office data for England is downloaded from the UK COVID reporting region weather data available at (https://metdatasa.blob.core.windows.net/covid19-response/index.html) through the function read.metofficecovid.R.
• The Google and Met Office data are slightly different. The Met Office data on daily weather conditions are broken down by COVID reporting region, whilst Google uses its own definitions of a region/district. We had to figure out how Google defined it’s regions (and produce a map of each region) and aggregate the weather from each of the COVID reporting regions (typically smaller than the Google regions) to each of the Google regions (match.metoffice.R).
• Google’s data is in the form % busyness relative to how busy parks were on the same day of the week (Mon-Sunday) in the 5‑week winter period Jan 3 – Feb 6, 2020. Positive percentages mean that parks are more busy than the average (median value) during this period, negative percentages mean they are less busy (e.g. + 100% = 2 x as busy, 0% = as busy as winter, -100% 0 visitors). The Met Office data is in absolute values.
• Thus, we had to write some code to make the Met Office data relative to the weather in the Google baseline period; so that, for example, the temperature on a particular day of the week is represented as % increase relative to the average temperature on that day of the week in the winter baseline period. Our function relative2baseline.R does this.

2. Matching Government (Natural England and OS Greenspace data) to Google data

• In order to control for the the density of gardens and parks in each Google district, we read in Ordnance Survey (OS) data that had been pre-processed by the Office for National Statistics (ONS) in match.OSgreenspace2google.R (lines 1-30).
• We then tidied this data (lines 31-55) and merged data for parks and gardens together (lines 56-86). These data were only available at lower-tier local authority level so in order to aggregate them at the level of the Google district, we first read in an openly available lower-tier local authority to upper-tier local authority lookup table (lines 87-104), merged this with the OS data (lines 105-118), before manually matching the upper-tier local authorities to ogle districts (lines 119-154).
• Lastly, the OS data was aggregated at the Google district level (lines 155-186) using sums for frequencies of gardens in a Google district and areal coverage by gardens in a Google district and weighted means (i.e. weighted by numbers of addresses in a Google district) for all other metrics (average size of gardens in a district; average distance to nearest public greenspace, average count of public greenspace within 1000m of a typical address; and average combined size of all public greenspace within 1000m of a typical address).
• Our next task was to obtain ten years of survey data from Natural England’s Monitor of Engagement with the Natural Environment (MENE) survey. The first section of getandmatch.mene.R (lines 1-31) reads in MENE’s visit-based data file, where each row corresponds to a visit. Using the grossing-up weights provided by Natural England, we aggregated the data by upper-tier local authority and produced an estimated annual (i.e. divided by 10) number of visits to green spaces taken by the adult population of England (aged 16+) to each upper-tier local authority.
• Again we manually matched upper-tier local authorities to Google districts in getandmatch.mene.R (lines 32-63).
• Of course, districts with higher populations confer higher numbers of visits, so we decided to normalise visit estimates by the population of each google district by reading in corresponding respondent-based MENE data from Natural England (lines 64-69), estimating the adult population of each upper-tier local authority (lines 70-78), and aggregating at the Google district (lines 79-116).
• As well as these per capita estimates, we further made provision for the normalisation of visit estimates per km2 of greenspace area within the Google district by reading in the OS data again (lines 117-132; see 2.3), merging with the MENE data (lines 133-136), and finally creating variables which represent: (a) raw visit estimates, (b) visit estimates relative to the population of each Google district, (c) visit estimates per km2 greenspace in the 1km surrounding a typical address in the Google district, and (d) visit estimates relative to both population (c) and greenspace access (d) (lines 137-143).

3. Building a model and forecasting park busyness from it

Finally, after matching all of the temporal (Met Office weather data) and single measure data (Natural England and OS Greenspace data) to the Google data, we used the combined dataset to model how the weather and social science data predicts the historical Google park busyness data (create.model.R). We deploy a random forest regression model for this, including the explanatory variables/predictors (name in the model given in brackets):

1. Mean temperature on the day (temp_mean): This is important because people are more likely to visit parks when it’s hotter! (Met Office data)
2. Maximum temperature on the day (temp_max): This is important because people are more likely to visit parks when it’s hotter! (Met Office data)
3. Mean rainfall on the day (rain_mean): This is important because people are less likely to visit parks when it’s raining! We didn’t include maximum or minimum rainfall because minimum rainfall is not provided by the Metoffice dataset, and OpenWeathers’ dataset for rainfall is not discrete enough to calculate an accurate maximum rainfall that corresponds to the Metoffice values. (Met Office data)
4. Minimum temperature on the day (temp_min): This is important because people are more likely to visit parks if it’s warmer in the evening. (Met Office data)
5. The day of the week it is (weekday): This is important because people are likely to behave differently on weekends than on weekdays (e.g. more of an increase because people have more free time
6. The number of addresses within the Google district (address_count_new) This is important as an estimate of population density in the district. A higher population could be associated with higher visits or conversely, a more urbanised area may have less accessible greenspace to visit. (Office for National Statistics/Ordinance Survey data)
7. The average total size of all publicly accessible green spaces within 1km of a typical address within the Google district i.e. averaged across all addresses within the district (avg_combined_size_public_green_1000m_radius_m2_new) This is important because it provides an estimate of the average areal coverage by accessible public green space for any given household in a region (Office for National Statistics/Ordinance Survey data)
8. The average distance in metres from a typical address within the Google district to the nearest public greenspace i.e. averaged across all addresses within the district (avg_dist_nearest_public_green_m_new) This indicates how readily accessible a public greenspace is for a typical household within the district (Office for National Statistics/Ordnance Survey data).
9. The percentage of addresses within the Google district which had access to a private outdoor space e.g. garden, balcony, or yard (percent_addresses_w_private_outdoor_space_new) This is important because if people have access to gardens they might substitute time in public greenspace for time in their own gardens. (Office for National Statistics/Ordinance Survey data)
10. The average size of a private garden or outdoor space (e.g. yard, balcony) for a typical address within the Google district i.e. averaged across all addresses with a private outdoor space within the district (average_size_private_outdoor_space_m2_new). Like (9), this speaks to potential substitutability - those with larger private outdoor spaces may substitute time in these for time in public greenspaces; those with smaller (e.g. shared or non-natural) private outdoor space, may choose to visit public greenspace more often (Office for National Staitstics/Ordnance Survey data).
11. The number of recreational visits to green spaces within the Google district taken by the adult population of England (aged 16+) annually (averaged across 10 years of data) divided by the 16+ population of the Google district and the total size of all publicly accessible greenspace within 1000m of a typical address within the Google district (annual_visits_per_capita_per_km2_greenspace_1km_radius) This is important because it provides an estimate of the number of visits to greenspaces in a region relative to the population size and the amount of available greenspace. (Natural England Monitor of Engagement with the Natural Environment data)
12. The average number of public green spaces within 1km of a typical address within the Google district i.e. averaged across all addresses within the district (avg_count_public_green_1000m_radius_new) This is important because it provides an estimate of the average number of accessible parks for any given household in a region; more choice could mean more visits, or conversely more indecision. (Office for National Statistics/Ordinance Survey data)

Variable Importance Plot of Random Forest Model

After building this model (create.model.R), we use it to predict how busy parks will be over the coming days, based on these variables. The Natural England and OS Greenspace data for each district remain constant for this prediction/forecast, whilst the weather data is brought in from 6-day OpenWeather forecasts. A prediction of park busyness (% change from winter baseline) is then made and displayed in our app as a white bar in the bar graph. The bar graph is shown per weekday because Google baselines are generated per-weekday, and so a plot per weekday represents a true time series.

The variable importance plot of our random forest model (above) indicates that temperature was the most important predictor of Google park visit trends, followed closely by rain. Day of the week was also important - probably indicating that whether it’s a weekend effects park visits a lot! The social science data had a subtler effect on model estimates, but were nonetheless important

Our approach here is very preliminary, there will be bugs due to our time constraints and it needs a lot of refinement - our main goal was to produce a very preliminary forecast that we could visualise in the app

Our Approach: Front-end

The front-end is an RShiny app (app.R) to display the historical park visit data alongside the predictions of park usage for the coming days. This app has two main components:

• A bar graph of park busyness over time, relative to the winter baseline period. Less busy than winter is displayed in grey, more busy than winter is displayed in green. Users can interact with the graphs in the following ways:

1. View the whole historical dataset for every day where there is Google data available, with no forecast
2. Select the time period to view the data on the whole historical dataset on the graph and on the map, using the calendar on the sidebar.
3. View the historical datasets for each weekday (a true time series), with prediction for the next weekday displayed as a white bar. Add a scaled percentage change plot for temperature and rainfall to both the historical dataset for each weekday and the whole historical dataset.

• A map with the boundaries of the Google districts and the busyness of parks (less busy than winter is displayed in darker shades of grey, more busy than winter is displayed in darker shades of green). Users can interact with this map by:

1. Selecting the time period to view the mapped-out data on the whole historical dataset (forecasts are not yet displayed on map), using the calendar on the sidebar.
2. Selecting a region on the sidebar, which is then highlighted with a purple border on the map.

The bar graph was produced by re-visualising the Google data in the more appropriate format of a bar-graph with negative and positive colours to make visualising how busy it is relative to winter more intuitive. In addition, we changed percentage change into the form of ‘how many times as busy is it’, as this is more intuitive. The function plot.googlemobilitydistricts.R does this for the whole time series, the function *plot.parkvisits.R does this on a per-weekday basis that can be interacted with in the app.

The map was a tricky one; after identifying how the Google regions were defined, the challenge was to match these to shapefiles from Ordinance Survey open boundary line data (https://www.ordnancesurvey.co.uk/business-government/products/boundaryline) in the script match.OSopenBoundary2google.R. To our knowledge, this has not been done before and this is the first map of the Google regions, which we make freely available in the ‘spatial’ folder of our Open Science Framework data repository (https://osf.io/c7kg4/) - which includes all the other data that goes into the app.

Conclusions

• It is feasible (but hard!) to bring together big data, environmental data and social science data for a combined visualisation and analysis of park visits
• Our app shows that there are more intuitive, interactive ways to visualise the available data without showing all of the complexity!
• Bedford's parks seem to be a lot less busy in the pandemic relative to other regions, and forecasts are potentially more accurate for Bedford than other regions (https://youtu.be/V4fkM7vKO0E)
• There is some indication that weather patterns are very important predictors of park usage compared to social factors, but this needs to be investigated further.
• There is potential to develop a forecast of park visits based on the weather and social science data, but this needs a lot more work (e.g. refining random forest parameters and debugging/checking classes of inputs, incorporating data relating to lockdown conditions

Further development

For further development, we would like to re-check every stage of our back-end code to ensure it is performing as expected and improve the forecasting element by dedicating a lot more time to this. Nonetheless, it is exciting that there is potential for forecasting park visits to improve individual, institutional and business decision-making, when the right data becomes available (or Google provide more thorough data that can be used in this way).

Code documentation (work in progress)

For detailed code documentation (work in progress), please see Code documentation.docx in the 'code' folder.

Data used

All the data that goes into the app is available at our Open Science Framework data repository https://osf.io/c7kg4/. It is derived from the sources below.

The primary data on park visits displayed in the app is Google data from the Google Community Mobility Reports - Google LLC Google COVID-19 Community Mobility Reports. https://www.google.com/covid19/mobility/

The map of the boundaries of the Google Community Mobility Reports UK districts are located at Open Science Framework (https://osf.io/c7kg4/). These shapefiles (named googleboundaries__XXXX) contain OS data © Crown copyright and database right 2020, derived from derived from OSopen boundary-line (https://www.ordnancesurvey.co.uk/business-government/products/boundaryline).

Historical weather data plotted on the bar graph and used to inform the forecast model is supplied by the Met Office (c) Crown Copyright, 2020 as part of their their COVID-19 response https://metdatasa.blob.core.windows.net/covid19-response/index.html. This data is released under the open government license version 3.0. Contains Met Office data licensed under the Open Government Licence v3.0TM. For licence details details see: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

Natural England data used to inform the forecast model is the ‘Visits’ data ( We http://publications.naturalengland.org.uk/file/6746346730291200) from Natural England’s Monitor of Engagement with the Natural Environment. The national survey on people and the natural environment. Technical Report to the 2009—2019 surveys. This data is released under the open government license version 3.0. Contains Natural England data licensed under the Open Government Licence v3.0TM. For licence details details see: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

Greenspace data used to inform the forecast model contains OS data © Crown copyright and database right 2020 Contains Royal Mail data © Royal Mail copyright and Database right 2020 Contains National Statistics data © Crown copyright and database right 2020. This data is released under the open government license version 3.0. Contains OS, Royal Mail and National Statistics data licensed under the Open Government Licence v3.0TM. For licence details details see: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/Available at https://www.ons.gov.uk/economy/environmentalaccounts/datasets/accesstogardensandpublicgreenspaceingreatbritain

Weather forecast data is from the OpenWeather 5-day/3 hour forecast weather API (https://openweathermap.org/api) obtained under a ‘Free’ pricing plan.

Team

• Scarlett Kynoch (University of Exeter/back-end R programming)
• Dr. Matt Grainger (Norsk institutt for naturforsknin/front-end RShiny programming)
• Anna Bush (University of Exeter/front-end RShiny programming & Github)
• Dr. Lewis Elliot (University of Exeter/back-end R programming & OS greenspace/Natural England MENE data)
• Dr. Matt Lloyd Jones (University of Exeter/Project Manager & back-end R programming).

We also thank Katherine Burgess, Beth Brockett & Rose O'Neill from Natural England for their advice and support throughout.