R/recre_supply.R
recre_supply.RdSum up the total amount of a specific park attribute supplied to each building polygon. The amount per building depends on the distances between that particular building and all parks; attributes from parks further away are generally reduced, an effect also known as the 'distance decay' (Rossi et al., 2015; Tu et al., 2020).
recre_supply(park_attribute, dist_matrix, c = 1)Rossi, S. D., Byrne, J. A., & Pickering, C. M. (2015). The role of distance in peri-urban national park use: Who visits them and how far do they travel?. Applied Geography, 63, 77-88.
Tu, X., Huang, G., Wu, J., & Guo, X. (2020). How do travel distance and park size influence urban park visits?. Urban Forestry & Urban Greening, 52, 126689.
numeric vector. Amount of a specific attribute per park. Length of vector is equal to the number of parks considered.
Matrix containing buildings (rows) and their pairwise distances to each park (columns).
Order of parks (columns) should be identical to the order of parks (elements) in park_attribute.
Coefficient determining rate of decay in recreation supply with increasing distance.
A numeric vector of the cumulative supply value per building (row) in the input matrix dist_matrix.
The length of the vector equals to the number of buildings considered.
The supply \(S\) of the park attribute is calculated based on the following equation:
\(S = \sum\limits_{i=1}^{n} s_{i} \cdot e^{-cd_{i}}\)
where
\(S\) = Total supply of a specific park attribute to the building from parks \(i\); \(i = 1,2,3\)... \(n\), \(n\) = total number of parks
\(s_{i}\) = Supply of a specific park attribute from park \(i\). A perfect positive linear association is assumed, since the focus is on supply metrics.
\(d_{i}\) = Distance in kilometres from the building to park \(i\) (e.g. Euclidean, Manhattan, etc.).
\(c\) = Coefficient determining rate of decay in supply \(i\) with increasing distance.
if (FALSE) {
data(parks_sgp) # load park polygons
data(buildings_pop_sgp) # load building polygons w population counts
# transform to projected crs
parks_sgp <- sf::st_transform(parks_sgp, sf::st_crs(32648))
buildings_pop_sgp <- sf::st_transform(buildings_pop_sgp, sf::st_crs(32648))
# Calculate pairwise distances between (the centroid of) each building & all parks
d_matrix <- buildings_pop_sgp %>%
st_centroid() %>%
st_distance(parks_sgp) # euclidean distance
m_dist <- m_dist / 1000 # convert distances to km
# run function for a specific park attribute (e.g. area)
recre_supply(park_attribute = parks_sgp$area,
dist_matrix = d_matrix,
c = 0.3) # example value for distance decay coefficient c
}