recre_supply: Calculate cumulative supply of an attribute from all parks to...

In ecological-cities/home2park: Spatial Provision of Urban Parks

Calculate cumulative supply of an attribute from all parks to each building

Description

Sum 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).

Usage

recre_supply(park_attribute, dist_matrix, c = 1)

Arguments

park_attribute	numeric vector. Amount of a specific attribute per park. Length of vector is equal to the number of parks considered.
dist_matrix	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.
c	Coefficient determining rate of decay in recreation supply with increasing distance.

Details

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.

Value

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.

Source

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.

Examples

## Not run: 
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

## End(Not run)

ecological-cities/home2park documentation built on Dec. 6, 2023, 1:05 a.m.