ediblecity

The goal of ediblecity is to is to estimate the potential of UA to contribute to addressing several urban challenges at the city-scale. Within this aim, we followed the urban challenges defined by the Eklipse project that are followed for nearly all of the European projects focused on Nature-based Solutions. We selected 8 indicators directly related to one or several urban challenges.

Installation

You can install the development version of ediblecity from r-universe with:

install.packages("ediblecity", repos = "jospueyo.r-universe.dev")

Indicators estimated

The package provides eight indicators that estimate different benefits of urban agriculture:

food_production(): Amount of food produced in the city.
green_capita(): Green per capita can be computed as raw or as the difference among neighbourhoods.
green_distance(): Distance to closest public green area larger than certain surface. It computes also the proportion of homes that are further than a specific threshold.
UHI(): Urban heat island as a rasters (stars object) or as numeric values.
edible_jobs(): Number of jobs created by commercial urban agriculture.
edible_volunteers(): Number of volunteers involved in community urban agriculture.
no2_seq(): Amount of NO₂ sequestered by urban green (in gr/s).
runoff_prev(): Runoff in the city after a specific rain event. It also computes the amount of rainwater harvested by urban agriculture initiatives.

Set a scenario

Although ediblecity can also estimate indicators directly from an sf object, the function set_scenario provides a basic tool to create an scenario combining different proportions of elements of urban agriculture. Some warnings are triggered when the function can’t satisfy the parameters passed by the user.

library(ediblecity)

scenario <- set_scenario(city_example,
                         pGardens = 0.7,
                         pVacant = 0.8,
                         pRooftop = 0.6,
                         pCommercial = 0.5)
#> Only 328 rooftops out of 362.4 assumed satisfy the 'min_area_rooftop'

All attributes of urban agriculture elements are included in city_land_uses dataframe. This can be used as default. Otherwise, a customized dataframe can be provided to compute each indicator.


knitr::kable(city_land_uses)

land_uses	edible	public	pGreen	jobs	volunteers	location	no2_seq1	no2_seq2	food1	food2	CN1	CN2	water_storage2	water_storage
Edible private garden	TRUE	FALSE	0.6	FALSE	FALSE	garden	0.07	0.09	0.2	6.6	85	88	10	TRUE
Community garden	TRUE	TRUE	1.0	FALSE	TRUE	vacant	0.07	0.09	0.2	2.2	85	88	10	TRUE
Commercial garden	TRUE	FALSE	1.0	TRUE	FALSE	vacant	0.07	0.09	4.0	6.6	85	85	10	TRUE
Rooftop garden	TRUE	TRUE	1.0	FALSE	TRUE	rooftop	0.07	0.07	0.2	2.2	67	88	10	TRUE
Hydroponic rooftop	TRUE	FALSE	1.0	TRUE	FALSE	rooftop	0.07	0.07	9.0	19.0	98	98	10	TRUE
Arable land	TRUE	FALSE	0.6	FALSE	FALSE	no	0.00	0.07	4.0	6.6	85	88	0	FALSE
Normal garden	FALSE	FALSE	0.6	FALSE	FALSE	no	0.07	0.07	1.0	1.0	74	86	10	TRUE
Permanent crops	TRUE	FALSE	0.6	FALSE	FALSE	no	0.09	0.09	4.0	6.6	65	77	0	FALSE
Vacant	FALSE	FALSE	1.0	FALSE	FALSE	no	0.07	0.09	1.0	1.0	74	87	0	FALSE
Grass	FALSE	TRUE	1.0	FALSE	FALSE	no	0.07	0.07	1.0	1.0	74	86	0	FALSE
Mulcher	FALSE	TRUE	1.0	FALSE	FALSE	no	0.00	0.00	1.0	1.0	88	88	0	FALSE
Raised bed	FALSE	TRUE	1.0	FALSE	FALSE	no	0.07	0.07	1.0	1.0	67	88	0	FALSE
Trees	FALSE	FALSE	1.0	FALSE	FALSE	no	0.11	0.11	1.0	1.0	70	77	0	FALSE
Vegetated pergola	FALSE	TRUE	1.0	FALSE	FALSE	no	0.07	0.07	1.0	1.0	98	98	0	FALSE

Contributors

Contributions are welcome! Some of the existing indicators can be improved as well as new indicators can be created. Likewise, the creation of new scenarios can include new elements of urban agriculture or provide further customization.

Scientific collaborations are also welcome! Check my research profile at Google scholar.

Acknowledgements

This research was funded by Edicitnet project (grant agreement nº 776665)