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1 The UrbanShift project

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1.1 Objectives

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UrbanShift is a global program that supports cities to adopt integrated approaches to urban development, shaping low-carbon, climate-resilient communities where people and planet both can thrive. The program is funded by the Global Environment Facility (GEF) and jointly managed by a global team consisting of the United Nations Environment Program (UNEP), World Resources Institute (WRI), C40 Cities and ICLEI Local Governments for Sustainability. The initiative supports 23 cities across nine countries, providing the knowledge, tools and training they need to transform their urban fabric and shift towards a more sustainable, equitable future.

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As one of the key activities to support development of a knowledge-base for the UrbanShift initiative and all participant cities, the WRI data team will work with UrbanShift cities to identify and provide all cities with a common set of critical spatial data layers. using open source, global data. World Resources Institute is providing several types of data-related assistance to participating cities:

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  • A suite of key geo-spatial layers
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  • Baseline measurements of core UrbanShift indicators
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  • Geo-spatial analysis on selected thematic areas
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  • Capacity building and technical assistance on data governance and geospatial data as part of the City Academy and Labs modules of UrbanShift.
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Outputs will include datasets, indicators and replicable analysis methods relevant to all cities. Additionally, analyses customized to the specific themes of interest for each city will be provided. Finally, an UrbanShift Lab will be delivered for which these data and analyses may act as one input.

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1.2 Baseline indicators

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To help understand the current status and identify changes of sustainability in UrbanShift cities, we aim to measure key baseline indicators for all cities using comparable approaches. The selected indicators focus on measuring the status and change on the core objectives of the global project, which are aligned with three of Global Environment Facility’s focal areas for its current investment cycle (GEF-7):land degradation, biodiversity, and greenhouse gas emissions.

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These assessments are intended to provide information to evaluate patterns within and between cities and to provide contextual information to cities to help them with problem and solution definition. We will disseminate the results to help local governments, the global project team, implementing agencies and national governments to gain a better understanding of the cities’ current status as it relates to sustainability efforts, capacities, main needs and opportunities, and planned investments.

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2 Biodiversity

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2.1 The Singapore Index on cities’ Biodiversity

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The City Biodiversity Index was launched by Singapore in 2008 at the eight Conference of the Parties to the convention on Biological Diversity (DBD). It serves as a self-assessment tool for cities to monitor the progress of their biodiversity conservation efforts.

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The Singapore Index framework is constituted of two parts:

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  • The profile of the City: It provides background information on the city: location, size, population, economic parameters, physical features, biodiversity features, administration….
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  • Biodiversity indicators: It includes 28 indicators that measure native biodiversity, ecosystem services and management of biodiversity in the city. Each indicator is assigned a scoring range between zero and four points.
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The complete methodology for computing Singapore Biodiversity Index is provide in this publication.

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2.2 UrbanShift Biodiversity indicators

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Indicator nameDefinitionData sources
I-1. Proportion of natural areas(Total area of natural, restored and naturalised areas) ÷ (Area of city) × 100%ESA WorldCover (natural areas as all values except crop, built-up, bare)
I-2. Connectivity measures or ecological networks to counter fragmentationESA WorldCover (natural areas as all values except crop, built-up, bare)
I-3. Native biodiversity in built-up areas (birds)(Number of native bird species found in built-up areas) ÷ (Total number of native bird species in the city) × 100%ESA WorldCover, iNaturalist 2020 research-grade observations
I-3. Native biodiversity in built-up areas (birds)(Number of native bird species found in built-up areas) ÷ (Total number of native bird species in the city) × 100%ESA WorldCover, iNaturalist 2020 research-grade observations
I-4. Change in number of native species (vascular plants)Total increase in number of vascular plant species (as a result of re-introduction, rediscovery, new species found due to more intensive and comprehensive surveys, etc.)iNaturalist 2020 research-grade observations
I-5. Change in number of native species (birds)Total increase in number of native bird species (as a result of re-introduction, rediscovery, new species found due to more intensive and comprehensive surveys, etc.)iNaturalist 2020 research-grade observations
I-6. Change in number of native species (arthropods)Total increase in number of native arthropod species (as a result of re-introduction, rediscovery, new species found due to more intensive and comprehensive surveys, etc.)iNaturalist 2020 research-grade observations
I-7. Habitat restoration(Area of habitat restored) ÷ (Area of original habitat that is degraded) × 100%
I-8. Proportion of protected natural areas(Area of protected or secured natural areas) ÷ (Total area of the city) × 100%World Database of Protected Areas
I-9. Proportion of invasive alien speciesTo ensure that the comparison of invasive alien specie with that of native species is meaningful, it would have to be a comparison of identical taxonomic groups.(Number of invasive alien species in a taxonomic group) ÷ (Total number of native species of the same taxonomic group + number of invasive alien species) × 100%Global Invasive Species Database
I-10. Regulation of quantity of water(Total permeable area) ÷ (Total terrestrial area of the city) × 100%GAIA 2018 30m impervious area
I-11. Climate regulation: carbon storage and cooling effect of vegetation(Tree canopy cover) ÷ (Total terrestrial area of the city) × 100%
I-12. Recreational services(Area of parks, nature conservation areas and other green spaces with natural areas and protected or secured accessible natural areas) /1000 personsOpenStreetMap, WorldPop
I-13 Proximity to parks(Population of city living within 400m from a park/green space) ÷ (Total population of city) × 100%OpenStreetMap, WorldPop
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2.3 Data sources

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  • Global Biodiversity Information Facility (GBIF): The Global Biodiversity Information Facility (GBIF) is an international network and data infrastructure funded by the world’s governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth.

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  • The World Database on Protected Areas (WDPA): The World Database on Protected Areas (WDPA) is the most comprehensive global database of marine and terrestrial protected areas. It is a joint project between UN Environment Programme and the International Union for Conservation of Nature (IUCN), and is managed by UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), in collaboration with governments, non-governmental organisations, academia and industry.

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  • The Global Invasive Species Database (GISD): The Global Invasive Species Database (GISD) is a free, online searchable source of information about alien and invasive species that negatively impact biodiversity. It focuses on invasive alien species that threaten native biodiversity and natural areas and covers all taxonomic groups from micro-organisms to animals and plants.

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  • ESA World Cover: The European Space Agency (ESA) WorldCover 10 m 2020 product provides a global land cover map for 2020 at 10 m resolution based on Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes, aligned with UN-FAO’s Land Cover Classification System, and has been generated in the framework of the ESA WorldCover project.

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3 Case study: San Jose

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3.1 Data sources exploration

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3.1.1 ESA World Cover

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The European Space Agency (ESA) WorldCover 10 m 2020 product provides a global land cover map for 2020 at 10 m resolution based on Sentinel-1 and Sentinel-2 data. The WorldCover project, part of the 5th Earth Observation Envelope Programme (EOEP-5), had the objective to produce, deliver and validate, as fast as possible, a global 10 meter resolution land cover (LC) map of the world within 3 months of the last data acquisition with a minimum of 10 land cover classes and a minimum overall accuracy of 75%.

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The WorldCover product comes with 11 land cover classes:

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  • Tree cover: This class includes any geographic area dominated by trees with a cover of 10% or more. Areas planted with trees for afforestation purposes and plantations (e.g. oil palm, olive trees) are included in this class. This class also includes tree covered areas seasonally or permanently flooded with fresh water except for mangroves.
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  • Shrubland: This class includes any geographic area dominated by natural shrubs having a cover of 10% or more. Shrubs are defined as woody perennial plants with persistent and woody stems and without any defined main stem being less than 5 m tall.
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  • Grassland: This class includes any geographic area dominated by natural herbaceous plants (Plants without persistent stem or shoots above ground and lacking definite firm structure): (grasslands, prairies, steppes, savannahs, pastures) with a cover of 10% or more, irrespective of different human and/or animal activities.
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  • Cropland: Land covered with annual cropland that is sowed/planted and harvestable at least once within the 12 months after the sowing/planting date.
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  • Built-up: Land covered by buildings, roads and other man-made structures such as railroads. Buildings include both residential and industrial building.
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  • Bare / sparse vegetation: Lands with exposed soil, sand, or rocks and never has more than 10% vegetated cover during any time of the year.
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  • Snow and ice: This class includes any geographic area covered by snow or glaciers persistently.
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  • Open water: This class includes any geographic area covered for most of the year (more than 9 months) by water bodies: lakes, reservoirs, and rivers.
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  • Herbaceous wetland: Land dominated by natural herbaceous vegetation (cover of 10% or more) that is permanently or regularly flooded by fresh, brackish or salt water.
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  • Mangroves: Taxonomically diverse, salt-tolerant tree and other plant species which thrive in intertidal zones of sheltered tropical shores, “overwash” islands, and estuaries.
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  • Moss and lichen: Land covered with lichens and/or mosses. Lichens are composite organisms formed from the symbiotic association of fungi and algae. Mosses contain photo-autotrophic land plants without true leaves, stems, roots but with leaf-and stemlike organs.
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+land cover class + +land percent + +year +
+Trees + +74.63 + +2020 +
+Grassland + +19.14 + +2020 +
+Built-up + +4.07 + +2020 +
+Cropland + +0.95 + +2020 +
+Barren / sparse vegetation + +0.87 + +2020 +
+Open water + +0.20 + +2020 +
+Shrubland + +0.14 + +2020 +
+Herbaceous wetland + +0.01 + +2020 +
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3.1.2 Global Biodiversity Information Facility (GBIF)

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3.1.2.1 Metropolitan level

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  • Location of reported birds:
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  • Number of reported birds by order:
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  • Number of reported birds by family:
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  • Number of reported birds by genus:
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+genus name + +Number of species +
+Thraupis + +58 +
+Momotus + +50 +
+Zenaida + +44 +
+Zonotrichia + +39 +
+Turdus + +37 +
+Tyrannus + +36 +
+Buteo + +35 +
+Piranga + +31 +
+Pitangus + +30 +
+Quiscalus + +27 +
+Melanerpes + +26 +
+Setophaga + +26 +
+Icterus + +25 +
+Piaya + +24 +
+Columbina + +23 +
+Myiozetetes + +23 +
+Aratinga + +21 +
+Heliodoxa + +21 +
+Patagioenas + +20 +
+Psilorhinus + +20 +
+Saltator + +19 +
+Amazilia + +18 +
+Psarocolius + +18 +
+Ramphastos + +18 +
+Volatinia + +17 +
+Campylorhynchus + +16 +
+Chlorospingus + +15 +
+Vireo + +15 +
+Euphonia + +14 +
+Ortalis + +14 +
+Tangara + +13 +
+Brotogeris + +12 +
+Dryocopus + +12 +
+Leiothlypis + +12 +
+Melozone + +12 +
+Troglodytes + +12 +
+Eupherusa + +11 +
+Glaucidium + +11 +
+Lampornis + +11 +
+Pheucticus + +11 +
+Campylopterus + +10 +
+Microchera + +10 +
+Mimus + +10 +
+Myioborus + +10 +
+Sayornis + +10 +
+Cypseloides + +9 +
+Eubucco + +9 +
+Ramphocelus + +9 +
+Semnornis + +9 +
+Aulacorhynchus + +8 +
+Basileuterus + +8 +
+Columba + +8 +
+Contopus + +8 +
+Discosura + +8 +
+Notiochelidon + +8 +
+Pionus + +8 +
+Cathartes + +7 +
+Catharus + +7 +
+Colibri + +7 +
+Coragyps + +7 +
+Crotophaga + +7 +
+Falco + +7 +
+Herpetotheres + +7 +
+Saucerottia + +7 +
+Tityra + +7 +
+Zentrygon + +7 +
+Archilochus + +6 +
+Ardea + +6 +
+Cardellina + +6 +
+Chamaepetes + +6 +
+Dendrocygna + +6 +
+Milvago + +6 +
+Panterpe + +6 +
+Pharomachrus + +6 +
+Tiaris + +6 +
+Anthracothorax + +5 +
+Bubulcus + +5 +
+Caracara + +5 +
+Cyanerpes + +5 +
+Dives + +5 +
+Eugenes + +5 +
+Heliomaster + +5 +
+Lepidocolaptes + +5 +
+Megarynchus + +5 +
+Mitrephanes + +5 +
+Myiodynastes + +5 +
+Passerina + +5 +
+Phainoptila + +5 +
+Arremon + +4 +
+Butorides + +4 +
+Cantorchilus + +4 +
+Chaetura + +4 +
+Empidonax + +4 +
+Passer + +4 +
+Phaethornis + +4 +
+Pselliophorus + +4 +
+Pseudoscops + +4 +
+Spinus + +4 +
+Thamnophilus + +4 +
+Todirostrum + +4 +
+Vermivora + +4 +
+Amazona + +3 +
+Aramides + +3 +
+Cinclus + +3 +
+Elaenia + +3 +
+Megascops + +3 +
+Mniotilta + +3 +
+Peucaea + +3 +
+Protonotaria + +3 +
+Pteroglossus + +3 +
+Rupornis + +3 +
+Seiurus + +3 +
+Selasphorus + +3 +
+Trogon + +3 +
+Anhinga + +2 +
+Arremonops + +2 +
+Atlapetes + +2 +
+Cairina + +2 +
+Calliphlox + +2 +
+Chloroceryle + +2 +
+Chlorophanes + +2 +
+Crax + +2 +
+Dendrortyx + +2 +
+Diglossa + +2 +
+Doryfera + +2 +
+Geothlypis + +2 +
+Glyphorynchus + +2 +
+Jacana + +2 +
+Margarornis + +2 +
+Micrastur + +2 +
+Molothrus + +2 +
+Myadestes + +2 +
+Pachyramphus + +2 +
+Premnoplex + +2 +
+Ptilogonys + +2 +
+Spizaetus + +2 +
+Streptoprocne + +2 +
+Tapera + +2 +
+Tringa + +2 +
+Tryngites + +2 +
+Vanellus + +2 +
+Acanthidops + +1 +
+Accipiter + +1 +
+Ammodramus + +1 +
+Anas + +1 +
+Antrostomus + +1 +
+Ara + +1 +
+Bartramia + +1 +
+Campephilus + +1 +
+Charadrius + +1 +
+Chiroxiphia + +1 +
+Chlorophonia + +1 +
+Chondrohierax + +1 +
+Cistothorus + +1 +
+Coccyzus + +1 +
+Cochlearius + +1 +
+Coereba + +1 +
+Colaptes + +1 +
+Cynanthus + +1 +
+Dumetella + +1 +
+Elanoides + +1 +
+Elanus + +1 +
+Fulica + +1 +
+Gampsonyx + +1 +
+Grallaria + +1 +
+Grallaricula + +1 +
+Habia + +1 +
+Hylocichla + +1 +
+Junco + +1 +
+Legatus + +1 +
+Leuconotopicus + +1 +
+Lophotriccus + +1 +
+Megaceryle + +1 +
+Mionectes + +1 +
+Morococcyx + +1 +
+Mycteria + +1 +
+Nomonyx + +1 +
+Numida + +1 +
+Odontophorus + +1 +
+Oxyruncus + +1 +
+Pandion + +1 +
+Petrochelidon + +1 +
+Platyrinchus + +1 +
+Pulsatrix + +1 +
+Riparia + +1 +
+Sclerurus + +1 +
+Scytalopus + +1 +
+Serpophaga + +1 +
+Spiza + +1 +
+Sporophila + +1 +
+Stelgidopteryx + +1 +
+Strix + +1 +
+Sturnella + +1 +
+Syndactyla + +1 +
+Thripadectes + +1 +
+Tigrisoma + +1 +
+Tolmomyias + +1 +
+Tyto + +1 +
+Xenops + +1 +
+Zeledonia + +1 +
+Zimmerius + +1 +
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3.1.2.2 Municipality level

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3.1.3 World Database on Protected Areas (WDPA)

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3.2 Baseline indicators

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3.2.1 I-1. Proportion of natural areas

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Natural ecosystems contain more species than human-altered landscapes, hence, the higher the percentage of natural areas compared to that of the total city area gives an indication of the amount of biodiversity.

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Natural ecosystems are defined as all areas that are natural and not highly disturbed or completely human-altered landscapes. Examples of natural ecosystems include forests, mangroves, freshwater swamps, natural grasslands, streams, lakes, etc. Parks, golf courses, roadside plantings are not considered as natural. However, natural ecosystems within parks where native species are dominant can be included in the computation.

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This indicator is calculated as the percent of natural area within the city boundary: (Total area of natural, restored and naturalised areas) ÷ (Area of city) × 100%

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Based on the assumption that,by definition, a city comprises predominantly human-altered landscapes, the maximum score will be accorded to cities with natural areas occupying more than 20% of the total city area.

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+Score + +Indicator.value +
+0 POINTS + +< 1.0% +
+1 POINTS + +0% – 6.9% +
+2 POINTS + +7.0% – 13.9% +
+3 POINTS + +14.0% – 20.0% +
+4 POINTS + +> 20.0% +
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3.2.2 I-2. Connectivity measures or ecological networks to counter fragmentation

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Fragmentation of natural areas usually occurs due to development of grey or built infrastructure such as roads, residential and commercial buildings, public amenities… It is increasingly being proven that connectivity is a vital element of landscape structure. The fragmentation of natural areas affects different species differently. For example, a road may not be a barrier for birds but it can seriously fragment a population of arboreal primates. While these differences have been considered, a pragmatic approach towards the calculation of a connectivity indicator is applied. It involves a 2 step process: calculating the effective mesh size, followed by coherence that will normalize for the size of the city.

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  • Step 1: calculate the effective mesh size (EMS):
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\[EMS = \frac{1}{A_{total}}(A^2_{G1}+A^2_{G1}+A^2_{G1}+...+A^2_{Gn})\] Where:

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  • \(A_{total}\) is the total area of all natural areas
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  • \(A_{G1}\) to \(A_{Gn}\) are the sizes of each group of connected patches of natural area that are distinct from each other
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  • \(n\) is the total number of groups of connected patches of natural area.
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\(A_{G1}\) to \(A_{Gn}\) may consist of areas that are the sum of two or more smaller patches which are connected. In general, patches are considered as connected if they are less than 100m apart. This equation was derived from Deslauriers et al. (2018).

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  • Step 2: calculate the coherence:
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\[Coherence = \frac{EffectiveMeshSize}{A_{total}}\]

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3.2.3 I-3 Native biodiversity in built-up areas (bird species)

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This indicator corresponds to the percentage of the number of native bird species in built up areas relative to the total number of native bird species. Built up areas include impermeable surfaces like buildings, roads, drainage channels, etc., and anthropogenic green spaces like roof gardens, roadside planting, golf courses, private gardens, cemeteries, lawns, urban parks, etc.

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The scoring is based on the reality that the built-up areas of cities have fewer diversity of natural eco-systems and hence, a lesser number of native bird species would be found in them.

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3.2.4 I-4 Change in number of native species (vascular plants)

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3.2.5 I-5 Change in number of native species (birds)

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3.2.6 I-6 Change in number of native species (arthropods)

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3.2.7 I-7 Habitat restoration

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3.2.8 I-8 Proportion of protected natural areas

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Protected or secured natural areas indicate the city’s commitment to biodiversity conservation. Hence, the proportion of protected or secured natural areas is an important indicator. The indicator using the following formula: Area of protected or secured natural areas) ÷ (Total area of the city) × 100%

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3.2.9 I-9 Proportion of invasive alien species

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3.2.10 I-10 Regulation of quantity of water

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Impervious areas alter the hydrologic cycle in cities, affecting both water quality and quantity. In addition, climate change is in many places predicted to result in increased variability in precipitation which in urban landscapes may translate into high peaks in water flow and damage to construction, business and transport, as well as lower ecological quality of receiving waters. Vegetation has a significant effect in reducing the rate of flow of water through the urban landscape, e.g., through presence of forest, parks, lawns, roadside greenery, streams, rivers, waterbodies, etc.

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This indicator is calculated by measuring the proportion of all permeable areas to total terrestrial area of city: Total permeable area) ÷ (Total terrestrial area of the city) × 100%

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3.2.11 I-11 Climate regulation: carbon storage and cooling effect of vegetation

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3.2.12 I-12 Recreational services

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It has been increasingly recognized that urban green parks, nature conservation areas and other green spaces with a high quality of biological diversity provide invaluable recreational, spiritual, cultural and educational services. They are essential for human physical and psychological health.

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The recreational services indicator is calculated as the total areas of parks, nature conservation areas and other green spaces with natural areas and protected or secured accessible natural areas by 1000 persons.

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3.2.13 I-13 Proximity to parks

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Proximity is measured in terms of the proportion of the households living within 400m from a park or green space. Straight line distances are used to determine whether households fall within 400m from a park or green space. This inidcator is calculated as following: (Population of city living within 400m from a park/green space) ÷ (Total population of city) × 100%.

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3.3 Biodiversity index synthesis

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The Singapore biodiversity Index is computed as the sum of the different biodiversity indicators:

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\[BiodiversityIndex_{city} = \sum_{i=1}^{nb_{indicators}} (I1_{city},I2_{city},...,In_{city})\]

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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Municipality + +I1 - Percent of natural areas + +I2 - Connectivity + +I3 - Birds in built areas + +I8 - Protected areas + +I10 - Water + +I12 - Recreational services + +I13 - Proximity to parks + +Biodiversity Index +
+San Jose + +4 + +0 + +4 + +0 + +0 + +0 + +4 + +12 +
+Alajuela + +4 + +4 + +4 + +2 + +4 + +0 + +2 + +20 +
+Moravia + +4 + +4 + +4 + +3 + +4 + +0 + +4 + +23 +
+Paraiso + +4 + +4 + +3 + +4 + +4 + +4 + +1 + +24 +
+Poas + +4 + +4 + +0 + +3 + +4 + +2 + +0 + +17 +
+Mora + +4 + +4 + +0 + +4 + +4 + +4 + +0 + +20 +
+Alvarado + +4 + +4 + +0 + +3 + +4 + +4 + +0 + +19 +
+Oreamuno + +4 + +4 + +4 + +4 + +4 + +0 + +0 + +20 +
+Heredia + +4 + +4 + +4 + +4 + +4 + +1 + +4 + +25 +
+Tibas + +2 + +3 + +4 + +0 + +0 + +0 + +4 + +13 +
+Vasquez de Coronado + +4 + +4 + +3 + +4 + +4 + +0 + +4 + +23 +
+Atenas + +4 + +4 + +0 + +3 + +4 + +0 + +0 + +15 +
+Desamparados + +4 + +4 + +3 + +1 + +4 + +0 + +3 + +19 +
+Aserri + +4 + +4 + +0 + +3 + +4 + +1 + +1 + +17 +
+Santo Domingo + +4 + +3 + +4 + +0 + +4 + +0 + +3 + +18 +
+El Cuarco + +4 + +4 + +0 + +4 + +4 + +0 + +1 + +17 +
+Montes de Oca + +4 + +3 + +4 + +0 + +2 + +0 + +4 + +17 +
+Goicoechea + +4 + +4 + +4 + +2 + +4 + +0 + +4 + +22 +
+Cartago + +4 + +4 + +4 + +4 + +4 + +0 + +2 + +22 +
+San Pablo + +4 + +2 + +4 + +0 + +2 + +1 + +4 + +17 +
+Curridabat + +4 + +1 + +4 + +1 + +1 + +0 + +4 + +15 +
+Alajuelita + +4 + +4 + +4 + +4 + +4 + +0 + +2 + +22 +
+Barva + +4 + +4 + +4 + +4 + +4 + +1 + +2 + +23 +
+Belen + +4 + +1 + +4 + +0 + +1 + +0 + +3 + +13 +
+Escazu + +4 + +4 + +4 + +4 + +4 + +2 + +2 + +24 +
+Flores + +4 + +3 + +4 + +0 + +0 + +0 + +2 + +13 +
+La Union + +4 + +4 + +0 + +4 + +4 + +0 + +3 + +19 +
+San Isidro + +4 + +4 + +4 + +2 + +4 + +1 + +2 + +21 +
+San Rafael + +4 + +4 + +4 + +3 + +4 + +0 + +2 + +21 +
+Santa Ana + +4 + +4 + +4 + +4 + +4 + +1 + +1 + +22 +
+Santa Barbara + +4 + +4 + +4 + +4 + +4 + +1 + +1 + +22 +
+
+
+ + + + +
+
+ + + + +
+ + + + + + + + + + + + + + + + diff --git a/baseline-indicators/biodiversity/reports/UrbanShift-Biodiversity-SanJose.Rmd b/baseline-indicators/biodiversity/reports/UrbanShift-Biodiversity-SanJose.Rmd index e03fe76..3854b0f 100644 --- a/baseline-indicators/biodiversity/reports/UrbanShift-Biodiversity-SanJose.Rmd +++ b/baseline-indicators/biodiversity/reports/UrbanShift-Biodiversity-SanJose.Rmd @@ -33,6 +33,8 @@ library(knitr) library(kableExtra) library(reactable) library(readxl) +library(downloadthis) + col_BoldRiverBlue = "#242456" col_BoldSunYellow = "#FFD450" col_BoldGrassGreen = "#2A553E" @@ -683,7 +685,7 @@ biodiversity_baseline_indicators_geo = boundary_municipality %>% ``` -### I-1. Porportion of natural areas +### I-1. Proportion of natural areas Natural ecosystems contain more species than human-altered landscapes, hence, the higher the percentage of natural areas compared to that of the total city area gives an indication of the amount of biodiversity. @@ -794,25 +796,36 @@ leaflet(height = 500, width = "100%") %>% labFormat = labelFormat(suffix = "")) %>% # Layers control addLayersControl( - baseGroups = c("CartoDB","OSM"), + baseGroups = c("OSM","CartoDB"), overlayGroups = c("Porportion of natural areas","Porportion of natural areas Score"), options = layersControlOptions(collapsed = FALSE) ) %>% hideGroup("Porportion of natural areas Score") +# prepare chart colors + +value_vector = biodiversity_baseline_indicators_geo %>% + drop_na(I1_percent_natural_areas_score) %>% + arrange(desc(I1_percent_natural_areas_score)) %>% + pull(I1_percent_natural_areas_score) %>% + as.numeric() + +color_vector = pal_score(value_vector) + # plot chart biodiversity_baseline_indicators_geo %>% arrange(desc(I1_percent_natural_areas_value)) %>% plot_ly(height = 500, width = 900) %>% add_trace(x = ~factor(Municipality), y = ~I1_percent_natural_areas_value, - marker = list(color = col_BoldGrassGreen), + marker = list(color = color_vector), type = "bar", orientation = "v") %>% layout(title = "Percent of natural areas (2020)", - xaxis = list(title = '', categoryorder = "array",categoryarray = ~I1_percent_natural_areas_value), - yaxis = list(title = 'Percent of natural area (%)')) + xaxis = list(title = '', categoryorder = "array",categoryarray = + ~I1_percent_natural_areas_value), + yaxis = list(title = '')) ``` ### I-2. Connectivity measures or ecological networks to counter fragmentation @@ -907,24 +920,33 @@ leaflet(height = 500, width = "100%") %>% labFormat = labelFormat(suffix = "")) %>% # Layers control addLayersControl( - baseGroups = c("CartoDB","OSM"), + baseGroups = c("OSM","CartoDB"), overlayGroups = c("Connectivity Value","Connectivity Score"), options = layersControlOptions(collapsed = FALSE) ) %>% hideGroup("Connectivity Score") +# prepare chat colors +value_vector = biodiversity_baseline_indicators_geo %>% + drop_na(I2_connectivity_value) %>% + arrange(desc(I2_connectivity_value)) %>% + pull(I2_connectivity_score) %>% + as.numeric() + +color_vector = pal_score(value_vector) + # plot chart biodiversity_baseline_indicators_geo %>% arrange(desc(I2_connectivity_value)) %>% plot_ly(height = 500, width = 900) %>% add_trace(x = ~factor(Municipality), y = ~I2_connectivity_value, - marker = list(color = col_BoldGrassGreen), + marker = list(color = color_vector), type = "bar", orientation = "v") %>% layout(title = "Connectivity measures (2020)", xaxis = list(title = '', categoryorder = "array",categoryarray = ~I2_connectivity_value), - yaxis = list(title = 'Connectivity value (%)')) + yaxis = list(title = '')) ``` ### I-3 Native biodiversity in built-up areas (bird species) @@ -1002,31 +1024,41 @@ leaflet(height = 500, width = "100%") %>% labFormat = labelFormat(suffix = "")) %>% # Layers control addLayersControl( - baseGroups = c("CartoDB","OSM"), + baseGroups = c("OSM","CartoDB"), overlayGroups = c("Native biodiversity in buitl-up areas value","Native biodiversity in buitl-up areas score"), options = layersControlOptions(collapsed = FALSE) ) %>% hideGroup("Native biodiversity in buitl-up areas score") +# prepare chat colors +value_vector = biodiversity_baseline_indicators_geo %>% + drop_na(I3_percent_birds_built_area_value) %>% + arrange(desc(I3_percent_birds_built_area_value)) %>% + pull(I3_percent_birds_built_area_score) %>% + as.numeric() + +color_vector = pal_score(value_vector) + # plot chart biodiversity_baseline_indicators_geo %>% arrange(desc(I3_percent_birds_built_area_value)) %>% plot_ly(height = 500, width = 900) %>% add_trace(x = ~factor(Municipality), y = ~I3_percent_birds_built_area_value, - marker = list(color = col_BoldGrassGreen), + marker = list(color = color_vector), type = "bar", orientation = "v") %>% layout(title = "Native biodiversity in buitl-up areas (2020)", - xaxis = list(title = '', categoryorder = "array",categoryarray = ~I3_percent_birds_built_area_value), - yaxis = list(title = 'Percent of birds in built-up areas (%)')) + xaxis = list(title = '', categoryorder = "array",categoryarray = + ~I3_percent_birds_built_area_value), + yaxis = list(title = '')) ``` ### I-4 Change in number of native species (vascular plants) ### I-5 Change in number of native species (birds) -### I-6 Change in number of native species (anthropods) +### I-6 Change in number of native species (arthropods) ### I-7 Habitat restoration ### I-8 Proportion of protected natural areas @@ -1104,24 +1136,33 @@ leaflet(height = 500, width = "100%") %>% labFormat = labelFormat(suffix = "")) %>% # Layers control addLayersControl( - baseGroups = c("CartoDB","OSM"), + baseGroups = c("OSM","CartoDB"), overlayGroups = c("Protected area value","Protected area score"), options = layersControlOptions(collapsed = FALSE) ) %>% hideGroup("Protected area score") +# prepare chat colors +value_vector = biodiversity_baseline_indicators_geo %>% + drop_na(I8_percent_protected_area_value) %>% + arrange(desc(I8_percent_protected_area_value)) %>% + pull(I8_percent_protected_area_score) %>% + as.numeric() + +color_vector = pal_score(value_vector) + # plot chart biodiversity_baseline_indicators_geo %>% arrange(desc(I8_percent_protected_area_value)) %>% plot_ly(height = 500, width = 900) %>% add_trace(x = ~factor(Municipality), y = ~I8_percent_protected_area_value, - marker = list(color = col_BoldGrassGreen), + marker = list(color = color_vector), type = "bar", orientation = "v") %>% layout(title = "Percent of protected areas (2020)", xaxis = list(title = '', categoryorder = "array",categoryarray = ~I8_percent_protected_area_value), - yaxis = list(title = 'Percent of protected areas (%)')) + yaxis = list(title = '')) @@ -1206,24 +1247,33 @@ leaflet(height = 500, width = "100%") %>% labFormat = labelFormat(suffix = "")) %>% # Layers control addLayersControl( - baseGroups = c("CartoDB","OSM"), + baseGroups = c("OSM","CartoDB"), overlayGroups = c("Permeable area value","Permeable area score"), options = layersControlOptions(collapsed = FALSE) ) %>% hideGroup("Permeable area score") +# prepare chat colors +value_vector = biodiversity_baseline_indicators_geo %>% + drop_na(I10_water_value) %>% + arrange(desc(I10_water_value)) %>% + pull(I10_water_score) %>% + as.numeric() + +color_vector = pal_score(value_vector) + # plot chart biodiversity_baseline_indicators_geo %>% arrange(desc(I10_water_value)) %>% plot_ly(height = 500, width = 900) %>% add_trace(x = ~factor(Municipality), y = ~I10_water_value, - marker = list(color = col_BoldGrassGreen), + marker = list(color = color_vector), type = "bar", orientation = "v") %>% layout(title = "Regulation of quantity of water (2020)", xaxis = list(title = '', categoryorder = "array",categoryarray = ~I10_water_value), - yaxis = list(title = 'Percent of permeable areas (%)')) + yaxis = list(title = '')) ``` ### I-11 Climate regulation: carbon storage and cooling effect of vegetation @@ -1306,24 +1356,33 @@ leaflet(height = 500, width = "100%") %>% labFormat = labelFormat(suffix = "")) %>% # Layers control addLayersControl( - baseGroups = c("CartoDB","OSM"), + baseGroups = c("OSM","CartoDB"), overlayGroups = c("Recreational services value","Recreational services score"), options = layersControlOptions(collapsed = FALSE) ) %>% hideGroup("Recreational services score") +# prepare chat colors +value_vector = biodiversity_baseline_indicators_geo %>% + drop_na(I12_recreational_services_value) %>% + arrange(desc(I12_recreational_services_value)) %>% + pull(I12_recreational_services_score) %>% + as.numeric() + +color_vector = pal_score(value_vector) + # plot chart biodiversity_baseline_indicators_geo %>% arrange(desc(I12_recreational_services_value)) %>% plot_ly(height = 500, width = 900) %>% add_trace(x = ~factor(Municipality), y = ~I12_recreational_services_value, - marker = list(color = col_BoldGrassGreen), + marker = list(color = color_vector), type = "bar", orientation = "v") %>% layout(title = "Recreational services (2020)", xaxis = list(title = '', categoryorder = "array",categoryarray = ~I12_recreational_services_value), - yaxis = list(title = 'Recreational services')) + yaxis = list(title = '')) ``` ### I-13 Proximity to parks @@ -1401,24 +1460,164 @@ leaflet(height = 500, width = "100%") %>% labFormat = labelFormat(suffix = "")) %>% # Layers control addLayersControl( - baseGroups = c("CartoDB","OSM"), + baseGroups = c("OSM","CartoDB"), overlayGroups = c("Proximity to parks value","Proximity to parks score"), options = layersControlOptions(collapsed = FALSE) ) %>% hideGroup("Proximity to parks score") +# prepare chat colors +value_vector = biodiversity_baseline_indicators_geo %>% + drop_na(I13_proximity_parks_value) %>% + arrange(desc(I13_proximity_parks_value)) %>% + pull(I13_proximity_parks_score) %>% + as.numeric() + +color_vector = pal_score(value_vector) + # plot chart biodiversity_baseline_indicators_geo %>% arrange(desc(I13_proximity_parks_value)) %>% plot_ly(height = 500, width = 900) %>% add_trace(x = ~factor(Municipality), y = ~I13_proximity_parks_value, - marker = list(color = col_BoldGrassGreen), + marker = list(color = color_vector), type = "bar", orientation = "v") %>% layout(title = "Proximity to parks", xaxis = list(title = '', categoryorder = "array",categoryarray = ~I13_proximity_parks_value), - yaxis = list(title = 'Proximity to parks')) + yaxis = list(title = '')) ``` +## Biodiversity index synthesis + +The Singapore biodiversity Index is computed as the sum of the different biodiversity indicators: + +$$BiodiversityIndex_{city} = \sum_{i=1}^{nb_{indicators}} (I1_{city},I2_{city},...,In_{city})$$ + +```{r Sum-score-table, warning=FALSE, message=FALSE, echo = FALSE, eval = TRUE} +biodiversity_baseline_scores = biodiversity_baseline_indicators_geo %>% + as.data.frame() %>% + drop_na(I1_percent_natural_areas_value) %>% + mutate(I1_percent_natural_areas_score = as.numeric(I1_percent_natural_areas_score), + I2_connectivity_score = as.numeric(I2_connectivity_score), + I3_percent_birds_built_area_score = as.numeric(I3_percent_birds_built_area_score), + I8_percent_protected_area_score = as.numeric(I8_percent_protected_area_score), + I10_water_score = as.numeric(I10_water_score), + I12_recreational_services_score = as.numeric(I12_recreational_services_score), + I13_proximity_parks_score = as.numeric(I13_proximity_parks_score)) %>% + dplyr::select(Municipality, + "I1 - Percent of natural areas" = I1_percent_natural_areas_score, + "I2 - Connectivity" = I2_connectivity_score, + "I3 - Birds in built areas" = I3_percent_birds_built_area_score, + "I8 - Protected areas" = I8_percent_protected_area_score, + "I10 - Water" = I10_water_score, + "I12 - Recreational services" = I12_recreational_services_score, + "I13 - Proximity to parks" = I13_proximity_parks_score) %>% + mutate("Biodiversity Index" = rowSums(.[2:8])) %>% + mutate_at(vars("I1 - Percent of natural areas":"I13 - Proximity to parks"), ~ cell_spec( + ., "html", + background = ifelse(. >= 4, "green", ifelse(. >= 3, "yellowgreen", ifelse(. >= 2, "yellow", ifelse(. >= 1, "orange", "red")))) + )) %>% + mutate_at(vars("Biodiversity Index"), ~ cell_spec( + ., "html", + font_size = "x-large", + background = ifelse(. >= 28, "green", ifelse(. >= 21, "yellowgreen", ifelse(. >= 14, "yellow", ifelse(. >= 7, "orange", "red")))) + )) + +biodiversity_baseline_scores %>% + kable(format = "html", escape = FALSE) %>% + kable_styling("striped", full_width = FALSE) %>% + scroll_box(width = "100%", height = "700px") + +``` + + +```{r Sum-score-map, warning=FALSE, message=FALSE, echo = FALSE, eval = TRUE} + +# map +biodiversity_baseline_scores = biodiversity_baseline_indicators_geo %>% + as.data.frame() %>% + drop_na(I1_percent_natural_areas_value) %>% + mutate(I1_percent_natural_areas_score = as.numeric(I1_percent_natural_areas_score), + I2_connectivity_score = as.numeric(I2_connectivity_score), + I3_percent_birds_built_area_score = as.numeric(I3_percent_birds_built_area_score), + I8_percent_protected_area_score = as.numeric(I8_percent_protected_area_score), + I10_water_score = as.numeric(I10_water_score), + I12_recreational_services_score = as.numeric(I12_recreational_services_score), + I13_proximity_parks_score = as.numeric(I13_proximity_parks_score)) %>% + dplyr::select(Municipality, + "I1 - Percent of natural areas" = I1_percent_natural_areas_score, + "I2 - Connectivity" = I2_connectivity_score, + "I3 - Birds in built areas" = I3_percent_birds_built_area_score, + "I8 - Protected areas" = I8_percent_protected_area_score, + "I10 - Water" = I10_water_score, + "I12 - Recreational services" = I12_recreational_services_score, + "I13 - Proximity to parks" = I13_proximity_parks_score) %>% + mutate("Biodiversity Index" = rowSums(.[2:8])) + +# join with geo +biodiversity_baseline_scores_geo = boundary_municipality %>% + left_join(biodiversity_baseline_scores, + by = c("Municipality")) + + +# define color palette for I1 levels +pal_Index <- colorNumeric(palette = "RdYlGn", + domain = biodiversity_baseline_scores_geo$`Biodiversity Index` , + na.color = "transparent", + revers = FALSE) + +# define labels + +labels_Index <- sprintf("%s
%s: %s", + biodiversity_baseline_scores_geo$shapeName.1, + "Biodiversity index", + biodiversity_baseline_scores_geo$`Biodiversity Index`) %>% + lapply(htmltools::HTML) + +# plot map +leaflet(height = 500, width = "100%") %>% + addTiles() %>% + addProviderTiles("OpenStreetMap.France", group = "OSM") %>% + addProviderTiles(providers$CartoDB.DarkMatter , group = "CartoDB") %>% + addPolygons(data = biodiversity_baseline_scores_geo, + group = "Biodiversity index", + fillColor = ~pal_Index(`Biodiversity Index`), + weight = 1, + opacity = 1, + color = "grey", + fillOpacity = 0.7, + label = labels_Index, + highlightOptions = highlightOptions(color = "black", weight = 2, + bringToFront = FALSE), + labelOptions = labelOptions( + style = list("font-weight" = "normal", padding = "3px 6px"), + textsize = "15px", + direction = "auto")) %>% + addLegend(pal = pal_Index, + values = biodiversity_baseline_scores_geo$`Biodiversity Index`, + opacity = 0.9, + title = "Biodiversity index (2020)", + position = "topright", + labFormat = labelFormat(suffix = "")) %>% + # Layers control + addLayersControl( + baseGroups = c("OSM","CartoDB"), + overlayGroups = c("Biodiversity index"), + options = layersControlOptions(collapsed = FALSE) + ) +``` + +```{r Sum-score-table-download, warning=FALSE, message=FALSE, echo = FALSE, eval = TRUE} +biodiversity_baseline_scores %>% + download_this( + output_name = "Biodiversity Index - San Jose", + output_extension = ".csv", + button_label = "Download data as csv", + button_type = "default", + has_icon = TRUE, + icon = "fa fa-save" + ) +``` \ No newline at end of file diff --git a/baseline-indicators/biodiversity/reports/UrbanShift-Biodiversity-SanJose.html b/baseline-indicators/biodiversity/reports/UrbanShift-Biodiversity-SanJose.html index 4adce8b..9f5c4bc 100644 --- a/baseline-indicators/biodiversity/reports/UrbanShift-Biodiversity-SanJose.html +++ b/baseline-indicators/biodiversity/reports/UrbanShift-Biodiversity-SanJose.html @@ -11,7 +11,7 @@ - + UrbanShift Biodiversity Indicators @@ -61,6 +61,7 @@ +