1 Introduction

As a part of the Data Portal for Cities project, this work aims at providing a prototype tool for helping cities in climate hazards adaptation planning.

The following document describes the methodological framework used for computing indicators on amenity exposure to heat hazards.

2 Data sources

Dataset Source Data integration scripts Data description Data storage path Format Data exploration
Administrative boundaries geoBoundaries database GeoLayers.Administrative boundaries download from GCS geojson
Amenities Open Street Map integrate_amenity_data.ipynb GeoLayers.Amenity download from GCS geojson
Amenities sectors global covenant of mayor GCRF GeoLayers.Amenity download from GCS csv
Land Surface Temperature Landsat integrate_lst_data.js GeoLayers.Land Surface Temperature download from GCS GeoTIFF

2.1 Administrative boundaries

Two data sources are used for collecting the administrative boundaries regarding the selected cities: the geoBoudnaries database and Open Street Map.

2.2 Amenities

2.2.1 Amenity data

Amenity data is provided by Open Street Map and collected by the mean of the Overpass API. OpenStreetMap’s free tagging system allows the map to include an unlimited number of attributes describing each feature. These tags are represented in key: value structure. The key, is used to describe a topic, category, or type of feature (e.g., highway or name). The value provides detail for the key-specified feature. For example, school features can be tagged with key = amenity and value = school.

For each city, we collected the location and attributes of available amenities situated within the city boundary. The data is stored in Google Cloud Storage bucket.

2.2.2 Sector categories

In order to provide exposure indicators by sector, we associated each amenity with a sector name based on the sector taxonomy provided by the CRF document of GCOM. This mapping between the amenities and sector categories is provided by the mean of a mapping file stored in the datalake.

2.3 Land Surface Temperature (LST)

Land Surface Temperature estimation within the city boundary is based on Landsat data provided with high spatial resolution (30m). In order to collect this data, we used an open source library provided by Sofia L. Ermida based on the Google Earth Engine (Link to the artilce). We extracted for each city an average heat value for a 3 months period corresponding to the hot season. Here is an example of parameters used for collecting LST data for the city of ‘Marroco’:

var satellite = 'L8';
var date_start = '2018-03-01';
var date_end = '2021-04-30';
var month_start = 6;
var month_end = 9;
var use_ndvi = true;

3 Data workflow

Data Workflow

4 Methdology

4.1 Amenity exposure

4.1.1 Amenity exposure definition

Amenity exposure to heat may be quantified simply as the heat value (as collected from Landsat) at the amenity location. Since our goal consists of identifying the most exposed amenities within each city, we need to define an indicator that may inform us about within city variability of exposed amenities.

Two methods have been explored for quantifying amenity exposure to heat:

  • Deviation from city average heat value: It consists of comparing each amenity heat value with the city average heat value (considered hence as exposure threshold). \[Exposure_{amenity} = (\frac {Heat_{amenity}}{HeatThreshold_{city}} * 100) - 100\]
  • Deviation from average amenity heat value: It consists of computing for each amenity a deviation ratio to the whole average amenities heat.

\[Exposure_{amenity} = (\frac {Heat_{amenity}}{HeatThreshold_{city}} * 100) - 100\]

4.1.2 Sector exposure definition

Two metrices are provided in order to characterize sectors’ exposure to heat:

  • Average deviation ratio by sector: It consists of computing the average deviation ratio of amenities at the sector level.

  • Percent of exposed amenities: It consists of computing for each sector the percent of amenities with heat value above the average.

4.1.3 Exposure Index

Amenity exposure level depends on two components: Number of amenities and heat deviation ratio. In order to identify the location of exposure hotspots, we propose here to compute an exposure index by spatially weighting the number of amenities by the heat deviation ratio as expressed by the following equations:

  • Computing exposure index values:

\[ExposureLevel_{cell} =\sum_{x = i}^{nb_{cells}}NumberOfAmenities_{i}*ExposureAmenity_{i}\]

  • Normalizing the exposure index:

\[ExposureIndex_{cell} = \frac {ExposureLevel_{cell} - min(ExposureLevel_{city})}{max(ExposureLevel_{city}) - min(ExposureLevel_{city})}\]

4.1.4 Limitations & Perspectives

  • Amenity data coverage: In this analysis we used amenity deata provided by Open Street Map API. Since the data is not complete and may differ between cities, our results are biased by data coverage. More efforts are then needed for evaluating data coverage and integratin other datasources such as Google Places API or Google Open Buildings dataset.

  • Definition of heat hazard

  • Definition of the most relevant administrative level in the boundaries data

4.2 Population exposure

5 Technical documentation

5.1 Dashboard inputs

5.1.1 Datasets

dataset name description format storage
boundary Administrative boundaries of pilot cities geojson
land_surface_temperature Raster layer of Land Surface Temperature within the city boundary box geotiff
amenity_exposure_lst Amenities’ exposure to heat csv
city_pop Raster layer of Population data within the city boundary box geotiff

5.1.1.1 boundary

field name field description
country_iso3 Code ISO 3 of the country
city_id City identifier: A concatenation of Country code iso 3 with the city name (e.g., PHL-Makati)
city_name Name of the city
boundary_data_source Source name of the boundary data (e.g., osm)
geometry Coordinates of the boundary polygons (CRS: ESPG 4326)

5.1.1.2 amenity_exposure_lst

field name field description
id Feature id based on OSM data
city_name Name of the city
cityName Name of the city
country_iso Code ISO 3 of the country
country_iso3 Code ISO 3 of the country
city_id City identifier (Country code iso 3 - city name)
latitude Latitude of the geographical position of the amenity (CRS: ESPG 4326)
longitude longitude of the geographical position of the amenity (CRS: ESPG 4326)
geometry Amenity coordinates (CRS: ESPG 4326)
feature_key feature key based on OSM taxonomy (e.g., amenity, building…)
featureCategory feature key based on OSM taxonomy (e.g., amenity, building…)
objectType feature key based on OSM taxonomy (e.g., amenity, building…)
feature_category feature category based on OSM taxonomy (e.g., Entertainment_Art_Culture, Transportation, Healthcare…)
featureType feature category based on OSM taxonomy (e.g., Entertainment_Art_Culture, Transportation, Healthcare…)
sector_name Name of the sector category based on OSM taxonomy
gcom_sector_name Name of the sector category based on GCOM taxonomy
city_lst_avg Average heat value within the city boundary
city_lst_perc_90 90th percentile of heat value within the city boundary
exposure_lst_mean Average heat value at the amenity position based on Land Surface Temperature estimation
lst_value_dev_city_mean Deviation from city average heat value (Celsus)
lst_pecent_dev_city_mean Deviation ratio from city average heat value (percent)
lst_dev_city_mean_exposure_class Amenity exposure class (Exposed; Not Exposed). Amenity is considered as exposed if the average heat value at the amenity location (exposure_lst_mean) is higher than the city average heat value (city_lst_avg)
lst_value_dev_city_perc_90 Deviation from city 90th percentile heat value (Celsus)
lst_pecent_dev_city_perc_90 Deviation ratio from city 90th percentile heat value (percent)
lst_dev_city_perc90_exposure_class Amenity exposure class (Exposed; Not Exposed). Amenity is considered as exposed if the average heat value at the amenity location (exposure_lst_mean) is higher than the city 90th percentile heat value (city_lst_perc_90)
integrationDate Date of amenity data collection from OSM API
projectName Name of the project concerned by the collected amenities
heat_dev_from_amenities Heat deviation value from amenities’ average heat

5.1.2 Filters

filter category variable name variable description
City available_cities A list of available cities: distinct city_name from amenity_exposure_lst table
Hazard available_hazards A list of available hazards: Only heat hazard is implemented
Period available_periods A list of available periods: Only 2020-2021 period is implemented
Amenity sectors available_amenity_sectors A list of available amenities’ sectors: distinct gcom_sector_name from amenity_exposure_lst table
Population category available_pop_categories A list of available population categories: All, Young (<20), Elderly (>60), Men, Women
Heat threshold slider slider_min_heat Minimum amenities’ heat value depending on the selected city: (min(amenity_exposure_lst) from amenity_exposure_lst table)
Heat threshold slider slider_max_heat Maximum amenities’ heat value depending on the selected city: (max(amenity_exposure_lst) from amenity_exposure_lst table)
Heat threshold slider slider_value_heat Average amenities’ heat value depending on the selected city: (mean(amenity_exposure_lst) from amenity_exposure_lst table)

5.1.3 User inputs

Tab filter category filter id filter description
Amenity exposure City selection City Select a city among a list of available cities (by default city = PHL-Makati)
Amenity exposure Hazard selection Hazard Select a hazard among a list of available hazards (by default hazard = Heat)
Amenity exposure Period selection Period Select a Period among a list of available periods (by default hazard = 2020-2021)
Amenity exposure Amenity sectors’ selection Sector Select a Period among a list of available sectors (available_amenity_sectors). By default, all amenities’ sectors are selected.
Amenity exposure Heat threshold selection heat_threshold Select a heat threshold value to consider for assessing amenities’ exposure. By default, average amenities’ heat value is selected (slider_value_heat).

5.2 Dashboard outputs

5.2.1 Amenity exposure tab

5.2.1.1 Datasets

5.2.1.2 city_boundary

  • This dataset is generated by filtering the boudary geojson file based on the selected city.

5.2.1.3 city_amenity

  • This dataset is generated by filtering the input amenity_exposure_lst table based on the following selected filters: city, sectors.

  • Two columns are added in this table in order to recompute amenities’ exposure based on the user defined heat threshold

    • deviation_from_threshold: Amenity heat deviation ratio from selected heat threshold. \(DeviationFromThreshold_{Amenity} = (\frac {Heat_{amenity}}{HeatThreshold_{user}} * 100) - 100\)
    • exposure_class: We attribute an exposure class to each amenity deoending on the deviation ratio from heat threshold.
deviation_from_threshold exposure_class
<= 0 0-Low
]0, 10] 1-Moderate
> 10 2-High

5.2.1.4 Main indicators

Indicator name Indicator id Indicator description
Amenity average heat value (selected sectors) selected_amenities_avg_heat Average heat value of amenities based on selected city and sectors of interest
Selected amenities heat deviation from all amenities selected_amenities_deviation_heat_value Average heat deviation of selected amenities from all amenities located within the city boundary
Selected amenities heat deviation ratio from all amenities selected_amenities_deviation_heat_ratio Heat deviation ratio of selected amenities from all amenities located within the city boundary

5.2.1.5 Map

  • Layer 1: Land Surface Temperature:

This layer shows the spatial distribution of land surface temperature within the city boundary at 30m resolution.

  • Layer 2: Amenity exposure value:

  • Layer 2: Amenity exposure class:

5.2.1.6 Amenity exposure table

5.2.1.7 Sector exposure bar chart

5.2.1.8 Narrative summary

5.2.2 Population exposure tab