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5. Detailed Setup

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zarayousefi edited this page Oct 2, 2024 · 8 revisions

Additional general information on configuration, analysis, reporting and comparison steps using the GHSCI software is provided below, including guidance on what to do if you get stuck.

Configuration

Study regions

Before commencing analysis, your study regions will need to be configured with details of your downloaded data, the metadata used to document this data, and parameters to guide the software's usage of this data in analyses.

The configuration files are text files using the YAML (.yml) format. They can be opened and modified using a text editor to define region-specific details, including which datasets are being used, where they were sourced from, and how they should be interpreted. Region configuration files are located within the configuration/regions sub-folder. An example region configuration for Las Palmas de Gran Canaria (for which data supporting analysis is included) has been provided in the file process/configuration/regions/example_ES_Las_Palmas_2023.yml. This can also be viewed online here. At the top of the configuration file are some instructions that describe how to understand and modify the file.

image

New regions can be added by using the configuration utility functions described elsewhere that initialise a new region configuration file using a city codename at process/configuration/regions/_codename_.yml. This file can be edited using a text editor, or within Jupyter Lab.

Study region template

Click to view the description of region configuration parameters 
###########################################################
## Study region configuration template v5.0.0 (3 Apri 2024)

## This configuration file uses the YAML format (https://yaml.org/) to describe the data sources, parameters and metadata used to analyse and generate urban indicator resources using the Global Healthy and Sustainable City Indicators software (https://global-healthy-liveable-cities.github.io/).

## Text beginning with a double hash symbol ("##") is are comments (used to provide descriptions of how to complete the item immediately below).  Text beginning with a single has symbol ("#") are commented out section of code that may optionally be uncommented as per the provided instructions.

## Optional sections that contain parameters which may be uncommented are marked with a series of hash symbols ("###########") at their start and end lines.

## If entering or modifying a parameter
## - leave a space after the semi-colon
## - indents (ie. 4 spaces) signify a nested sub-list of configurable parameters

## It is recommended to view or edit this file in an application providing syntax highlighting, for example, the provided Jupyter Lab web app:
## - type 'lab' at the GHSCI prompt
## - navigate to the "process/configuration/region/" folder in the browser pane
## - double click on the YAML configuration file with the name ending in .yml you wish to edit
###########################################################

## Full study region name, e.g. Las Palmas de Gran Canaria
name:
## Target year for analysis, e.g. 2023
year:
## Fully country name, e.g. España
country:
## Two character country code (ISO3166 Alpha-2 code), e.g. ES
country_code:
## Continent name, e.g. Europe
continent:
## Projected coordinate reference system (CRS) metadata
crs:
    ## name of the projected (i.e. units in metres) coordinate reference system (CRS), e.g. REGCAN95 / LAEA Europe
    name:
    ## acronym of the standard catalogue defining this CRS, eg. EPSG
    standard:
    ## Projected CRS spatial reference identifier (SRID) integer that identifies this CRS according to the specified standard, e.g. 5635 (see https://spatialreference.org/, or search for what is commonly used in your city or country; e.g. a national CRS like those listed at https://en.wikipedia.org/wiki/List_of_national_coordinate_reference_systems )
    srid:
## Study region boundary metadata
study_region_boundary:
    ## Path to downloaded data relative to project data directory, or urban_query:variable='value')
    ## e.g. to load a file (geojson, shp, geopackage):
    ## "region_boundaries/Example/Las Palmas de Gran Canaria - Centro Nacional de Información Geográfica - WGS84 - EPSG4326.geojson"
    ## e.g. to use the urban region and urban query specified elsewhere in this configuration file:
    ## urban_query
    ## e.g. to query an attribute for a specific layer in a geopackage
    ## region_boundaries/your_geopackage.gpkg:layer_name -where "some_attribute=='some_value'"
    ## e.g. to query an attribute for a specific layer in a shapefile
    ## region_boundaries/your_shapefile.shp -where "some_attribute=='some_value'"
    data:
    ## The name of the provider of this data, e.g. Centro Nacional de Información Geográfica
    source:
    ## Publication date for study region area data source, or date of currency, e.g. 2019-02-01
    publication_date:
    ## URL for the source dataset, or its provider, e.g. https://datos.gob.es/en/catalogo/e00125901-spaignllm
    url:
    ## Licence for the data, e.g. CC-BY-4.0
    licence:
    ## Whether the provided study region boundary will be further restricted to an urban area defined by its intersection with a linked urban region dataset (see urban_region), e.g. true
    ghsl_urban_intersection: false
    ## A formal citation for this data, For example, "Instituto Geográfico Nacional (2019). Base de datos de divisiones administrativas de España. https://datos.gob.es/en/catalogo/e00125901-spaignllm."
    citation:
    ## Optional notes of relevance for understanding this study region's context
    notes:
###########
## Optional custom aggregation to additional areas of interest (e.g. neighbourhoods, suburbs, specific developments); uncomment and complete to use
# custom_aggregations:
    ## Name for this aggregation layer
    ## The name is followed by a colon, indicating that a list of detail follows
    # custom_layer_using_population_grid:
        ## path to data relative to project data folder
        # data:
        ## The field used as a unique identifier
        # id: 'Codigo'
        ## A list of column field names to be retained
        # keep_columns: Denominaci, cod_postal
        ## The indicator layer to be aggregated ("point" or "grid")
        ## Aggregation is based on the average of intersecting results
        ## unless the agg_distance parameter is defined (see alternative example below)
        # aggregation_source: grid
        ## The variable used for weighting (e.g. 'pop_est' for population when using the grid; leave blank or "false" if using sample points)
        # weight: pop_est
        ## An optional note to provide details about what this aggregation represents
        # note: "Example of aggregating indicators for high school catchment districts within Las Palmas, using the intersection with the population grid and taking the population-weighted average of indicators.  Boundary data was derived from data sourced from the open data portal of the Gobierno de Canarias under CC BY 4.0 licence terms: https://opendata.sitcan.es/dataset/centros-educativos/resource/ea650255-c6ea-48c1-84e8-547735624017 (last updated 31 May 2023)."
    ## an example for aggregating for buildings represented in OpenStreetMap
    # buildings_osm_30m:
        # data: "OSM:building is not NULL"
        # keep_columns: building
        ## Distance within metres to use for taking average when aggregating
        ## (see note)
        # aggregate_within_distance: 30
        # aggregation_source: point
        # note: "Example of aggregating using buildings extracted from the configured OpenStreetMap data, taking the average of sample point estimates taken along the pedestrian network within 30m.  This has been done because the point indicators were sampled along the pedestrian network and are therefore unlikely to intersect with buildings.  By taking the average of points within some reasonable distance, the result is like a moving window average that should provide a reasonable representation of the immediate neighbourhood milieu surrounding the building."
## Population metadata (raster or vector)
population:
    ## name of the population data
    name: "Global Human Settlements population data: 2020, Mollweide (EU JRC, 2022)"
    ## path relative to project data directory to folder containing tifs, or to vector file
    data_dir:
    ## type of data (e.g. "raster:Int64" or "vector");  e.g. for GHSL-POP, raster:Int64
    data_type:
    ###########
    ## Vector data specific-fields; uncomment if using vector data (e.g. shp, geojson, gpkg)
    ## The column field with population estimates for your population group of interest (may be total, or for a sub-group of interest to be the focus of your indicators)
    # vector_population_data_field:
    ## The field with the total population data within that area (if you are interested in total, should be the same value as vector_population_data_field)
    # population_denominator:
    ###########
    ###########
    ## Raster data specific-fields; comment out if using raster data (i.e. tif)
    ## image resolution;  e.g. for GHSL-POP with 100 metre resolution, 100m
    resolution:
    ## the image band containing the relevant data, e.g. for GHSL-POP, 1
    raster_band:
    ## A value in the image that represents 'no data', e.g. for GHSL-POP, -200
    raster_nodata:
    ###########
    ## Sample points intersecting grid cells with estimated population less than this will be excluded from analysis.  Depending on your population data resolution, you can use this to exclude areas with very low population due to the uncertainty of where anyone might live in that area, or if they do at all.  For example, 1
    pop_min_threshold:
    ## Coordinate reference system metadata for population data.
    ## For example, for GHSL-POP (Mollweide, ESRI, 54009), enter Mollweide
    crs_name:
    ## For example, for GHSL-POP (Mollweide, ESRI, 54009), enter ESRI
    crs_standard:
    ## For example, for GHSL-POP (Mollweide, ESRI, 54009), enter 54009
    crs_srid:
    ## URL for where this data was sourced from
    source_url:
    ## metadata for citation
    ## when it was published (yyyy), e.g. 2023
    year_published:
    ## the year it is intended to represent (yyyy), e.g. 2020
    year_target:
    ## when you retrieved it (yyyymmdd).  This can be useful to record as data can be subject to revision.  e.g. 20230627
    date_acquired:
    ## licence, e.g. "CC BY 4.0"
    licence: CC BY 4.0
    ## citation, e.g. "Schiavina, M; Freire, S; Carioli, A., MacManus, K (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset] doi: 10.2905/D6D86A90-4351-4508-99C1-CB074B022C4A"
    citation:
## OpenStreetMap metadata
OpenStreetMap:
    ## path relative to the project data directory
    data_dir:
    ## the source of the OpenStreetMap data (e.g. Planet OSM, GeoFabrik or OpenStreetMap.fr)
    source:
    ## when it was published (yyyymmdd), e.g. 20230627
    publication_date:
    ## licence (which is most likely ODbL for OpenStreetMap data published since 2012)
    licence: ODbL
    ## the URL from where it was downloaded
    url:
    ## An optional note regarding this data
    note:
## Network analysis related configuration parameters
network:
    #########
    ## Optional network parameters for use in some contexts (eg. island cities)
    ## Whether to only retain the main connected network when retrieving OSM roads (set to "false"; the default, which is appropriate for most settings); or retain network 'islands' if present ("true")
    # osmnx_retain_all: false
    ## Whether to extract the network for the buffered study region. It is recommended to set to 'true' (the default) in most cases.  Setting this to false may be appropriate for true islands, but could be problematic for anywhere else where the network and associated amenities may be accessible beyond the edge of the study region boundary).
    # buffered_region: true
    ## Iterate over and combine polygons (this may be appropriate for a series of islands, like Hong Kong), but in most cases it is recommended to be set as false
    # polygon_iteration: false
    ## Minimum total network distance for subgraphs to retain.  This is a useful parameter for customising analysis for islands, like Hong Kong, but for most purposes, you can leave this blank (the default).
    # connection_threshold:
    #########
    ## Tolerance in metres for cleaning intersections.  If not providing your own data for evaluating intersection density (see below), this is an important methodological choice.  The chosen parameter should be robust to a variety of network topologies in the city being studied.  See https://github.com/gboeing/osmnx-examples/blob/main/notebooks/04-simplify-graph-consolidate-nodes.ipynb. For example, 12
    intersection_tolerance:
    #########
    ## Optionally, data for evaluating intersections can be provided as an alternative to deriving intersections from OpenStreetMap (where available, this may be preferable); uncomment and complete the required fields to do this.
    ## Custom intersection data settings
    # intersections:
        ## path to data relative to the project data directory
        # data: network_data/your_intersection_data.geojson
        ## citation for optional custom intersection data
        # citation: 'Provider of your intersection data.  YYYY.  Name of your intersection data. https://source-url-for-your-data.place'
        ## a note to describe custom intersection data
        # note: 'Uncomment this configuration section to optionally specify an external dataset of intersections.  Otherwise, these are derived using OpenStreetMap and OSMnx using the intersection_tolerance parameter.  If providing intersection data, you can modify this note for it to be included in the metadata, or remove it.
        #########
## Urban region metadata.   An urban region can optionally be defined to supplement the study region definition, e.g. using the Global Human Settlements Layer Urban Centres Database
urban_region:
    ## name for the urban region data, e.g. "Global Human Settlements urban centres: 2015 (EU JRC, 2019)"
    name:
    ## path to data relative to the project data directory, e.g. "urban_regions/GHS_STAT_UCDB2015MT_GLOBE_R2019A_V1_2.gpkg"
    ## Please note, this data has not been provided but can be retrieved. See the citation below.
    data_dir:
    ## licence, e.g. CC BY 4.0
    licence:
    ## citation for this data, this has been prefilled for the GHSL UCDB (2019), but change as required if using
    ## For example, for GHSL UCDB (r2019a), enter: "Florczyk, A. et al. (2019): GHS Urban Centre Database 2015, multitemporal and multidimensional attributes, R2019A. European Commission, Joint Research Centre (JRC). https://data.jrc.ec.europa.eu/dataset/53473144-b88c-44bc-b4a3-4583ed1f547e"
    citation:
    ## A list of additional covariates that can be optionally linked for cities included in the GHSL UCDB
    covariates:
        E_EC2E_T15:
            Units: tonnes per annum
            Unit description: tonnes per annum
            Description: Total emission of CO 2 from the transport sector, using non-short-cycle-organic fuels in 2015
        E_EC2O_T15:
            Units: tonnes per annum
            Unit description: tonnes per annum
            Description: Total emission of CO 2 from the energy sector, using short-cycle-organic fuels in 2015
        E_EPM2_T15:
            Units: tonnes per annum
            Unit description: tonnes per annum
            Description: Total emission of PM 2.5 from the transport sector in 2015
        E_CPM2_T14:
            Units: µg per cubic metre
            Unit description: micrograms per cubic meter
            Description: Total concertation of PM 2.5 for reference epoch 2014
        EL_AV_ALS:
            Units: metres above sea level
            Unit description: metres above sea level
            Description: The average elevation estimated within the spatial domain of the Urban Centre, and expressed in metres above sea level (MASL) (EORC & JAXA, 2017).
        E_KG_NM_LST:
            Units: List of climate classes
            Unit description: List of climate classes
            Description: Semi-colon separated list of names of Köppen-Geiger climate classes, intersecting with the spatial domain of the Urban Centre (1986-2010) (Rubel et al., 2017).
        E_WR_T_14:
            Units: °C
            Unit description: Average temperature in Celsius degrees (°C)
            Description: Average temperature calculated from annual average estimates for a time interval centred on the year 2015 (the interval spans from 2012 to 2015) within the spatial domain of the Urban Centre, and expressed in Celsius degrees (°C) (Harris et al., 2014).
        E_WR_P_14:
            Units: mm
            Unit description: The amount of rain per square meter in one hour (mm)
            Description: Average precipitations calculated from annual average estimates for a time interval centred on the year 2015 (the interval spans from 2012 to 2015) within the spatial domain of the Urban Centre; and expressed in millimetres (mm), the amount of rain per square meter in one hour) (Harris et al., 2014).
## Query used to identify the specific urban region relevant for this region in the Urban Centres database
## GHS or other linkages of covariate data (GHS:variable='value', or path:variable='value' for a dataset with equivalently named fields defined in project parameters for air_pollution_covariates), e.g. GHS:UC_NM_MN=='Las Palmas de Gran Canaria' and CTR_MN_NM=='Spain'
urban_query:
## Additional study region summary covariates to be optionally linked. This is designed to retrieve the list of covariates specifies in the 'urban_region' configuration, either from the configured Global Human Settlements Layer data (enter "urban_query"), or from a CSV file (provide a path relative to the project data directory)
covariate_data:
## Country-level income metadata for additional city context
country_gdp:
    ## Country GDP classification, e.g. lower-middle
    classification:
    ## Citation for the GDP classification, e.g. The World Bank. 2020. World Bank country and lending groups. https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups
    citation:
## Details of custom destinations to use (e.g. as done for Maiduguri, Nigeria), in addition to those from OSM (optional, as required; else, leave blank) file name (located in study region folder), category plain name field, category full name field, Y coordinate, X coordinate, EPSG number, attribution
#########
## Optional custom destinations to import in addition to those from (e.g. as done for Maiduguri, Nigeria).  Uncomment if required.
# custom_destinations:
    ## name of file relative to project data directory
    # file:
    ## destination identifier/name
    # name_field:
    ## destination detailed name or description
    # description_field:
    ## y coordinate
    # lat:
    ## x coordinate
    # lon:
    ## EPSG code
    # epsg:
    ## a citation for this data
    # citation:
    #########
#########
## Optional set up for General Transit Feed Specification (GTFS) transit data.
## GTFS feed data is used to evaluate access to public transport stops with regular weekday daytime service
## For cities with no GTFS feeds identified, this may be left commented out.
#gtfs_feeds:
    ## City-specific parent folder in the 'process/data/transit_feeds' directory
    # folder:
    ## list of zipped GTFS feeds saved in above folder
    # name_of_your_gtfs_zip_file.zip:
        ## Name of agency that published this data
        # gtfs_provider:
        ## Year the data was published
        # gtfs_year:
        ## Source URL for the data
        # gtfs_url:
        ## The start date of a representative period for analysis
        ## (outside school holidays and extreme weather events), e.g. Spring/Summer
        ## for Northern Hemisphere: 20230405
        ## for Southern Hemisphere: 20231008
        # start_date_mmdd:
        ## The start date of a representative period for analysis
        ## (outside school holidays and extreme weather events), e.g. Spring/Summer
        ## for Northern Hemisphere: 20230605
        ## for Southern Hemisphere: 20231205
        # end_date_mmdd:
        ## If departure_times within the stop_times.txt file are missing for stops, the analysis will be inaccurate unless these are filled in.
        ## In such a case, processing of the GTFS feed will halt with a warning advising the user.
        ## A user could: source alternate data or fill/interpolate these values themselves.
        ## A function has been provided to perform a linear interpolation according to the provided stop sequence start and end times within each trip_id.
        ## This is an approximation based on the available information, and results may still differ from the actual service frequencies at these stops.
        ## It is the user's responsibility to determine if this interpolation is appropriate for their use case.
        ## To interpolate stop_times where these are missing, set the following parameter to 'true':
        # interpolate_stop_times: false
        ## Optionally the default modes presented here can be modified
        # modes:
            # Tram       : {'route_types': [ 0],'agency_id': }
            # Metro      : {'route_types': [ 1],'agency_id': }
            # Rail       : {'route_types': [ 2],'agency_id': }
            # Bus        : {'route_types': [ 3],'agency_id': }
            # Ferry      : {'route_types': [ 4],'agency_id': }
            # Cable tram : {'route_types': [ 5],'agency_id': }
            # Aerial lift: {'route_types': [ 6],'agency_id': }
            # Funicular  : {'route_types': [ 7],'agency_id': }
            # Trolleybus : {'route_types': [11],'agency_id': }
            # Monorail   : {'route_types': [12],'agency_id': }
            #########
## Optional path to results of policy indicator review for inclusion in generated reports
## See https://healthysustainablecities.github.io/software/#Policy-checklist-data
# e.g. for the example file: process/data/policy_review/Urban policy checklist_1000 Cities Challenge_version 1.0.1_LPGC_Sept23_AQ_JMG - draft example.xlsx
policy_review:
## Optional additional notes for this region
notes:
#########
## Reporting configuration (uncomment to modify)
# reporting:
    ## PDF report templates (uncomment as requires)
    ## Policy templates require completion and configuration of policy review checklist
    # templates:
        # - spatial
        ## - policy_spatial
        ## - policy
    ## Set 'publication_ready' to True once you have checked results, updated the summary and are ready to publish; before then, it should be False.
    # publication_ready: False
    ## Select a basemap for the study region report - options are 'satellite' (recent cloudless composite image of Sentinel-2 satellite imagery to view the urban fabric, https://s2maps.eu by EOX IT Services GmbH), or 'osm' (a light coloured thematic map based on OpenStreetMap with labels), or 'streets' (a light coloured thematic map based on OpenStreetMap without labels, displaying streets),
    # study_region_context_basemap: 'satellite'
    ## Once ready for publication it is recommended to register a DOI for your report, e.g. through figshare, zenodo or other repository
    # doi:
    ## Feature inspiring healthy, sustainable urban design from your city, crediting the source
    # images:
        # 1: # (.jpg, 2100px by 1000px; or 21:10 equivalent aspect ratio)
            # file: "Example image of a vibrant, walkable, urban neighbourhood - landscape.jpg"
            # description: "Example image of a vibrant, walkable, urban neighbourhood with diverse people using active modes of transport and a tram (replace with a photograph, customised in region configuration)"
            # credit: "Feature inspiring healthy, sustainable urban design from your city, crediting the source, e.g.: Carl Higgs, Bing Image Creator, 2023"
        # 2: (.jpg, 2100px by 1000px; or equivalent 21:10 aspect ratio)
            # file: "Example image 2-Landscape.jpg"
            # description: "Example image of a vibrant, walkable, urban area (replace with a photograph or your own image, customised in region configuration)"
            # credit: "Feature inspiring healthy, sustainable urban design from your city, crediting the source, e.g.: Eugen Resendiz, Bing Image Creator, 2023"
        # 3: # (.jpg, 1000px by 1000px; or equivalent 1:1 aspect ratio)
            # file: Example image of a vibrant, walkable, urban neighbourhood - square.jpg
            # description: Example image of a vibrant, walkable, urban neighbourhood with diverse people using active modes of transport and a tram (replace with a photograph, customised in region configuration)
            # credit: "Use your image & credit: e.g. Carl Higgs, Bing Image Creator, 2023"
        # 4: # (.jpg, 1000px by 1000px; or equivalent 1:1 aspect ratio)
            # file: "Example image of climate resilient lively city watercolor-Square.jpg"
            # description: "Example image of a climate-resilient, lively city (replace with an image for your city, customised in region configuration)"
            # credit: "Feature inspiring healthy, sustainable urban design from your city, crediting the source, e.g.: Eugen Resendiz, Bing Image Creator, 2023"
    ## Languages configuration
    # languages:
        ## Add a list of languages as required.  Languages listed should correspond to columns in the _report_configuration.xlsx file 'languages' worksheet.  New languages can be added, although some may require additional fonts.  Some languages may not be supported (eg. complex scripts like Tamil and Thai may not be supported by the report template and require manual edits).
        # English:
            # ## City name in English, for example: Las Palmas
            # name:
            # ## Country name in English, for example: Spain
            # country:
            # ## After reviewing the results, update this summary text to contextualise your findings, and relate to external text and documents (e.g. using website hyperlinks).  This text will be used in the report summary.
            # summary: |
                # After reviewing results for your city, provide a contextualised summary by modifying the "summary" text for each configured language within the region configuration file.
            # ## Contextual summary for study region spatial report.  Users may choose to translate these entries (following the hyphen) for languages configured for their city.  Remember, if you put a colon (":") in, put quotes around the text to make it explicitly understood as text.
            # context:
                # # A brief summary of region characteristics
                # - City context:
                    # # Contextual information about your study region.   Please briefly summarise the city location, history and topography, as relevant.
                    # - summary: Edit the region configuration file to provide background context for your study region. Please briefly summarise the location, history and topography, as relevant.
                    # - source: Add any citations used here.
                # - Levels of government:
                # # For example, for this report, policies from [insert levels of government from policy checklist,  e.g. national, metropolitan, local] levels of government were analysed.Completed policy checklist values will be added, but prose may be customised here.
                    # - summary:
                    # - source: Add any citations used here.
                # - Demographics and health equity:
                    # # For example, highlight socio-economic demographic characteristics and key health challenges and inequities present in this urban area.
                    # - summary:
                    # - source: Add any citations used here.
                # - Environmental disaster context:
                    # # For example, environmental hazards likely to be experience by the urban area over the next 5-10 years, may include [insert those listed as ‘yes’ in the policy checklist].  Completed policy checklist values will be added, but prose may be customised here.
                    # - summary:
                    # - source: Add any citations used here.
    ## Optionally, exceptions to the template can be specified here, this can be useful for additional translation customisation without modifying the report_configuration.xlsx file.  These phrases can incorporate translated phrases defined in report configuration, by enclosing these in curly braces, e.g. like {this}, if 'this' has been defined as a phrase in the relevant language.  See the example region for a demonstration of how this can be used.  Sections from the example can be pasted here and modified as required, or the below example can be uncommented.
    # exceptions:
        # "English":
            # 'author_names': 'Add your names here, or modify authors in config.yml and remove this line'
            # 'policy_jurisdiction': 'Customise the entry for policy jurisdiction to override the record found in a completed policy review checklist.'
        # "Another configured language":
            # 'author_names': 'Agregue sus nombres aquí, o modifique los autores en config.yml y elimine esta línea'
            # 'citation_doi': '{author_names}. 2022. {title_city} — {title_series_line1} {disclaimer} ({city}, {country} — Healthy and Sustainable City Indicators Report: Comparisons with 25 cities internationally. {language} {translation}: {translation_names}). {city_doi}'
#########

Example configuration for Las Palmas de Gran Canaria (Spain) in 2023

Click to view example completion of a record in this file for Las Palmas de Gran Canaria, Spain, using the codename `example_ES_Las_Palmas_2023`. 
###########################################################
## Example configuration for Las Palmas de Gran Canaria, Spain
## using Study region configuration template v4.2.2 (27 June 2023)

## This configuration file uses the YAML format (https://yaml.org/) to describe the data sources, parameters and metadata used to analyse and generate urban indicator resources using the Global Healthy and Sustainable City Indicators software (https://healthysustainablecities.github.io/).

## Text beginning with a double hash symbol ("##") is are comments (used to provide descriptions of how to complete the item immediately below).  Text beginning with a single has symbol ("#") are commented out section of code that may optionally be uncommented as per the provided instructions.

## Optional sections that contain parameters which may be uncommented are marked with a series of hash symbols ("###########") at their start and end lines.

## It is recommended to view or edit this file in an application providing syntax highlighting, for example, the provided Jupyter Lab web app:
## - type 'lab' at the GHSCI prompt
## - navigate to the "process/configuration/region/" folder in the browser pane
## - double click on the YAML configuration file with name ending in .yml you wish to edit
###########################################################

## Full study region name, e.g. Las Palmas de Gran Canaria
name: Las Palmas de Gran Canaria
## Target year for analysis, e.g. 2023
year: 2023
## Fully country name, e.g. España
country: Spain
## Two character country code (ISO3166 Alpha-2 code), e.g. ES
country_code: ES
## Continent name, e.g. Europe
continent: Europe
## coordinate reference system (CRS) metadata
crs:
    ## name of the coordinate reference system (CRS), e.g. REGCAN95 / LAEA Europe
    name: REGCAN95 / LAEA Europe
    ## acronym of the standard catalogue defining this CRS, eg. EPSG
    standard: EPSG
    ## spatial reference identifier (SRID) integer that identifies this CRS according to the specified standard, e.g. 5635 (see https://spatialreference.org/, or search for what is commonly used in your city or country; e.g. a national CRS like those listed at https://en.wikipedia.org/wiki/List_of_national_coordinate_reference_systems )
    srid: 5635
## Study region boundary metadata
study_region_boundary:
    ## Path to downloaded data relative to project data directory, or urban_query:variable='value')
    ## e.g. to load a file (geojson, shp, geopackage):
    ## "region_boundaries/Example/Las Palmas de Gran Canaria - Centro Nacional de Información Geográfica - WGS84 - EPSG4326.geojson"
    ## e.g. to use the urban region and urban query specified elsewhere in this configuration file:
    ## urban_query
    ## e.g. to query an attribute for a specific layer in a geopackage
    ## region_boundaries/your_geopackage.gpkg:layer_name -where some_attribute=="some_value" ## to
    data: "region_boundaries/Example/Las Palmas de Gran Canaria - Centro Nacional de Información Geográfica - WGS84 - EPSG4326.geojson"
    ## The name of the provider of this data, e.g. Centro Nacional de Información Geográfica
    source: Centro Nacional de Información Geográfica
    ## Publication date for study region area data source, or date of currency, e.g. 2019-02-01
    publication_date: 2019-02-01
    ## URL for the source dataset, or its provider, e.g. https://datos.gob.es/en/catalogo/e00125901-spaignllm
    url: https://datos.gob.es/en/catalogo/e00125901-spaignllm
    ## Licence for the data, e.g. CC-BY-4.0
    licence: CC-BY-4.0
    ## Whether the provided study region boundary will be further restricted to an urban area defined by its intersection with a linked urban region dataset (see urban_region), e.g. true
    ghsl_urban_intersection: true
    ## A formal citation for this data, For example, "Instituto Geográfico Nacional (2019). Base de datos de divisiones administrativas de España. https://datos.gob.es/en/catalogo/e00125901-spaignllm."
    citation: "Instituto Geográfico Nacional (2019). Base de datos de divisiones administrativas de España. https://datos.gob.es/en/catalogo/e00125901-spaignllm."
    ## Optional notes of relevance for understanding this study region's context
    notes: manually extracted municipal boundary for Las Palmas de Gran Canaria in WGS84 from the downloaded zip file 'lineas_limite.zip' using QGIS to a geojson file for demonstration purposes."
###########
## Optional custom aggregation to additional areas of interest (e.g. neighbourhoods, suburbs, specific developments); uncomment and complete to use
custom_aggregations:
    ## Name for this aggregation layer
    ## The name is followed by a colon, indicating that a list of details follows
    school_districts_grid_pop:
        ## path to data relative to the project data folder
        data: "region_boundaries/Example/Las Palmas excerpt- gobcan_educacion_areainfluenciacentrosecundaria.geojson"
        ## The field used as a unique identifier
        id: 'Codigo'
        ## A list of column field names to be retained
        keep_columns: Denominaci, cod_postal
        ## The indicator layer to be aggregated ("point" or "grid")
        ## Aggregation is based on the average of intersecting results
        ## unless the agg_distance parameter is defined (see alternative example below)
        aggregation_source: grid
        ## The variable used for weighting (e.g. 'pop_est' for population when using the grid; leave blank or "false" if using sample points)
        weight: pop_est
        note: "Example of aggregating indicators for high school catchment districts within Las Palmas, using the intersection with the population grid and taking the population weighted average of indicators.  Boundary data was derived from data sourced from the open data portal of the Gobierno de Canarias under CC BY 4.0 licence terms: https://opendata.sitcan.es/dataset/centros-educativos/resource/ea650255-c6ea-48c1-84e8-547735624017 (last updated 31 May 2023)."
    ## an example for aggregating for buildings represented in OpenStreetMap
    buildings_osm_30m:
        data: "OSM:building is not NULL"
        keep_columns: building
        ## Distance within metres to use for taking average when aggregating
        ## (see note)
        aggregate_within_distance: 30
        aggregation_source: point
        note: "Example of aggregating using buildings extracted from the configured OpenStreetMap data, taking the average of sample point estimates taken along the pedestrian network within 30m.  This has been done because the point indicators were sampled along the pedestrian network and are therefore unlikely to intersect with buildings.  By taking the average of points within some reasonable distance, the result is like a moving window average that should provide a reasonable representation of the immediate neighbourhood milieu surrounding the building."
###########
## Population metadata (raster or vector)
population:
    ## name of the population data
    name: "Global Human Settlements population data: 2020, Mollweide (EU JRC, 2022)"
    ## path relative to project data directory to folder contining tifs, or to vector file
    data_dir: population_grids/Example/GHS_POP_E2020_GLOBE_R2022A_54009_100_V1_0_R6_C17
    ## type of data (e.g. "raster:Int64" or "vector")
    data_type: raster:Int64
    ###########
    ## Vector data specific-fields; uncomment if using vector data (e.g. shp, geojson, gpkg)
    ## The column field with population estimates for your population group of interest (may be total, or for a sub-group of interest to be the focus of your indicators)
    # vector_population_data_field:
    ## The field with the total population data within that area (if you are interested in total, should be the same value as vector_population_data_field)
    # population_denominator:
    ###########
    ###########
    ## Raster data specific-fields; comment out if using raster data (i.e. tif)
    ## image resolution, e.g. 100 m
    resolution: 100m
    ## the image band containing the relevant data, e.g. for GHSL-POP, 1
    raster_band: 1
    ## A value in the image that represents 'no data', e.g. for GHSL-POP, -200
    raster_nodata: -200
    ###########
    ## Sample points intersecting grid cells with estimated population less than this will be excluded from analysis.  Depending on your population data resolution, you can use this to exclude areas with very low population due to the uncertainty of where anyone might live in that area, or if they do at all
    pop_min_threshold: 1
    ## Coordinate reference system metadata for population data (e.g. Mollweide, ESRI, 54009)
    crs_name: Mollweide
    crs_standard: ESRI
    crs_srid: 54009
    ## URL for where this data was sourced from
    source_url: https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/GHSL/GHS_POP_GLOBE_R2022A/GHS_POP_E2020_GLOBE_R2022A_54009_100/V1-0/tiles/GHS_POP_E2020_GLOBE_R2022A_54009_100_V1_0_R6_C17.zip
    ## metadata for citation
    ## when it was published (yyyy), e.g. 2023
    year_published: 2022
    ## the year it is intended to represent (yyyy), e.g. 2020
    year_target: 2020
    ## when you retrieved it (yyyymmdd).  This can be useful to record as data can be subject to revision.  e.g. 20230627
    date_acquired: 20230222
    ## licence, e.g. "CC BY 4.0"
    licence: CC BY 4.0
    ## citation, e.g. "Schiavina, M; Freire, S; Carioli, A., MacManus, K (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset] doi: 10.2905/D6D86A90-4351-4508-99C1-CB074B022C4A"
    citation: "Schiavina, Marcello; Freire, Sergio; MacManus, Kytt (2022): GHS-POP R2022A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset] doi: 10.2905/D6D86A90-4351-4508-99C1-CB074B022C4A"
## OpenStreetMap metadata
OpenStreetMap:
    ## path relative to the project data directory
    data_dir: OpenStreetMap/Example/example_las_palmas_2023_osm_20230221.pbf
    ## the source of the OpenStreetMap data (e.g. Planet OSM, GeoFabrik or OpenStreetMap.fr)
    source: OpenStreetMap.fr
    ## when it was published (yyyymmdd), e.g. 20230627
    publication_date: 20230221
    ## licence (which is most likely ODbL for OpenStreetMap data published since 2012)
    licence: ODbL
    ## the URL from where it was downloaded
    url: https://download.openstreetmap.fr/extracts/africa/spain/canarias/las_palmas-latest.osm.pbf
    ## An optional note regarding this data
    note: This is configured with a derived excerpt from the larger OpenStreetMap dataset for Las Canarias based on the 1600m buffered municipal boundary of Las Palmas de Gran Canaria to reduce file size for demonstration purposes.
## Network analysis related configuration parameters
network:
    #########
    ## Optional network parameters for use in some contexts (eg. island cities)
    ## Whether to only retain main connected network when retrieving OSM roads (set to "false"; the default, which is appropriate for most settings); or retain network 'islands' if present ("true")
    # osmnx_retain_all: false
    ## Whether to extract the network for the buffered study region. It is recommended to set to 'true' (the default) in most cases.  Setting this to false may be appropriate for true islands, but could be problematic for anywhere else where the network and associated amenities may be accessible beyond the edge of the study region boundary).
    # buffered_region: true
    ## Iterate over and combine polygons (this may be appropriate for a series of islands, like Hong Kong), but in most cases it is recommended to be set as false
    # polygon_iteration: false
    ## Minimum total network distance for subgraphs to retain.  This is a useful parameter for customising analysis for islands, like Hong Kong, but for most purposes you can leave this blank (the default).
    # connection_threshold:
    #########
    ## Tolerance in metres for cleaning intersections.  If not providing your own data for evaluating intersection density (see below), this is an important methodological choice.  The chosen parameter should be robust to a variety of network topologies in the city being studied.  See https://github.com/gboeing/osmnx-examples/blob/main/notebooks/04-simplify-graph-consolidate-nodes.ipynb. For example, 12
    intersection_tolerance: 12
    #########
    ## Optionally, data for evaluating intersections can be provided as an alternative to deriving intersections from OpenStreetMap (where available, this may be preferable); uncomment and complete the required fields to do this.
    ## Custom intersection data settings
    # intersections:
        ## path to data relative to the project data directory
        # data: network_data/your_intersection_data.geojson
        ## citation for optional custom intersection data
        # citation: 'Provider of your intersection data.  YYYY.  Name of your intersection data. https://source-url-for-your-data.place'
        ## a note to describe custom intersection data
        # note: 'Uncomment this configuration section to optionally specify an external dataset of intersections.  Otherwise, these are derived using OpenStreetMap and OSMnx using the intersection_tolerance parameter.  If providing intersection data, you can modify this note for it to be included in the metadata, or remove it.
        #########
## Urban region metadata.   An urban region can optionally be defined to supplement the study region definition, e.g. using the Global Human Settlements Layer Urban Centres Database
urban_region:
    ## name for the urban region data, e.g. "Global Human Settlements urban centres: 2015 (EU JRC, 2019)"
    name: "Global Human Settlements urban centres: 2015 (EU JRC, 2019; Las Palmas de Gran Canaria only)"
    ## path to data relative to the project data directory, e.g. "urban_regions/GHS_STAT_UCDB2015MT_GLOBE_R2019A_V1_2.gpkg"
    ## Please note, this data has note been provided but can be retrieved. See the citation below.
    data_dir: "urban_regions/Example/Las Palmas de Gran Canaria - GHS_STAT_UCDB2015MT_GLOBE_R2019A_V1_2.gpkg"
    ## licence, e.g. CC BY 4.0
    licence: CC BY 4.0
    ## citation for this data, this has been pre-filled for the GHSL UCDB (2019), but change as required if using
    citation: "Florczyk, A. et al. (2019): GHS Urban Centre Database 2015, multitemporal and multidimensional attributes, R2019A. European Commission, Joint Research Centre (JRC). https://data.jrc.ec.europa.eu/dataset/53473144-b88c-44bc-b4a3-4583ed1f547e"
    ## A list of additional covariates that can be optionally linked for cities included in the GHSL UCDB
    covariates:
        E_EC2E_T15:
            Units: tonnes per annum
            Unit description: tonnes per annum
            Description: Total emission of CO 2 from the transport sector, using non-short-cycle-organic fuels in 2015
        E_EC2O_T15:
            Units: tonnes per annum
            Unit description: tonnes per annum
            Description: Total emission of CO 2 from the energy sector, using short-cycle-organic fuels in 2015
        E_EPM2_T15:
            Units: tonnes per annum
            Unit description: tonnes per annum
            Description: Total emission of PM 2.5 from the transport sector in 2015
        E_CPM2_T14:
            Units: µg per cubic metre
            Unit description: micrograms per cubic meter
            Description: Total concertation of PM 2.5 for reference epoch 2014
        EL_AV_ALS:
            Units: metres above sea level
            Unit description: metres above sea level
            Description: The average elevation for the urban centre
        E_KG_NM_LST:
            Units: List of climate classes
            Unit description: List of climate classes
            Description: semi-colon separated list of names of Köppen-Geiger climate classes, intersecting with the spatial domain of the Urban Centre
        E_WR_T_14:
            Units: °C
            Unit description: Average temperature in Celsius degrees (°C)
            Description: average temperature calculated from annual average estimates for time interval centred on the year 1990 (the interval spans from 1988 to 1991) within the spatial domain of the Urban Centre
        E_WR_P_14:
            Units: mm
            Unit description: the amount of rain per square meter in one hour (mm)
            Description: average precipitations calculated from annual average estimates for time interval centred on the year 2015 (the interval spans from 2012 to 2015) within the spatial domain of the Urban Centre
## Query used to identify the specific urban region relevant for this region in the Urban Centres database
## GHS or other linkage of covariate data (GHS:variable='value', or path:variable='value' for a dataset with equivalently named fields defined in project parameters for air_pollution_covariates), e.g. GHS:UC_NM_MN=='Las Palmas de Gran Canaria' and CTR_MN_NM=='Spain'
urban_query: GHS:UC_NM_MN=='Las Palmas de Gran Canaria' and CTR_MN_NM=='Spain'
## Additional study region summary covariates to be optionally linked. This is designed to retrieve the list of covariates specifies in the 'urban_region' configuration, either from the configured Global Human Settlements Layer data (enter "urban_query"), or from a CSV file (provide a path relative to the project data directory)
covariate_data: urban_query
## Country-level income metadata for additional city context
country_gdp:
    ## Country GDP classification, e.g. lower-middle
    classification: High-income
    ## Citation for the GDP classification, e.g. The World Bank. 2020. World Bank country and lending groups. https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups
    citation: The World Bank. 2020. World Bank country and lending groups. https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups
#########
## Optional custom destinations to import in addition to those from (e.g. as done for Maiduguri, Nigeria).  Uncomment if required.
# custom_destinations:
    ## name of file relative to project data directory
    # file:
    ## category plain name
    # dest_name:
    ## category full name
    # dest_name_full:
    ## y coordinate
    # lat:
    ## x coordinate
    # lon:
    ## EPSG code
    # epsg:
    ## a citation for this data
    # citation:
    #########
#########
## Optional set up for General Transit Feed Specification (GTFS) transit data.
## GTFS feed data is used to evaluate access to public transport stops with regular weekday daytime service
## For cities with no GTFS feeds identified, this may be left commented out.
gtfs_feeds:
    ## City-specific parent folder in the 'process/data/transit_feeds' directory
    folder: Example
    ## list of zipped GTFS feeds saved in above folder
    gtfs_es_las_palmas_de_gran_canaria_guaguas_20230222.zip:
        ## Name of agency that published this data
        gtfs_provider: Guaguas
        ## Year the data was published
        gtfs_year: 2023
        ## Source URL for the data
        gtfs_url: http://www.guaguas.com/transit/google_transit.zip
        ## The start date of a representative period for analysis
        ## (outside school holidays and extreme weather events), e.g. Spring/Summer
        ## for Northern Hemisphere: 20230405
        ## for Southern Hemisphere: 20231008
        start_date_mmdd: 20230405
        ## The start date of a representative period for analysis
        ## (outside school holidays and extreme weather events), e.g. Spring/Summer
        ## for Northern Hemisphere: 20230605
        ## for Southern Hemisphere: 20231205
        end_date_mmdd: 20230605
## Optional path to results of policy indicator review for inclusion in generated reports.
policy_review: process/data/policy_review/_policy_review_template_v0_TO-BE-UPDATED.xlsx
## Optional additional notes for this region
notes:
#########
## Reporting configuration
reporting:
    ## Set 'publication_ready' to True once you have checked results, updated the summary and are ready to publish; before then, it should be False.
    publication_ready: False
    ## Once ready for publication it is recommended to register a DOI for your report, e.g. through figshare, zenodo or other repository
    doi:
    images:
        ## Store images in the process/configuration/assets folder.
        ## Update file name, description and credit as required.
        1:
            file: Example image of a vibrant, walkable, urban neighbourhood - landscape.jpg
            description: Example image of a vibrant, walkable, urban neighbourhood with diverse people using active modes of transport and a tram (replace with a photograph, customised in region configuration)
            credit: Carl Higgs, Bing Image Creator, 2023
        2:
            file: Example image of a vibrant, walkable, urban neighbourhood - square.jpg
            description: Example image of a vibrant, walkable, urban neighbourhood with diverse people using active modes of transport and a tram (replace with a photograph, customised in region configuration)
            credit: Carl Higgs, Bing Image Creator, 2023
    ## Languages configuration
    languages:
        ## Add a list of languages as required.  Languages listed should correspond to columns in the _report_configuration.xlsx file 'languages' worksheet.  New languages can be added, although some may require additional fonts.  Some languages may not be supported (eg. complex scripts like Tamil and Thai may not be supported by the report template and require manual edits).
        English:
            ## City name in English, for example: Las Palmas
            name: Las Palmas
            ## Country name in English, for example: Spain
            country: Spain
            ## After reviewing the results, update this summary text to contextualise your findings, and relate to external text and documents (e.g. using website hyperlinks).  This text will be used in the report summary.
            summary: |
                After reviewing the results, update this summary text to contextualise your findings, and relate to external text and documents (e.g. using website hyperlinks).
        Spanish - Spain:
            name: Las Palmas de Gran Canaria
            country: España
            summary: |
                Después de revisar los resultados, actualice este texto de resumen para contextualizar sus hallazgos y relacionarlo con textos y documentos externos (por ejemplo, utilizando hipervínculos de sitios web).
        Chinese - Simplified:
            name: 大加那利岛拉斯帕尔马斯
            country: 西�牙
            summary: |
                查看结果�，更新此摘�文本以将您的�现置于上下文中，并与外部文本和文档相关（例如使用网站超链接）。
    ## Optionally, exceptions to the template can be specified here, this can be useful for additional translation customisation without modifying the report_configuration.xlsx file.  These phrases can incorporate translated phrases defined in report configuration, by enclosing these in curly braces, e.g. like {this}, if 'this' has been defined as a phrase in the relevant language.  See the example region for a demonstration of how this can be used.  Sections from the example can be pasted here and modified as required, or the below example can be uncommented.
    exceptions:
        "English":
          'local_collaborators_names': 'Add your names here, or modify authors in config.yml and remove this line'
        "Spanish - Spain":
          'local_collaborators_names': 'Agregue sus nombres aquí, o modifique los autores en config.yml y elimine esta línea'
          'citation_doi': '{local_collaborators_names}. 2022. {title_city} — {title_series_line1} {title_series_line2} ({city}, {country} — Healthy and Sustainable City Indicators Report. Traducción al español (España): {translation_names}). {city_doi}'
        "Chinese - Simplified":
          'local_collaborators_names': '在此处添加您的姓�，或在 config.yml 中修改作者并删除此行'
          'citation_doi': '{local_collaborators_names}. 2022. {title_city} — {title_series_line1} {title_series_line2} ({city}, {country} — Healthy and Sustainable City Indicators Report: Comparisons with 25 cities internationally. {language} {translation}: {translation_names}). {city_doi}'
#########

Codenames

Region configuration files are named using a codename to represent a city when used in processing, for example example_ES_Las_Palmas_2023. This helps to avoid issues with ambiguity when analysing multiple cities across different regions and time points (e.g. cities named Valencia are found in both Spain and Venezuala). In the case of our example city, we have used 'example' to provide a clarification about the study region's purpose, 'ES' clarifies that this is a Spanish city, 'Las_Palmas' is a common short way of writing the city's name, and the analysis is designed to target 2023, using data sources that could be reasonably assumed to provide a fair representation for the city at that time point.

Initialising

To initialise a new study region configuration file, you can run use the Web app study region form, Jupyter Lab or Python (see example below), or command line (configure <codename>; Figure 17). Running the configuration step for a study region with a previously initialised configuration file (e.g. configure example_ES_Las_Palmas_2023) would advise that the study region configuration has already been initialised, and provide guidance on how to run analyses once configuration has been completed by the user using a text editor.

image
Figure 17. Output resulting from creating the configuration files; if you were to run this a second time after successfully running it, it would recognise and report that the files already exist, and otherwise remind you about the purpose of the respective configuration files.

Alternatively, you can initialise a configuration file using Python (including using a Jupyter notebook), for example for the Australian city of Melbourne with a target time point of 2023:

from subprocesses import ghsci
r = ghsci.Region('AU_Melbourne_2023')

Once initialised, study region configuration files must then be modified using a text editor to provide information on the locations of downloaded data and analysis parameters as required.

A good way to view and edit the configuration files is using the provided Jupyter Lab interface.

Other than initialising a new study region (as described above), you can also copy the provided example to a new file and modify the values as required; this might be the easiest way to get started with your new region!

Advanced options

Additional configuration files will be initialised in the process/configuration folder, and may be be edited in a text editor (or in a spreadsheet editor such as Excel for the CSV file) to add and customise analysis for new regions, including

  • config.yml for overall project configuration, including the names of you, your colleagues and organsisation for inclusion in generated metadata and reports
  • datasets.yml to optionally define shared datasets and metadata for OpenStreetMap, population, urban regions and transit feeds that can be referenced by multiple study regions

Optionally, projects can be configured to:

  • analyse GTFS feed data for evaluating accessibility to regularly serviced public transport
  • use custom sets of OpenStreetMap tags for identifying destinations (see OpenStreetMap TagInfo and region-specific tagging guidelines to inform relevant synonyms for points of interest)
  • use custom destination data (a path to CSV with coordinates for points of interest for different destination categories can be configured in process/configuration/regions/_codename_.yml)
Configuration file File description
regions/codename.yml Used to define and specify details for study regions to be analysed. ** Generate this file by running the configuration script followed by a codename of your choice, then open and edit the resulting text file guided by the provided headings.**
config.yml Defines general project parameters (e.g. your name and local time zone, for accurate localised recording of start and end times of processing in log files).
osm_open_space.yml Definitions for identifying areas of open space using OpenStreetMap
datasets.yml Optionally ysed to define shared datasets and their associated metadata
indicators.yml Some aspects of indicators calculated can optionally be modified here; currently this is set up for the core indicators of interest for the 1000 City Challenge.
osm_destinations.csv Used to classify destinations to be retrieved from OpenStreetMap and support calculation of indicators defined in indicators.yml (e.g. percentage of population with access to a particular kind of amenity)
policies.yml A list of policies for reporting. This is a proof of concept for now, based on the 25-city comparative study. An update is planned to support the new policy template.
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