All Metrics¶
| Class | Unit | Layers used | Description |
|---|---|---|---|
AirPollutantAnnualDailyMean__Tonnes |
Tonnes | Cams, CamsSpecies |
Annual mean daily concentration of a selected air pollutant species. |
AirPollutantAnnualDailyMax__Tonnes |
Tonnes | Cams, CamsSpecies |
Annual maximum daily concentration of a selected air pollutant species. |
AirPollutantAnnualTotalSocialCost__USD |
USD | Cams, CamsSpecies |
Estimated total annual social/health cost of air pollutant emissions. |
AirPollutantWhoExceedance__Days |
Days | Cams, CamsSpecies |
Days per year a selected pollutant exceeds its WHO guideline threshold. |
AreaFractionalVegetationExceedsThreshold__Percent |
Percent | FractionalVegetationPercent |
% of area where fractional vegetation meets/exceeds a threshold. |
BuiltAreaWithoutTreeCover__Percent |
Percent | TreeCanopyHeight, EsaWorldCover |
% of built-up land with no tree cover above a canopy height threshold. |
BuiltLandWithHighLST__Percent |
Percent | HighLandSurfaceTemperature, EsaWorldCover |
% of built-up land experiencing high land surface temperature. |
BuiltLandWithLowSurfaceReflectivity__Percent |
Percent | Albedo, EsaWorldCover |
% of built-up land with low surface albedo. |
BuiltLandWithVegetation__Percent |
Percent | EsaWorldCover, FractionalVegetationPercent |
% of built-up land with NDVI-based vegetation cover above a threshold. |
CanopyAreaPerResident__SquareMeters |
Square Meters | TreeCanopyHeight, WorldPop, UrbanLandUse |
Tree canopy area available per resident. |
CanopyAreaPerResidentChildren__SquareMeters |
Square Meters | (same, WorldPopClass.CHILDREN) |
Canopy area per child resident. |
CanopyAreaPerResidentElderly__SquareMeters |
Square Meters | (same, WorldPopClass.ELDERLY) |
Canopy area per elderly resident. |
CanopyAreaPerResidentFemale__SquareMeters |
Square Meters | (same, WorldPopClass.FEMALE) |
Canopy area per female resident. |
CanopyAreaPerResidentInformal__SquareMeters |
Square Meters | (same + informal-settlement mask) | Canopy area per resident within informal settlements. |
CanopyCoveredPopulation__Percent |
Percent | WorldPop, UrbanLandUse, TreeCanopyCoverMask |
% of population living in areas meeting a canopy-coverage threshold. |
CanopyCoveredPopulationChildren__Percent |
Percent | (same, CHILDREN) |
% of children in canopy-covered areas. |
CanopyCoveredPopulationElderly__Percent |
Percent | (same, ELDERLY) |
% of elderly in canopy-covered areas. |
CanopyCoveredPopulationFemale__Percent |
Percent | (same, FEMALE) |
% of females in canopy-covered areas. |
CanopyCoveredPopulationInformal__Percent |
Percent | (same + informal-settlement mask) | % of informal-settlement population in canopy-covered areas. |
GhgEmissions__Tonnes |
Tonnes | CamsGhg |
Mean GHG emissions over a zone (optionally CO2e by species/sector). |
HabitatConnectivityCoherence__Percent |
Percent | NaturalAreas |
Degree to which natural-area patches form one connected cluster. |
HabitatConnectivityEffectiveMeshSize__Hectares |
Hectares | NaturalAreas |
Effective mesh size — a habitat fragmentation measure. |
HabitatTypesRestored__CoverTypes |
Cover types | LandCoverSimplifiedGlad, LandCoverHabitatChangeGlad |
Count of distinct land-cover types in areas restored to natural habitat. |
HospitalsPerTenThousandResidents__Hospitals |
Hospitals (rate) | OpenStreetMap (HOSPITAL), WorldPop |
Hospitals per 10,000 residents. |
ImperviousArea__Percent |
Percent | ImperviousSurface |
% of zone land area classified impervious. |
ImperviousSurfaceOnUrbanizedLand__Percent |
Percent | UrbanExtents, ImperviousSurface, WorldPop |
% of urbanized land covered by impervious surface. |
KeyBiodiversityAreaProtected__Percent |
Percent | WorldPop, KeyBiodiversityAreas, ProtectedAreas |
% of Key Biodiversity Area under formal legal protection. |
KeyBiodiversityAreaUndeveloped__Percent |
Percent | WorldPop, KeyBiodiversityAreas, EsaWorldCover (BUILT_UP) |
% of Key Biodiversity Area that remains undeveloped. |
LandNearNaturalDrainage__Percent |
Percent | HeightAboveNearestDrainage |
% of land near natural drainage channels. |
MeanPM2P5Exposure__MicrogramsPerCubicMeter |
µg/m³ | AcagPM2p5, UrbanLandUse (optional) |
Mean ambient PM2.5 concentration across a zone. |
MeanPM2P5ExposurePopWeighted__MicrogramsPerCubicMeter |
µg/m³ | PopWeightedPM2p5, UrbanLandUse |
Population-weighted mean PM2.5 exposure. |
MeanPM2P5ExposurePopWeightedChildren__MicrogramsPerCubicMeter |
µg/m³ | (same, children) | Population-weighted PM2.5 exposure for children. |
MeanPM2P5ExposurePopWeightedElderly__MicrogramsPerCubicMeter |
µg/m³ | (same, elderly) | Population-weighted PM2.5 exposure for elderly. |
MeanPM2P5ExposurePopWeightedFemale__MicrogramsPerCubicMeter |
µg/m³ | (same, female) | Population-weighted PM2.5 exposure for females. |
MeanPM2P5ExposurePopWeightedInformal__MicrogramsPerCubicMeter |
µg/m³ | (same + informal-settlement mask) | Population-weighted PM2.5 exposure for informal settlements. |
MeanTreeCover__Percent |
Percent | TreeCover |
Mean % tree cover across a zone. |
NaturalAreas__Percent |
Percent | NaturalAreas (layer) |
% of zone classified as natural land. |
BirdRichness__Species |
Species | SpeciesRichness (BIRDS) |
Count of distinct bird species in a zone. |
ArthropodRichness__Species |
Species | SpeciesRichness (ARTHROPODS) |
Count of distinct arthropod species in a zone. |
VascularPlantRichness__Species |
Species | SpeciesRichness (VASCULAR_PLANTS) |
Count of distinct vascular plant species in a zone. |
BirdRichnessInBuiltUpArea__Species |
Species | SpeciesRichness (BIRDS), EsaWorldCover (BUILT_UP) |
Count of distinct bird species within built-up areas. |
ProtectedArea__Percent |
Percent | ProtectedAreas, EsaWorldCover |
% of zone land that is designated/protected area. |
RecreationalSpacePerThousand__HectaresPerThousandPersons |
Hectares / 1,000 persons | WorldPop, OpenStreetMap (OPEN_SPACE) |
Recreational/open space per 1,000 residents. |
RiparianLandWithVegetationOrWater__Percent |
Percent | RiparianAreas, NdwiSentinel2, FractionalVegetationPercent |
% of riparian land covered by water or vegetation. |
RiverineOrCoastalFloodRiskArea__Percent |
Percent | AqueductFlood (riverine + coastal) |
% of zone exposed to riverine/coastal flood risk. |
SteeplySlopedLandWithVegetation__Percent |
Percent | Slope, FractionalVegetationPercent |
% of steeply sloped land that is also vegetated. |
TreeCarbonFlux__Tonnes |
Tonnes | CarbonFluxFromTrees |
Net carbon flux from trees in a zone. |
UrbanOpenSpace__Percent |
Percent | EsaWorldCover (BUILT_UP), OpenStreetMap (OPEN_SPACE) |
% of built-up urban land that is open/recreational space. |
VegetationWaterChangeGainArea__SquareMeters |
Square Meters | VegetationWaterMap (gain) |
Area gained in vegetation/water cover between two dates. |
VegetationWaterChangeLossArea__SquareMeters |
Square Meters | VegetationWaterMap (loss) |
Area lost in vegetation/water cover between two dates. |
VegetationWaterChangeGainLoss__Ratio |
Ratio | VegetationWaterMap (gain + loss + baseline) |
Net gain-minus-loss ratio of vegetation/water change, normalized by start area. |
WaterCover__Percent |
Percent | SurfaceWater, NdwiSentinel2 |
% of zone area covered by surface water. |
Non-conforming preprocessing classes
Era5MetPreprocessingUmep and Era5MetPreprocessingUPenn don't follow the Name__Unit convention — they produce multi-column, mixed-unit DataFrames of meteorological forcing data (built on Era5HottestDay) for use by external microclimate models (UMEP, UPenn), rather than a single scalar indicator.
API¶
city_metrix.metrics.air_pollutant_annual_daily_statistic.AirPollutantAnnualDailyMean__Tonnes ¶
AirPollutantAnnualDailyMean__Tonnes(species=[], year=2024, **kwargs)
city_metrix.metrics.air_pollutant_annual_daily_statistic.AirPollutantAnnualDailyMax__Tonnes ¶
AirPollutantAnnualDailyMax__Tonnes(species=[], year=2024, **kwargs)
city_metrix.metrics.air_pollutant_annual_daily_statistic.AirPollutantAnnualTotalSocialCost__USD ¶
AirPollutantAnnualTotalSocialCost__USD(species=[], year=2024, **kwargs)
city_metrix.metrics.air_pollutant_who_exceedance_days.AirPollutantWhoExceedance__Days ¶
AirPollutantWhoExceedance__Days(species=None, year=2023, **kwargs)
city_metrix.metrics.area_fracveg_exceeds_threshold.AreaFractionalVegetationExceedsThreshold__Percent ¶
AreaFractionalVegetationExceedsThreshold__Percent(min_threshold=50, year=2024, **kwargs)
city_metrix.metrics.built_area_without_tree_cover.BuiltAreaWithoutTreeCover__Percent ¶
BuiltAreaWithoutTreeCover__Percent(height=MIN_TREE_HEIGHT, year=2025, **kwargs)
Bases: Metric
get_metric ¶
get_metric(geo_zone: GeoZone, spatial_resolution: int = None) -> Union[pd.DataFrame | pd.Series]
Get percentage of land (assuming zones are based on urban extents) with no tree cover (>3 Global Canopy Height dataset).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
geo_zone
|
GeoZone
|
GeoDataFrame with geometries to collect zonal stats on |
required |
Returns:
| Type | Description |
|---|---|
Union[DataFrame | Series]
|
Pandas Series of percentages or DataFrame of value and zone |
city_metrix.metrics.built_land_with_high_land_surface_temperature.BuiltLandWithHighLST__Percent ¶
BuiltLandWithHighLST__Percent(year=datetime.datetime.now().year, **kwargs)
Bases: Metric
get_metric ¶
get_metric(geo_zone: GeoZone, spatial_resolution: int = None) -> Union[pd.DataFrame | pd.Series]
Get percentage of built up land with low albedo based on Sentinel 2 imagery.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
geo_zone
|
GeoZone
|
GeoDataFrame with geometries to collect zonal stats over |
required |
Returns:
| Type | Description |
|---|---|
Union[DataFrame | Series]
|
Pandas Series of percentages or DataFrame of value and zone |
city_metrix.metrics.built_land_with_low_surface_reflectivity.BuiltLandWithLowSurfaceReflectivity__Percent ¶
BuiltLandWithLowSurfaceReflectivity__Percent(start_date='2021-01-01', end_date='2022-01-01', albedo_threshold=0.2, **kwargs)
Bases: Metric
get_metric ¶
get_metric(geo_zone: GeoZone, spatial_resolution: int = None) -> Union[pd.DataFrame | pd.Series]
Get percentage of built up land with low albedo based on Sentinel 2 imagery.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
geo_zone
|
GeoZone
|
GeoDataFrame with geometries to collect zonal stats over |
required |
start_date
|
start time for collecting albedo values. |
required | |
end_date
|
end time for collecting albedo values. |
required | |
albedo_threshold
|
threshold for "low" albedo. |
required |
Returns:
| Type | Description |
|---|---|
Union[DataFrame | Series]
|
Pandas Series of percentages or DataFrame of value and zone |
city_metrix.metrics.built_land_with_vegetation.BuiltLandWithVegetation__Percent ¶
BuiltLandWithVegetation__Percent(year=datetime.datetime.now().year, **kwargs)
Bases: Metric
get_metric ¶
get_metric(geo_zone: GeoZone, spatial_resolution: int = None) -> Union[pd.DataFrame | pd.Series]
Get percentage of built up land (using ESA world cover) with NDVI vegetation cover.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
zones
|
GeoDataFrame with geometries to collect zonal stats on |
required |
Returns:
| Type | Description |
|---|---|
Union[DataFrame | Series]
|
Pandas Series of percentages |
city_metrix.metrics.canopy_area_per_resident.CanopyAreaPerResident__SquareMeters ¶
CanopyAreaPerResident__SquareMeters(agesex_classes=[], worldpop_version=1, height=3, informal_only=False, year=2025, **kwargs)
city_metrix.metrics.canopy_area_per_resident.CanopyAreaPerResidentChildren__SquareMeters ¶
CanopyAreaPerResidentChildren__SquareMeters(height=3, year=2025, worldpop_version=1, **kwargs)
city_metrix.metrics.canopy_area_per_resident.CanopyAreaPerResidentElderly__SquareMeters ¶
CanopyAreaPerResidentElderly__SquareMeters(height=3, year=2025, worldpop_version=1, **kwargs)
city_metrix.metrics.canopy_area_per_resident.CanopyAreaPerResidentFemale__SquareMeters ¶
CanopyAreaPerResidentFemale__SquareMeters(height=3, year=2025, worldpop_version=1, **kwargs)
city_metrix.metrics.canopy_area_per_resident.CanopyAreaPerResidentInformal__SquareMeters ¶
CanopyAreaPerResidentInformal__SquareMeters(height=3, year=2025, worldpop_version=1, **kwargs)
city_metrix.metrics.canopy_covered_population.CanopyCoveredPopulation__Percent ¶
CanopyCoveredPopulation__Percent(worldpop_agesex_classes=[], worldpop_version=1, height=3, percentage=30, informal_only=False, year=2025, **kwargs)
city_metrix.metrics.canopy_covered_population.CanopyCoveredPopulationChildren__Percent ¶
CanopyCoveredPopulationChildren__Percent(height=3, percentage=30, year=2025, worldpop_version=1, **kwargs)
city_metrix.metrics.canopy_covered_population.CanopyCoveredPopulationElderly__Percent ¶
CanopyCoveredPopulationElderly__Percent(height=3, percentage=30, year=2025, worldpop_version=1, **kwargs)
city_metrix.metrics.canopy_covered_population.CanopyCoveredPopulationFemale__Percent ¶
CanopyCoveredPopulationFemale__Percent(height=3, percentage=30, year=2025, worldpop_version=1, **kwargs)
city_metrix.metrics.canopy_covered_population.CanopyCoveredPopulationInformal__Percent ¶
CanopyCoveredPopulationInformal__Percent(height=3, percentage=30, year=2025, worldpop_version=1, **kwargs)
city_metrix.metrics.era5_met_preprocessing_umep_gee.Era5MetPreprocessingUmep ¶
Era5MetPreprocessingUmep(start_date: str = None, end_date: str = None, seasonal_utc_offset: float = 0, **kwargs)
Bases: Metric
MINOR_NAMING_ATTS
class-attribute
instance-attribute
¶
MINOR_NAMING_ATTS = None
Attributes: start_date: starting date for data retrieval end_date: ending date for data retrieval seasonal_utc_offset: UTC-offset in hours as determined for AOI and DST usage.
get_metric ¶
get_metric(geo_zone: GeoZone, spatial_resolution: int = None) -> pd.DataFrame
Get ERA 5 data for the hottest day
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
geo_zone
|
GeoZone
|
GeoZone with geometries to collect zonal stats on |
required |
Returns:
| Type | Description |
|---|---|
DataFrame
|
Pandas Dataframe of data |
city_metrix.metrics.era5_met_preprocessing_upenn_gee.Era5MetPreprocessingUPenn ¶
Era5MetPreprocessingUPenn(start_date: str = None, end_date: str = None, seasonal_utc_offset: float = 0, **kwargs)
Bases: Metric
MINOR_NAMING_ATTS
class-attribute
instance-attribute
¶
MINOR_NAMING_ATTS = None
Attributes: start_date: starting date for data retrieval end_date: ending date for data retrieval seasonal_utc_offset: UTC-offset in hours as determined for AOI and DST usage.
get_metric ¶
get_metric(geo_zone: GeoZone, spatial_resolution: int = None) -> pd.DataFrame
Get ERA 5 data for the hottest day
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
geo_zone
|
GeoZone
|
GeoZone with geometries to collect zonal stats on |
required |
Returns:
| Type | Description |
|---|---|
DataFrame
|
Pandas Dataframe of data |
city_metrix.metrics.future_climate_hazard.FutureHeatwaveFrequency__Heatwaves ¶
FutureHeatwaveFrequency__Heatwaves(start_year: int = 2040, end_year: int = 2049, model_rank=1, **kwargs)
city_metrix.metrics.future_climate_hazard.FutureHeatwaveMaxDuration__Days ¶
FutureHeatwaveMaxDuration__Days(start_year: int = 2040, end_year: int = 2049, model_rank=1, **kwargs)
city_metrix.metrics.future_climate_hazard.FutureDaysAbove35__Days ¶
FutureDaysAbove35__Days(start_year: int = 2040, end_year: int = 2049, model_rank=1, **kwargs)
city_metrix.metrics.future_climate_hazard.FutureAnnualMaxTemp__DegreesCelsius ¶
FutureAnnualMaxTemp__DegreesCelsius(start_year: int = 2040, end_year: int = 2049, model_rank=1, **kwargs)
city_metrix.metrics.future_climate_hazard.FutureExtremePrecipitationDays__Days ¶
FutureExtremePrecipitationDays__Days(start_year: int = 2040, end_year: int = 2049, model_rank=1, **kwargs)
city_metrix.metrics.ghg_emissions.GhgEmissions__Tonnes ¶
GhgEmissions__Tonnes(species=None, sector='sum', co2e=True, year=2023, **kwargs)
city_metrix.metrics.habitat_connectivity.HabitatConnectivityCoherence__Percent ¶
HabitatConnectivityCoherence__Percent(**kwargs)
Bases: _HabitatConnectivity
city_metrix.metrics.habitat_connectivity.HabitatConnectivityEffectiveMeshSize__Hectares ¶
HabitatConnectivityEffectiveMeshSize__Hectares(**kwargs)
Bases: _HabitatConnectivity
city_metrix.metrics.habitat_types_restored.HabitatTypesRestored__CoverTypes ¶
HabitatTypesRestored__CoverTypes(start_year=2000, end_year=2020, **kwargs)
city_metrix.metrics.hospitals_per_ten_thousand_residents.HospitalsPerTenThousandResidents__Hospitals ¶
HospitalsPerTenThousandResidents__Hospitals(year=datetime.datetime.now().year, worldpop_version=1, **kwargs)
city_metrix.metrics.impervious_area.ImperviousArea__Percent ¶
ImperviousArea__Percent(year=2018, **kwargs)
city_metrix.metrics.impervious_surface_on_urbanized_land.ImperviousSurfaceOnUrbanizedLand__Percent ¶
ImperviousSurfaceOnUrbanizedLand__Percent(year=2015, worldpop_version=1, **kwargs)
city_metrix.metrics.key_biodiversity_area.KeyBiodiversityAreaProtected__Percent ¶
KeyBiodiversityAreaProtected__Percent(country_code_iso3=None, worldpop_version=1, **kwargs)
city_metrix.metrics.key_biodiversity_area.KeyBiodiversityAreaUndeveloped__Percent ¶
KeyBiodiversityAreaUndeveloped__Percent(country_code_iso3=None, worldpop_version=1, **kwargs)
city_metrix.metrics.land_near_natural_drainage.LandNearNaturalDrainage__Percent ¶
LandNearNaturalDrainage__Percent(**kwargs)
city_metrix.metrics.mean_pm2p5_exposure.MeanPM2P5Exposure__MicrogramsPerCubicMeter ¶
MeanPM2P5Exposure__MicrogramsPerCubicMeter(year=2022, informal_only=False, **kwargs)
city_metrix.metrics.mean_pm2p5_exposure.MeanPM2P5ExposurePopWeighted__MicrogramsPerCubicMeter ¶
MeanPM2P5ExposurePopWeighted__MicrogramsPerCubicMeter(year=2022, worldpop_agesex_classes=[], informal_only=False, **kwargs)
city_metrix.metrics.mean_pm2p5_exposure.MeanPM2P5ExposurePopWeightedChildren__MicrogramsPerCubicMeter ¶
MeanPM2P5ExposurePopWeightedChildren__MicrogramsPerCubicMeter(year=2022, **kwargs)
city_metrix.metrics.mean_pm2p5_exposure.MeanPM2P5ExposurePopWeightedElderly__MicrogramsPerCubicMeter ¶
MeanPM2P5ExposurePopWeightedElderly__MicrogramsPerCubicMeter(year=2022, **kwargs)
city_metrix.metrics.mean_pm2p5_exposure.MeanPM2P5ExposurePopWeightedFemale__MicrogramsPerCubicMeter ¶
MeanPM2P5ExposurePopWeightedFemale__MicrogramsPerCubicMeter(year=2022, **kwargs)
city_metrix.metrics.mean_pm2p5_exposure.MeanPM2P5ExposurePopWeightedInformal__MicrogramsPerCubicMeter ¶
MeanPM2P5ExposurePopWeightedInformal__MicrogramsPerCubicMeter(year=2022, **kwargs)
city_metrix.metrics.mean_tree_cover.MeanTreeCover__Percent ¶
MeanTreeCover__Percent(year=2025, **kwargs)
Bases: Metric
get_metric ¶
get_metric(geo_zone: GeoZone, spatial_resolution: int = None) -> Union[pd.DataFrame | pd.Series]
Get mean tree cover (WRI tropical tree cover).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
geo_zone
|
GeoZone
|
GeoZone with geometries to collect zonal stats on |
required |
Returns:
| Type | Description |
|---|---|
Union[DataFrame | Series]
|
Pandas Series of percentages or DataFrame of value and zone |
city_metrix.metrics.natural_areas.NaturalAreas__Percent ¶
NaturalAreas__Percent(year=2020, **kwargs)
city_metrix.metrics.number_species.BirdRichness__Species ¶
BirdRichness__Species(start_year=2019, end_year=2024, **kwargs)
city_metrix.metrics.number_species.ArthropodRichness__Species ¶
ArthropodRichness__Species(start_year=2019, end_year=2024, **kwargs)
city_metrix.metrics.number_species.VascularPlantRichness__Species ¶
VascularPlantRichness__Species(start_year=2019, end_year=2024, **kwargs)
city_metrix.metrics.number_species.BirdRichnessInBuiltUpArea__Species ¶
BirdRichnessInBuiltUpArea__Species(start_year=2019, end_year=2024, **kwargs)
city_metrix.metrics.protected_area.ProtectedArea__Percent ¶
ProtectedArea__Percent(status=['Inscribed', 'Adopted', 'Designated', 'Established'], status_year=2024, iucn_cat=['Ia', 'Ib', 'II', 'III', 'IV', 'V', 'VI', 'Not Applicable', 'Not Assigned', 'Not Reported'], **kwargs)
city_metrix.metrics.recreational_space_per_thousand.RecreationalSpacePerThousand__HectaresPerThousandPersons ¶
RecreationalSpacePerThousand__HectaresPerThousandPersons(year=datetime.datetime.now().year, worldpop_version=1, **kwargs)
city_metrix.metrics.riparian_land_with_vegetation_or_water.RiparianLandWithVegetationOrWater__Percent ¶
RiparianLandWithVegetationOrWater__Percent(year=2024, **kwargs)
Bases: Metric
get_metric ¶
get_metric(geo_zone: GeoZone, spatial_resolution: int = None) -> Union[pd.DataFrame | pd.Series]
Get total area as number of pixels in NwdiSentinel2. Get water area as number of 1-valued pixels in SurfaceWater.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
geo_zone
|
GeoZone
|
GeoZone with geometries to collect zonal stats on |
required |
Returns:
| Type | Description |
|---|---|
Union[DataFrame | Series]
|
Pandas Series of percentages |
city_metrix.metrics.riverine_or_coastal_flood_risk_area.RiverineOrCoastalFloodRiskArea__Percent ¶
RiverineOrCoastalFloodRiskArea__Percent(year=2050, **kwargs)
city_metrix.metrics.steeply_sloped_land_with_vegetation.SteeplySlopedLandWithVegetation__Percent ¶
SteeplySlopedLandWithVegetation__Percent(year=2024, **kwargs)
Bases: Metric
get_metric ¶
get_metric(geo_zone: GeoZone, spatial_resolution: int = None) -> Union[pd.DataFrame | pd.Series]
Get total area as number of pixels in NwdiSentinel2. Get water area as number of 1-valued pixels in SurfaceWater.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
geo_zone
|
GeoZone
|
GeoZone with geometries to collect zonal stats on |
required |
Returns:
| Type | Description |
|---|---|
Union[DataFrame | Series]
|
Pandas Series of percentages |
city_metrix.metrics.tree_carbon_flux.TreeCarbonFlux__Tonnes ¶
TreeCarbonFlux__Tonnes(**kwargs)
city_metrix.metrics.urban_open_space.UrbanOpenSpace__Percent ¶
UrbanOpenSpace__Percent(year=datetime.datetime.now().year, **kwargs)
city_metrix.metrics.vegetation_water_change.VegetationWaterChangeGainArea__SquareMeters ¶
VegetationWaterChangeGainArea__SquareMeters(start_date='2016-01-01', end_date='2022-12-31', **kwargs)
city_metrix.metrics.vegetation_water_change.VegetationWaterChangeLossArea__SquareMeters ¶
VegetationWaterChangeLossArea__SquareMeters(start_date='2016-01-01', end_date='2022-12-31', **kwargs)
city_metrix.metrics.vegetation_water_change.VegetationWaterChangeGainLoss__Ratio ¶
VegetationWaterChangeGainLoss__Ratio(start_date='2016-01-01', end_date='2022-12-31', **kwargs)
city_metrix.metrics.water_cover.WaterCover__Percent ¶
WaterCover__Percent(year=2024, **kwargs)
Bases: Metric
get_metric ¶
get_metric(geo_zone: GeoZone, spatial_resolution: int = None) -> Union[pd.DataFrame | pd.Series]
Get total area as number of pixels in NwdiSentinel2. Get water area as number of 1-valued pixels in SurfaceWater.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
geo_zone
|
GeoZone
|
GeoZone with geometries to collect zonal stats on |
required |
Returns:
| Type | Description |
|---|---|
Union[DataFrame | Series]
|
Pandas Series of percentages |