create data layer for worldpop_age_sex

city_metrix/layers/world_pop.py

@@ -5,25 +5,45 @@
 
 from .layer import Layer, get_utm_zone_epsg, get_image_collection
 
+
 class WorldPop(Layer):
     """
     Attributes:
+        agesex_classes: list of age-sex classes to retrieve (see https://airtable.com/appDWCVIQlVnLLaW2/tblYpXsxxuaOk3PaZ/viwExxAgTQKZnRfWU/recFjH7WngjltFMGi?blocks=hide)
+        year: year used for data retrieval
         spatial_resolution: raster resolution in meters (see https://github.com/stac-extensions/raster)
     """
 
-    def __init__(self, spatial_resolution=100, **kwargs):
+    def __init__(self, agesex_classes=[], year=2020, spatial_resolution=100, **kwargs):
         super().__init__(**kwargs)
+        # agesex_classes options:
+        # M_0, M_1, M_5, M_10, M_15, M_20, M_25, M_30, M_35, M_40, M_45, M_50, M_55, M_60, M_65, M_70, M_75, M_80
+        # F_0, F_1, F_5, F_10, F_15, F_20, F_25, F_30, F_35, F_40, F_45, F_50, F_55, F_60, F_65, F_70, F_75, F_80
+        self.agesex_classes = agesex_classes
+        self.year = year
         self.spatial_resolution = spatial_resolution
 
     def get_data(self, bbox):
-        # load population
-        dataset = ee.ImageCollection('WorldPop/GP/100m/pop')
-        world_pop = ee.ImageCollection(dataset
-                     .filterBounds(ee.Geometry.BBox(*bbox))
-                     .filter(ee.Filter.inList('year', [2020]))
-                     .select('population')
-                     .mean()
-                     )
-
-        data = get_image_collection(world_pop, bbox, self.spatial_resolution, "world pop")
-        return data.population
+        if not self.agesex_classes:
+            # total population
+            dataset = ee.ImageCollection('WorldPop/GP/100m/pop')
+            world_pop = ee.ImageCollection(dataset
+                                           .filterBounds(ee.Geometry.BBox(*bbox))
+                                           .filter(ee.Filter.inList('year', [self.year]))
+                                           .select('population')
+                                           .mean()
+                                           )
+
+            data = get_image_collection(world_pop, bbox, self.spatial_resolution, "world pop").population
+
+        else:
+            # sum population for selected age-sex groups
+            dataset = ee.ImageCollection('WorldPop/GP/100m/pop_age_sex')
+            world_pop = dataset.filterBounds(ee.Geometry.BBox(*bbox))\
+                .filter(ee.Filter.inList('year', [self.year]))\
+                .select(self.agesex_classes)\
+                .mean()
+            world_pop = ee.ImageCollection(world_pop.reduce(ee.Reducer.sum()).rename('sum_age_sex_group'))
+            data = get_image_collection(world_pop, bbox, self.spatial_resolution, "world pop age sex").sum_age_sex_group
+
+        return data

tests/test_layer_parameters.py

@@ -8,18 +8,20 @@
     AlosDSM,
     AverageNetBuildingHeight,
     BuiltUpHeight,
-    EsaWorldCover, EsaWorldCoverClass,
+    EsaWorldCover, 
+    EsaWorldCoverClass,
     HighLandSurfaceTemperature,
     ImperviousSurface,
     LandSurfaceTemperature,
     NasaDEM,
     NaturalAreas,
     NdviSentinel2,
+    OpenBuildings,
     OpenStreetMap,
     TreeCanopyHeight,
     TreeCover,
     UrbanLandUse,
-    WorldPop, OpenBuildings
+    WorldPop
 )
 from tests.resources.bbox_constants import BBOX_BRA_LAURO_DE_FREITAS_1
 from tests.tools.general_tools import get_class_from_instance, get_class_default_spatial_resolution