This is a userguide test notebook

1 Data access

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import os
import pandas as pd
from arcgis.gis import GIS
import requests
import geopandas as gpd
import folium

Use the GRID3 data portal to find data you would like to use: https://data.grid3.org/

Or see a list of GRID3 REST services available: https://services3.arcgis.com/BU6Aadhn6tbBEdyk/arcgis/rest/services


url = "https://services3.arcgis.com/BU6Aadhn6tbBEdyk/arcgis/rest/services/GRID3_NGA_roads/FeatureServer/0/query"
 
params = {
        "where": "1=1",
        "outFields": "*",
        "outSR": 4326,
        "f": "json" 
    }

resp = requests.get(url, params=params)
data = resp.json()
data.keys()
dict_keys(['objectIdFieldName', 'uniqueIdField', 'globalIdFieldName', 'geometryProperties', 'geometryType', 'spatialReference', 'fields', 'exceededTransferLimit', 'features'])

features = data["features"] #use features as the key to get list of features from this dataset

# attributes
df = pd.json_normalize([f["attributes"] for f in features])

geoms = []

from shapely.geometry import LineString

for f in features:
    geom = f["geometry"]

    if "paths" in geom:
        # most road features have one path
        geoms.append(LineString(geom["paths"][0]))
    else:
        geoms.append(None)
        
        
gdf = gpd.GeoDataFrame(
    df,
    geometry=geoms,
    crs="EPSG:4326"
)

gdf.head()
OBJECTID id country iso3 source_id class speed_estimate speed_estimate_method road_surface names subclass speed_limits date source_acronym Shape__Length geometry
0 1 08059fffffffffff046f5212a07291c3 Nigeria NGA Overture / OSM unclassified 40 literature review None \r 2025 OVERTURE_OSM_001 5509.471374 LINESTRING (6.75328 9.90715, 6.75328 9.90717, ...
1 2 08059fffffffffff046ff6a34145d397 Nigeria NGA Overture / OSM unclassified 40 literature review 2025 OVERTURE_OSM_001 2944.939957 LINESTRING (6.78229 9.88151, 6.78231 9.88163, ...
2 3 08059fffffffffff046ff6d48288b3ef Nigeria NGA Overture / OSM unclassified 40 literature review 2025 OVERTURE_OSM_001 3355.942042 LINESTRING (6.80792 9.88413, 6.80758 9.8839, 6...
3 4 08059fffffffffff046ff775b238a897 Nigeria NGA Overture / OSM unclassified 40 literature review 2025 OVERTURE_OSM_001 3808.948460 LINESTRING (6.81537 9.84995, 6.81475 9.84981, ...
4 5 08059fffffffffff046fff05b325608b Nigeria NGA Overture / OSM tertiary 50 GPS 2025 OVERTURE_OSM_001 7136.589369 LINESTRING (6.75366 9.82817, 6.75391 9.82827, ... 
gdf.plot()

2 Using Folium to visualize data

The package folium is a great way to visualize spatial data in Python. Below is an example of how to use it.



# center the map on your data
minx, miny, maxx, maxy = gdf.total_bounds

center = [
    (miny + maxy) / 2,
    (minx + maxx) / 2
]

m = folium.Map(
    location=center,
    zoom_start=8,
    tiles="CartoDB positron" #"OpenStreetMap"  # basemap
)

folium.GeoJson(
    gdf,
    name="GRID3 Roads",
    popup=folium.GeoJsonPopup(
        fields=list(df.columns)[:5]  # limit fields
    )
).add_to(m)



folium.LayerControl().add_to(m)

m
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