A library to read ESRI Vector Tile Package (.vtpk) files
This library can be installed via pip (note the difference between a dash and an underscore for the name for installation versus imports):
pip install vtpk-reader
Anaconda installation is currently a work in progress...
Read vtpk file and extract some features
from vtpk_reader import Vtpk
vtpk = Vtpk("path/to/your/vtpk/file.vtpk")
# Get all tiles at LOD (level of detail) zero, there should only be one tiles
tiles = vtpk.get_tiles(0, None)
# Get the one of the tiles
one_tile = list(tiles)[0]
# Extract the tile features
features = vtpk.tile_features(one_tile)
# Look at the keys in the data
print(f"{features.keys()})This file is can be found at by searching for "vtpk" and "dodge" on www.arcgis.com. Based on the name and the description, it probably does not contain content not available elsewhere, but being small it is a useful test file.
from vtpk_reader import Vtpk
import geopandas as gpd
import matplotlib.pyplot as plt
import contextily as cx
# Load the vtpk
vtpk = Vtpk("dodge_city.vtpk")
# Create a matplotlib plot
fig, ax = plt.subplots(figsize=(10, 10))
# Get all the tiles at the maximum Level Of Detail (LOD)
# ... not normally recommended, but in case of small VTPKs it should be OK?
tiles = vtpk.get_tiles([vtpk.max_lod], None)
# Get a color palette from matplotlib
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
for idx_tile, tile in enumerate(tiles):
features = vtpk.tile_features(tile)
# Plot every kind of feature there is
for feature_key_idx, feature_key in enumerate(features.keys()):
gdf = gpd.GeoDataFrame(
[prop["geometry"] for prop in features[feature_key]["features"]],
columns=["geometry"],
crs=vtpk.crs,
geometry="geometry",
)
gdf.plot(ax=ax, alpha=0.25, color=colors[idx_tile])
# Add a basemap
cx.add_basemap(ax, source=cx.providers.CartoDB.DarkMatter)
# Expand the plot limits by a factor
increase_factor = 0.125
xlim = ax.get_xlim()
xextent = xlim[1] - xlim[0]
ylim = ax.get_ylim()
yextent = ylim[1] - ylim[0]
ax.set_xlim(xlim[0] - xextent*increase_factor, xlim[1] + xextent*increase_factor/2)
ax.set_ylim(ylim[0] - yextent*increase_factor, ylim[1] + yextent*increase_factor/2)
ax.set_axis_off()You should get something like this:
