def test_map_colorado_like_readme():
# Read congresisonal district data
# Median household income estimate is "DP03_0062E", https://api.census.gov/data/2018/acs/acs1/profile/groups/DP03.html
# https://api.census.gov/data/2018/acs/acs1/profile?get=group(DP03),NAME&for=congressional%20district:*&in=state:08
data = census.load_json_file(
data_dir / "DP03_08_cd.json", headers=["NAME", "GEO_ID", "DP03_0062E"]
)
# .shp GEOID uses last 4 digits
data["GEOID"] = [geoid[-4:] for geoid in data["GEO_ID"]]
# Create column of floats (to color by)
data["Median Household Income"] = [float(x) for x in data["DP03_0062E"]]
# Create column of nicely formatted strings (to display)
data["Median Household Income (pretty format)"] = [
"${:,.2f}".format(x) for x in data["Median Household Income"]
]
# Make and save GIS GeoJson map object
# Shapefile from: https://www.census.gov/cgi-bin/geo/shapefiles/index.php
# Search for "Congressional Districts"
gis.make_map(
data_dir / "tl_2019_08_cd116",
data,
join_on="GEOID",
color_by="Median Household Income",
save_to=Path.cwd() / "user/map.html",
)
def test_map_nc():
# Read race data per county subdivision
# https://api.census.gov/data/2010/dec/sf1?get=group(H6),NAME&for=county:*&in=state:37
data = census.load_json_file(
data_dir / "nc_race_by_county.json",
headers=[
"NAME",
"GEO_ID",
"H006001",
"H006002",
"H006003",
"H006004",
"H006005",
"H006006",
"H006007",
"H006008",
],
)
# .shp GEOID uses last 5 digits
data["GEOID"] = [geoid[-5:] for geoid in data["GEO_ID"]]
# Percentage of white people out of the total respondants (to color by)
data["% White"] = [
float(w) / float(t) if not t == 0 else 100
for w, t in zip(data["H006002"], data["H006001"])
]
# Percentage of white people (formatted nicely for display)
data["% White (pretty format)"] = [
f"{round(x * 100, 1)}%" for x in data["% White"]
]
aliases = {
"NAME": "Name",
"GEO_ID": "GEOID",
"H006001": "Total",
"H006002": "White",
"H006003": "Black",
"H006004": "Native American",
"H006005": "Asian",
"H006006": "Pasific Islander",
"H006007": "Other",
"H006008": "Two or More",
"% White (pretty format)": "Percent White",
}
# Make and save GIS GeoJson map object
# Shapefile from: https://www.census.gov/cgi-bin/geo/shapefiles/index.php
# Search for "County,"" then select "North Carolina"
gis.make_map(
data_dir / "tl_2019_37_county",
data,
join_on="GEOID",
color_by="% White",
include=aliases,
save_to=Path.cwd() / "user/map.html",
)
def test_make_map_joins_properly(tmp_path):
data = standard_data
data_map = gis.make_map(
shapefile_path,
data,
join_on="name",
)
geojson = data_map.data
assert geojson["type"] == "FeatureCollection"
features = geojson["features"]
assert len(features) == 36
feature0_props = features[0]["properties"]
assert feature0_props["name"] == "Bay Colony, Port Royale"
assert feature0_props["Demographic 1"] == "bay col dem1"
assert feature0_props["Demographic 2"] == "bay col dem2"
feature1_props = features[1]["properties"]
assert feature1_props["name"] == "NE 48th"
assert feature1_props["Demographic 1"] == "NE 48th dem1"
assert feature1_props["Demographic 2"] == "NE 48th dem2"
feature2_props = features[2]["properties"]
assert feature2_props["name"] == "Linden Pointe Apartments"
assert feature2_props["Demographic 1"] is None
assert feature2_props["Demographic 2"] is None
data_map.add_to(m)
save_path = (tmp_path/"temp.html").resolve() #tmp_path will unlink automatically
m.save(str(save_path))
def test_make_map_joins_properly():
data = standard_data
data_map = gis.make_map(
shapefile_path,
data,
join_on="name",
)
geojson = data_map.data
assert geojson["type"] == "FeatureCollection"
features = geojson["features"]
assert len(features) == 36
feature0_props = features[0]["properties"]
assert feature0_props["name"] == "Bay Colony, Port Royale"
assert feature0_props["Demographic 1"] == "bay col dem1"
assert feature0_props["Demographic 2"] == "bay col dem2"
feature1_props = features[1]["properties"]
assert feature1_props["name"] == "NE 48th"
assert feature1_props["Demographic 1"] == "NE 48th dem1"
assert feature1_props["Demographic 2"] == "NE 48th dem2"
feature2_props = features[2]["properties"]
assert feature2_props["name"] == "Linden Pointe Apartments"
assert feature2_props["Demographic 1"] is None
assert feature2_props["Demographic 2"] is None
data_map.add_to(m)
_, save_path = tempfile.mkstemp(suffix=".html")
m.save(save_path)
Path(save_path).unlink()
def test_make_map_tooltip_without_alias():
data = standard_data
data_map = gis.make_map(shapefile_path, data, join_on="name")
children = data_map._children
tooltip = children.popitem(last=False)[1] # Get first tooltip
assert tooltip.fields == ["Demographic 1", "Demographic 2"]
assert tooltip.aliases == ["Demographic 1", "Demographic 2"]
def test_make_map_tooltip_with_field_include():
data = standard_data
fields = list(data.keys())
data_map = gis.make_map(shapefile_path, data, join_on="name", include=fields)
children = data_map._children
tooltip = children.popitem(last=False)[1] # Get first tooltip
assert tooltip.fields == ["name", "Demographic 1", "Demographic 2"]
assert tooltip.aliases == ["name", "Demographic 1", "Demographic 2"]
def test_make_map_tooltip_with_alias_include():
data = standard_data
aliases = {
"name": "Name",
"Demographic 1": "Population",
}
data_map = gis.make_map(shapefile_path, data, join_on="name", include=aliases)
children = data_map._children
tooltip = children.popitem(last=False)[1] # Get first tooltip
assert tooltip.fields == ["name", "Demographic 1"]
assert tooltip.aliases == ["Name", "Population"]
# Percentage of hispanic or latino origin people out of the total respondants (to color by)
data["% Hispanic or Latino Origin"] = [
float(his) / float(tot) * 100 if not tot == 0 else None
for tot, his in zip(data["B03003_001E"], data["B03003_003E"])
]
# Formatted nicely for display
data["pretty format"] = [f"{round(x, 1)}%" for x in data["% Hispanic or Latino Origin"]]
# What to include
aliases = {
"NAME": "Name",
"GEOID": "GEOID",
"B03003_001E": "Total",
"B03003_003E": "Hispanic or Latino Origin",
"B03003_002E": "Not Hispanic or Latino Origin",
"pretty format": "% Hispanic or Latino Origin",
}
# Make the map!
gis.make_map(
shapefile_dir,
data,
join_on="GEOID",
color_by="% Hispanic or Latino Origin",
include=aliases,
save_to=here / "tx-map.html",
trim=True,
)
if not re.match(r"B02001_00[2-9]E", census_code):
continue
label = f"% {description}"
valid_data[label] = [
float(race) / float(tot) * 100 if not tot == "0" else None for tot,
race in zip(valid_data["B02001_001E"], valid_data[census_code])
]
percentage_labels.append(label)
# Log the percentage label so it shows up nicely in the tooltip
variables.update({label: label for label in percentage_labels})
# Make all the maps!
for census_code, description in variables.items():
if not re.match(r"B02001_00[2-9]E", census_code):
continue
gis.make_map(
shapefile_dir,
valid_data,
join_on="ZCTA5CE10",
include=variables,
color_by=f"% {description}",
save_to=here / f"tx-zcta-map-{census_code}.html",
trim=True, # We definitely don't want all us zip codes on this map
)
def test_make_map_exception_for_value_not_list():
data = standard_data.copy()
data["Demographic 1"] = "I am not a list"
with pytest.raises(ValueError):
gis.make_map(shapefile_path, data, join_on="name")
def test_make_map_exception_for_mismatched_data_size():
data = standard_data.copy()
data["name"] = data["name"] + ["extra element"]
with pytest.raises(ValueError):
gis.make_map(shapefile_path, data, join_on="name")
def test_make_map_exception_for_bad_join_key():
data = standard_data
with pytest.raises(KeyError):
gis.make_map(shapefile_path, data, join_on="Name") # note caps
#
# To make it possible to join the census data with the shapefile, we need to make
# a column that matches between the census data and the shapefile properties. For this
# example, we can simply combine the census data state, county, and tract
data["GEOID"] = [
s + c + t for s, c, t in zip(data["state"], data["county"], data["tract"])
]
# Right now the median household income is stored as a string (e.g. "11456.84"). This
# is great for viewing, but makes it hard to do any numerical calculations with.
# Here we will create column of "Median Household Income" but as a float (a number,
# e.g. 11456.84). This will be used to generate the colormap.
data["Median Household Income"] = [float(x) for x in data["DP03_0062E"]]
# Just to make the map prettier, it would be nice to format the median household
# income with a "$" and commas between every third number (e.g. "$11,456.84").
# This will be shown in the map's tooltip.
data["Median Household Income (pretty format)"] = [
"${:,.2f}".format(x) for x in data["Median Household Income"]
]
# Make and save GIS GeoJson map object of "Congressional Districts"
# Shapefile from: https://www.census.gov/cgi-bin/geo/shapefiles/index.php
gis.make_map(
shapefile_path,
data,
join_on="GEOID",
color_by="Median Household Income",
save_to=here / "co-map.html",
)
