Open in Colab

Mapping Your Data#

This template file shows how you can create maps (static and interactive).

import geopandas as gpd
import folium
import matplotlib.pyplot as plt
!pip install contextily
import contextily as cx

Load your data#

In Google Colab, click on the folder icon to the left of the screen. If it says “Connecting to a runtime to enable file browsing,” you may need to wait a few seconds for Google Colab to connect and load. Click the Upload icon (the left-most icon) and select your file on your computer.

You will see a pop-up that says Warning: Ensure that your files are saved elsewhere. This runtime's files will be deleted when this runtime is terminated. This is because, once you upload your data, Google Colab creates a copy of the data in your browser, which will be deleted once you close the browser. The data on your computer will remain the same, and nothing will be changed even if you make edits here in Google Colab.

Here is how to upload your spatial data based on the different file types.

  1. Shapefile (.shp):

if you uploaded a zipped shapefile:

data = gpd.read_file('name_of_your_file.zip')

or if you uploaded the separate shapefiles (.SHP, .DBF, .SHX, etc.)

data = gpd.read_file('name_of_your_file.shp')

  1. GeoJSON (.geojson):

data = gpd.read_file('name_of_your_file.geojson')

  1. Geopackage (.gpkg):

data = gpd.read_file('name_of_your_file.gpkg', layer='name_of_your_layer')

  1. Geodatabase (.gdb) - must be zipped first:

data = gpd.read_file('name_of_your_zipped_gdb.zip', layer='name_of_your_layer')

# Enter code here
data =

For this example, we’ll use the GLOBE Observer Mosquito Habitat Mapper dataset, filtered to Florida:

data = gpd.read_file('https://github.com/geo-di-lab/emerge-lessons/raw/refs/heads/main/docs/data/globe_mosquito.zip')
fl = gpd.read_file('https://github.com/geo-di-lab/emerge-lessons/raw/refs/heads/main/docs/data/florida_boundary.geojson')[['geometry']]

data = gpd.sjoin(data, fl, how="inner", predicate='intersects') \
          .drop(columns=['index_right']) \
          .reset_index(drop=True)

# View first 5 rows of the table
data.head()
CountryCode CountryName Elevation AbdomenCloseupPhotoUrls BreedingGroundEliminated Comments DataSource ExtraData Genus GlobeTeams ... WaterSourcePhotoUrls WaterSourceType OrganizationId OrganizationName Protocol SiteId SiteName MeasuredDate LarvaeCountProcessed geometry
0 USA United States 18.6 None true Water is kinda brown and dirty, there are lots... GLOBE Observer App LarvaeVisibleNotSure None None ... https://data.globe.gov/system/photos/2024/10/0... still: lake/pond/swamp 17043304 United States of America Citizen Science mosquito_habitat_mapper 366553 17RLM547097 2024-10-06 0.0 POINT (-82.47913 28.10484)
1 USA United States 17.8 None false This water is mostly clear, next to flowing wa... GLOBE Observer App LarvaeVisibleYes None None ... https://data.globe.gov/system/photos/2024/10/0... flowing: still water found next to river or st... 17043304 United States of America Citizen Science mosquito_habitat_mapper 366554 17RLM547096 2024-10-06 7.0 POINT (-82.47912 28.10394)
2 USA United States 19.6 None false None GLOBE Observer App LarvaeVisibleNotSure None None ... https://data.globe.gov/system/photos/2024/10/0... still: lake/pond/swamp 17043304 United States of America Citizen Science mosquito_habitat_mapper 366558 17RLM545096 2024-10-06 NaN POINT (-82.48116 28.10392)
3 USA United States 1.4 None false Brown standing water GLOBE Observer App LarvaeVisibleYes None None ... https://data.globe.gov/system/photos/2024/09/1... still: lake/pond/swamp 17043304 United States of America Citizen Science mosquito_habitat_mapper 364622 17RLL333667 2024-09-16 80.0 POINT (-82.69086 27.71431)
4 USA United States 29.5 None false None GLOBE Observer App LarvaeVisibleNo None None ... None still: lake/pond/swamp 17043304 United States of America Citizen Science mosquito_habitat_mapper 348376 17RMM705497 2024-04-09 0.0 POINT (-81.30136 28.47356)

5 rows × 36 columns

# View statistics like mean, min, max for each column
data.describe()
MeasuredAt MeasurementLatitude MeasurementLongitude LarvaeCountProcessed
count 615 615.000000 615.000000 377.000000
mean 2020-11-29 01:34:37.073000 26.873566 -81.030581 6.336870
min 2018-03-17 16:09:00 24.995600 -86.457000 0.000000
25% 2019-08-24 15:33:30 26.339450 -81.943400 0.000000
50% 2020-07-06 20:00:00 26.599800 -80.678600 0.000000
75% 2021-09-15 19:15:00 27.427750 -80.114600 0.000000
max 2024-10-06 20:33:00 30.522400 -80.056200 300.000000
std NaN 0.928048 1.004293 28.153371

Make a simple map to see the data.

data.plot()

<Axes: >
../../_images/64a253b95772a182ea24f1d8aaeab2594c06a417696f484a9f6ad97eae81f29e.png

Add a basemap using contextily.

Note: We use .to_crs(epsg=3857) to set the data to a coordinate reference system that is consistent with the basemap (the map in the background) that we want to add.

ax = data.to_crs(epsg=3857).plot(color="gold",        # Name of color for points
                                 edgecolor="black",
                                 markersize=25)
cx.add_basemap(ax)
ax.axis('off')
plt.show()
../../_images/3983e5aeeafba9b1f56237675c952150562a490ffa7a7472c146d02dddec31b4.png

You can set the color of the points. Here is a list of named colors: List of colors

Create an interactive map using folium.#

If your dataset is made up of points#

For this example, we’ll continue using the GLOBE Observer Mosquito Habitat Mapper Florida dataset.

In the code below, you can define “popup_content” which will appear when you click on a point. For example,

popup_content = f"""<b>Label1:</b> {row['Column1']}<br>
                    <b>Label2:</b> {row['Column2']}<br>
                    <b>Label3:</b> {row['Column3']}"""

In this code, <b>Name:</b> indicates that the word “Name:” will be bolded because it is sandwiched between <b> and </b>. The <br> indicates a break, which will put the following text on a new line.

To customize this code, replace Column1, Column2, and Column3 with the exact name of the columns in your dataset. Then, replace Label1, Label2, and Label3 with what you want to appear in the pop-up to describe each column. These labels can be the same as your column names or they can be more descriptive. For example, if your column name is “lat,” your label name might be “Latitude.”

# Create empty map zoomed to Florida
map = folium.Map(location=[28.263363, -83.497652], tiles="CartoDB positron", zoom_start=7)

# Add each point as a marker on the map
for idx, row in data.iterrows():
    popup_content = f"""<b>Label1:</b> {row['Column1']}<br>
                        <b>Label2:</b> {row['Column2']}<br>
                        <b>Label3:</b> {row['Column3']}"""
    folium.Marker(
        location=[row.geometry.y, row.geometry.x],
        popup=folium.Popup(popup_content, max_width=300)
    ).add_to(map)

display(map)
Make this Notebook Trusted to load map: File -> Trust Notebook

If your dataset is made up of polygons#

For this example, we’ll use Florida county boundaries:

data = gpd.read_file('https://github.com/geo-di-lab/emerge-lessons/raw/refs/heads/main/docs/data/florida_counties.geojson')

# Write the name of a column from your dataset that you want to appear in the pop-up
column_name = ""

# Create empty map zoomed to Florida
map = folium.Map(location=[28.263363, -83.497652], tiles="CartoDB positron", zoom_start=7)

# Add each polygon as a marker on the map
for idx, row in data.iterrows():
    # Simplify geometry to make the map load faster
    sim_geo = gpd.GeoSeries(row["geometry"]).simplify(tolerance=0.001)
    geo_j = sim_geo.to_json()
    geo_j = folium.GeoJson(data=geo_j, style_function=lambda x: {"fillColor": "blue"})
    folium.Popup(row[column_name]).add_to(geo_j)
    geo_j.add_to(map)

display(map)
Make this Notebook Trusted to load map: File -> Trust Notebook

References: