Introduction to GLOBE Data#
We will examine two GLOBE datasets: Mosquito Habitat Mapper and Land Cover.
You can view an interactive dashboard of this data.
You can run the following code using Google Colab, which runs on your browser (no installations required).
First, we’ll load the Python packages, which gives us more options with our code.
import pandas as pd # For working with data
pd.set_option("display.max_columns", None) # Lets us see all columns of the data instead of just a preview
import geopandas as gpd # For working with spatial data
import numpy as np # For working with numbers
import matplotlib.pyplot as plt # For making graphs
from datetime import date # For formatting dates
from PIL import Image # For getting and displaying images from links
import requests # For getting information from links
from io import BytesIO # For working with types of input and output
Mosquito Habitat Mapper#
We’ll read a saved copy of the GLOBE data, stored as a GeoParquet file so that it loads quickly and reliably.
url = "https://github.com/geo-di-lab/geoemerge/raw/refs/heads/main/data/mosquito_habitat_mapper_orig.parquet"
data = gpd.read_parquet(BytesIO(requests.get(url).content))
If you get an error NameError: name 'gpd' is not defined, go to the top of this notebook and click the arrow next to the first code block starting with import geopandas as gpd. This will install the packages needed for the rest of the code.
Optional: get the most recent data
The file above is a saved copy of the GLOBE database, including data submitted up to August 2026. If you want the newest observations instead, you can download them directly from the GLOBE API.
Go to the GLOBE API Interface.
Click the second option,
GET /v1/measurement/protocol/measureddate/ Find measurements by protocol and measured date range.Click
Try it outin the expanded section.For “protocols” choose
mosquito_habitat_mapper.Enter your desired start and end date in the format Year-Month-Day (like 2026-08-31).
Set “geojson” to
TRUE.Set “sample” to
FALSE.Click
Executeand wait for it to finish loading.Scroll down to “Response body” and click
Download.Upload the file to this Google Colab page using the folder icon on the left.
Replace the cell above with the name of your file, in quotes like below, and re-run that cell.
data = gpd.read_file("")
View the first 10 rows of the data, which are the most recently collected entries submitted to the GLOBE Observer App.
data.head(10)
| countryCode | countryName | elevation | mosquitohabitatmapperAbdomenCloseupPhotoUrls | mosquitohabitatmapperBreedingGroundEliminated | mosquitohabitatmapperComments | mosquitohabitatmapperDataSource | mosquitohabitatmapperExtraData | mosquitohabitatmapperGenus | mosquitohabitatmapperGlobeTeams | mosquitohabitatmapperLarvaFullBodyPhotoUrls | mosquitohabitatmapperLarvaeCount | mosquitohabitatmapperLastIdentifyStage | mosquitohabitatmapperLocationAccuracyM | mosquitohabitatmapperLocationMethod | mosquitohabitatmapperMeasuredAt | mosquitohabitatmapperMeasurementElevation | mosquitohabitatmapperMeasurementLatitude | mosquitohabitatmapperMeasurementLongitude | mosquitohabitatmapperMosquitoAdults | mosquitohabitatmapperMosquitoEggCount | mosquitohabitatmapperMosquitoEggs | mosquitohabitatmapperMosquitoHabitatMapperId | mosquitohabitatmapperMosquitoPupae | mosquitohabitatmapperSpecies | mosquitohabitatmapperUserid | mosquitohabitatmapperWaterSource | mosquitohabitatmapperWaterSourcePhotoUrls | mosquitohabitatmapperWaterSourceType | organizationId | organizationName | protocol | siteId | siteName | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | THA | Thailand | 27.3 | null | true | null | GLOBE Observer App | LarvaeVisibleNo | null | [Western GLOBE Community Thailand] | null | 0 | null | 4 | automatic | 2026-08-31 02:10:00 | 0 | 13.9537 | 99.6878 | null | null | null | 51237 | false | null | 183949365 | cement, metal or plastic tank | https://data.globe.gov/system/photos/2026/08/3... | container: artificial | 106647949 | Wat Wang Sala School | mosquito_habitat_mapper | 424480 | 47PNR742427 | POINT (99.68694 13.95358) |
| 1 | GTM | Guatemala | 1494.9 | null | true | null | GLOBE Observer App | LarvaeVisibleNo | null | [CENCAR, dominicana2, EMERGE GEO DI Guatemala,... | null | 0 | null | 128 | automatic | 2026-08-29 20:28:00 | 0 | 14.5977 | -90.5589 | null | null | null | 51235 | false | null | 12781123 | plant clumps (bamboo etc) | null | container: natural | 112712735 | STEAM Program Guatemala | mosquito_habitat_mapper | 324426 | 15PYS629152 | POINT (-90.55974 14.59732) |
| 2 | TUR | Turkey | 4.2 | null | true | null | GLOBE Observer App | LarvaeVisibleNo | null | NaN | null | 0 | null | 11 | automatic | 2026-08-29 11:20:00 | 0 | 39.1478 | 26.8284 | null | null | null | 51233 | false | null | 193050607 | discarded: other | https://data.globe.gov/system/photos/2026/08/2... | container: artificial | 17542446 | Turkey Citizen Science | mosquito_habitat_mapper | 424761 | 35SMD851331 | POINT (26.82757 39.14697) |
| 3 | USA | United States | 95.9 | null | false | Active fish feeding | GLOBE Observer App | LarvaeVisibleNo | null | [Midlands Overwatch ] | null | 0 | null | 3 | automatic | 2026-08-28 15:29:00 | 0 | 34.1429 | -80.9579 | null | null | null | 51228 | false | null | 161454935 | puddle or still water next to a creek, stream ... | null | flowing: still water found next to river or st... | 17043304 | United States of America Citizen Science | mosquito_habitat_mapper | 419154 | 17SNT038780 | POINT (-80.95878 34.14289) |
| 4 | IND | India | 25.4 | null | true | null | GLOBE Observer App | LarvaeVisibleNo | null | NaN | null | 0 | null | 38 | automatic | 2026-08-28 11:26:00 | 0 | 19.2038 | 72.9821 | null | null | null | 51225 | false | null | 193290387 | puddle, vehicle or animal tracks | null | still: lake/pond/swamp | 17179766 | India Citizen Science | mosquito_habitat_mapper | 424695 | 43QBB878246 | POINT (72.98171 19.20373) |
| 5 | USA | United States | 106.2 | null | false | null | GLOBE Observer App | LarvaeVisibleNo | null | [Midlands Overwatch ] | null | 0 | null | 3 | automatic | 2026-08-28 16:19:00 | 0 | 34.1443 | -80.9579 | null | null | null | 51230 | false | null | 161454935 | pond | null | still: lake/pond/swamp | 17043304 | United States of America Citizen Science | mosquito_habitat_mapper | 419155 | 17SNT038781 | POINT (-80.95878 34.14379) |
| 6 | GTM | Guatemala | 1519.3 | null | false | null | GLOBE Observer App | LarvaeVisibleYes | null | [EMERGE GEO DI Guatemala] | https://data.globe.gov/system/photos/2026/08/2... | 4000 | identify-siphon-pecten | 15 | automatic | 2026-08-28 23:58:00 | 0 | 14.6279 | -90.5374 | true | null | true | 51232 | true | null | 190813680 | adult mosquito trap | https://data.globe.gov/system/photos/2026/08/2... | container: artificial | 19879312 | Guatemala Citizen Science | mosquito_habitat_mapper | 424749 | 15PYS652186 | POINT (-90.53807 14.62781) |
| 7 | THA | Thailand | 10.4 | null | true | null | GLOBE Observer App | LarvaeVisibleYes | null | NaN | null | 15 | identify | 48 | automatic | 2026-08-28 03:41:00 | 0 | 8.5253 | 99.9732 | false | null | true | 51223 | false | null | 193277208 | dish or pot | https://data.globe.gov/system/photos/2026/08/2... | container: artificial | 17615655 | Thailand Citizen Science | mosquito_habitat_mapper | 424687 | 47PPK071425 | POINT (99.97312 8.52524) |
| 8 | USA | United States | 99.4 | null | false | Active habitat for fish. | GLOBE Observer App | LarvaeVisibleNo | null | [Midlands Overwatch ] | null | 0 | null | 3 | automatic | 2026-08-28 15:14:00 | 0 | 34.1424 | -80.9585 | null | null | null | 51227 | false | null | 161454935 | swamp or wetland | null | still: lake/pond/swamp | 17043304 | United States of America Citizen Science | mosquito_habitat_mapper | 424718 | 17SNT038779 | POINT (-80.95878 34.14199) |
| 9 | USA | United States | 105.3 | null | false | Water running | GLOBE Observer App | LarvaeVisibleNo | null | [Midlands Overwatch ] | null | 0 | null | 3 | automatic | 2026-08-28 16:09:00 | 0 | 34.1447 | -80.9587 | null | null | null | 51229 | false | null | 161454935 | puddle or still water next to a creek, stream ... | null | flowing: still water found next to river or st... | 17043304 | United States of America Citizen Science | mosquito_habitat_mapper | 424725 | 17SNT038782 | POINT (-80.95878 34.14469) |
Let’s use the info() function to learn more about what the dataset contains.
data.info()
<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 49464 entries, 0 to 49463
Data columns (total 35 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 countryCode 49378 non-null str
1 countryName 49378 non-null str
2 elevation 49464 non-null str
3 mosquitohabitatmapperAbdomenCloseupPhotoUrls 49464 non-null str
4 mosquitohabitatmapperBreedingGroundEliminated 49464 non-null str
5 mosquitohabitatmapperComments 49464 non-null str
6 mosquitohabitatmapperDataSource 49464 non-null str
7 mosquitohabitatmapperExtraData 49464 non-null str
8 mosquitohabitatmapperGenus 49464 non-null str
9 mosquitohabitatmapperGlobeTeams 18279 non-null str
10 mosquitohabitatmapperLarvaFullBodyPhotoUrls 49464 non-null str
11 mosquitohabitatmapperLarvaeCount 49464 non-null str
12 mosquitohabitatmapperLastIdentifyStage 49464 non-null str
13 mosquitohabitatmapperLocationAccuracyM 29746 non-null str
14 mosquitohabitatmapperLocationMethod 29746 non-null str
15 mosquitohabitatmapperMeasuredAt 49464 non-null datetime64[ms]
16 mosquitohabitatmapperMeasurementElevation 49447 non-null str
17 mosquitohabitatmapperMeasurementLatitude 49447 non-null str
18 mosquitohabitatmapperMeasurementLongitude 49447 non-null str
19 mosquitohabitatmapperMosquitoAdults 49464 non-null str
20 mosquitohabitatmapperMosquitoEggCount 49464 non-null str
21 mosquitohabitatmapperMosquitoEggs 49464 non-null str
22 mosquitohabitatmapperMosquitoHabitatMapperId 49464 non-null str
23 mosquitohabitatmapperMosquitoPupae 49464 non-null str
24 mosquitohabitatmapperSpecies 49464 non-null str
25 mosquitohabitatmapperUserid 49464 non-null str
26 mosquitohabitatmapperWaterSource 49464 non-null str
27 mosquitohabitatmapperWaterSourcePhotoUrls 49464 non-null str
28 mosquitohabitatmapperWaterSourceType 49464 non-null str
29 organizationId 49464 non-null str
30 organizationName 49378 non-null str
31 protocol 49464 non-null str
32 siteId 49464 non-null str
33 siteName 49464 non-null str
34 geometry 49464 non-null geometry
dtypes: datetime64[ms](1), geometry(1), str(33)
memory usage: 30.5 MB
We see information about each column, how many non-null rows there are (non-null means not missing), and the type (a dtype of “object” means it is text). We see that we also have a datetime column, which would be great for analyzing the data over time. The geometry column gives us information about where the citizen scientist collected the data about mosquitoes.
However, there are some columns that are currently stored as an “object” when we want then to be stored as a “float” (decimal) or “int” (whole number). Also, for some rows, if the value is missing, there is the word “null,” which can be confused as an actual value. We’ll replace this with NAN, which Python reads as empty rather than an object.
Note: The column ‘mosquitohabitatmapperLarvaeCount’ stores numbers in addition to ranges like 1-25. However, we need to simplify these ranges to a single number to make them easier to store in the dataset and more consistent. So, if there is a range, we will replace it with the lower value of the range.
# Let's remove the "mosquitomapper" in front of all the column names to make it easier to see the names
new_column_names = data.columns.str.replace("mosquitohabitatmapper", "")
# Make the first letter of each column name capitalized (for consistency) except for geometry
new_column_names = [name[0].upper() + name[1:] if name != 'geometry' else name for name in new_column_names]
data.columns = new_column_names
data.info()
<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 49464 entries, 0 to 49463
Data columns (total 35 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 CountryCode 49378 non-null str
1 CountryName 49378 non-null str
2 Elevation 49464 non-null str
3 AbdomenCloseupPhotoUrls 49464 non-null str
4 BreedingGroundEliminated 49464 non-null str
5 Comments 49464 non-null str
6 DataSource 49464 non-null str
7 ExtraData 49464 non-null str
8 Genus 49464 non-null str
9 GlobeTeams 18279 non-null str
10 LarvaFullBodyPhotoUrls 49464 non-null str
11 LarvaeCount 49464 non-null str
12 LastIdentifyStage 49464 non-null str
13 LocationAccuracyM 29746 non-null str
14 LocationMethod 29746 non-null str
15 MeasuredAt 49464 non-null datetime64[ms]
16 MeasurementElevation 49447 non-null str
17 MeasurementLatitude 49447 non-null str
18 MeasurementLongitude 49447 non-null str
19 MosquitoAdults 49464 non-null str
20 MosquitoEggCount 49464 non-null str
21 MosquitoEggs 49464 non-null str
22 MosquitoHabitatMapperId 49464 non-null str
23 MosquitoPupae 49464 non-null str
24 Species 49464 non-null str
25 Userid 49464 non-null str
26 WaterSource 49464 non-null str
27 WaterSourcePhotoUrls 49464 non-null str
28 WaterSourceType 49464 non-null str
29 OrganizationId 49464 non-null str
30 OrganizationName 49378 non-null str
31 Protocol 49464 non-null str
32 SiteId 49464 non-null str
33 SiteName 49464 non-null str
34 geometry 49464 non-null geometry
dtypes: datetime64[ms](1), geometry(1), str(33)
memory usage: 30.5 MB
# Add new column for date, not including the time
data['MeasuredDate'] = data['MeasuredAt'].dt.date
# The LarvaeCount column has some grouped entries like '1-25' that we will replace with the center value
data['LarvaeCountProcessed'] = data['LarvaeCount'].replace({
'1-25': 13,
'26-50': 38,
'51-100': 76,
'more than 100': 100,
'null': np.nan
})
# If the LarvaeCount is very long (more than 10 characters), then it is likely an error, so we'll replace with NAN
data.loc[data['LarvaeCountProcessed'].str.len() > 10, 'LarvaeCountProcessed'] = np.nan
numeric_cols = ['LarvaeCountProcessed', 'MeasurementLatitude', 'MeasurementLongitude']
data[numeric_cols] = data[numeric_cols].apply(pd.to_numeric)
# Drop the MosquitoEggCount column because it is all null
data = data.drop(columns=['MosquitoEggCount'])
# Replace the word 'null' with NAN to ensure it is stored as an empty value instead of a word
data = data.replace('null', np.nan)
data.info()
<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 49464 entries, 0 to 49463
Data columns (total 36 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 CountryCode 49378 non-null str
1 CountryName 49378 non-null str
2 Elevation 49464 non-null str
3 AbdomenCloseupPhotoUrls 1005 non-null str
4 BreedingGroundEliminated 49403 non-null str
5 Comments 4725 non-null str
6 DataSource 49464 non-null str
7 ExtraData 17029 non-null str
8 Genus 4837 non-null str
9 GlobeTeams 18279 non-null str
10 LarvaFullBodyPhotoUrls 9422 non-null str
11 LarvaeCount 29327 non-null str
12 LastIdentifyStage 32424 non-null str
13 LocationAccuracyM 18008 non-null str
14 LocationMethod 23007 non-null str
15 MeasuredAt 49464 non-null datetime64[ms]
16 MeasurementElevation 49447 non-null str
17 MeasurementLatitude 49447 non-null float64
18 MeasurementLongitude 49447 non-null float64
19 MosquitoAdults 18559 non-null str
20 MosquitoEggs 18548 non-null str
21 MosquitoHabitatMapperId 49464 non-null str
22 MosquitoPupae 46678 non-null str
23 Species 1372 non-null str
24 Userid 49464 non-null str
25 WaterSource 49464 non-null str
26 WaterSourcePhotoUrls 38606 non-null str
27 WaterSourceType 49464 non-null str
28 OrganizationId 49378 non-null str
29 OrganizationName 49378 non-null str
30 Protocol 49464 non-null str
31 SiteId 49464 non-null str
32 SiteName 49464 non-null str
33 geometry 49464 non-null geometry
34 MeasuredDate 49464 non-null object
35 LarvaeCountProcessed 29324 non-null float64
dtypes: datetime64[ms](1), float64(3), geometry(1), object(1), str(30)
memory usage: 28.6+ MB
The final data cleaning step we will perform for now is to remove points that have invalid coordinates. Sometimes, the latitude and longitude may be incorrectly reported, placing the point at the middle of the ocean. We want to remove any points that are not on land. To do that, let’s load a publicly-available file of country boundaries. The country boundaries (excluding Antarctica) were downloaded from ArcGIS Data and Maps.
countries = gpd.read_file('https://github.com/geo-di-lab/emerge-lessons/raw/refs/heads/main/docs/data/world_countries.zip')[['COUNTRY', 'geometry']].to_crs(4326)
countries.plot()
<Axes: >
We will use a spatial join (sjoin) to get all of the data that “intersects” the countries layer, meaning it is either within or on the boundary of a country.
data = gpd.sjoin(data, countries, how="inner", predicate='intersects') \
.drop(columns=['index_right', 'COUNTRY']) \
.reset_index(drop=True)
data
| CountryCode | CountryName | Elevation | AbdomenCloseupPhotoUrls | BreedingGroundEliminated | Comments | DataSource | ExtraData | Genus | GlobeTeams | LarvaFullBodyPhotoUrls | LarvaeCount | LastIdentifyStage | LocationAccuracyM | LocationMethod | MeasuredAt | MeasurementElevation | MeasurementLatitude | MeasurementLongitude | MosquitoAdults | MosquitoEggs | MosquitoHabitatMapperId | MosquitoPupae | Species | Userid | WaterSource | WaterSourcePhotoUrls | WaterSourceType | OrganizationId | OrganizationName | Protocol | SiteId | SiteName | geometry | MeasuredDate | LarvaeCountProcessed | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | THA | Thailand | 27.3 | NaN | true | NaN | GLOBE Observer App | LarvaeVisibleNo | NaN | [Western GLOBE Community Thailand] | NaN | 0 | NaN | 4 | automatic | 2026-08-31 02:10:00 | 0 | 13.953700 | 99.687800 | NaN | NaN | 51237 | false | NaN | 183949365 | cement, metal or plastic tank | https://data.globe.gov/system/photos/2026/08/3... | container: artificial | 106647949 | Wat Wang Sala School | mosquito_habitat_mapper | 424480 | 47PNR742427 | POINT (99.68694 13.95358) | 2026-08-31 | 0.0 |
| 1 | GTM | Guatemala | 1494.9 | NaN | true | NaN | GLOBE Observer App | LarvaeVisibleNo | NaN | [CENCAR, dominicana2, EMERGE GEO DI Guatemala,... | NaN | 0 | NaN | 128 | automatic | 2026-08-29 20:28:00 | 0 | 14.597700 | -90.558900 | NaN | NaN | 51235 | false | NaN | 12781123 | plant clumps (bamboo etc) | NaN | container: natural | 112712735 | STEAM Program Guatemala | mosquito_habitat_mapper | 324426 | 15PYS629152 | POINT (-90.55974 14.59732) | 2026-08-29 | 0.0 |
| 2 | TUR | Turkey | 4.2 | NaN | true | NaN | GLOBE Observer App | LarvaeVisibleNo | NaN | NaN | NaN | 0 | NaN | 11 | automatic | 2026-08-29 11:20:00 | 0 | 39.147800 | 26.828400 | NaN | NaN | 51233 | false | NaN | 193050607 | discarded: other | https://data.globe.gov/system/photos/2026/08/2... | container: artificial | 17542446 | Turkey Citizen Science | mosquito_habitat_mapper | 424761 | 35SMD851331 | POINT (26.82757 39.14697) | 2026-08-29 | 0.0 |
| 3 | USA | United States | 95.9 | NaN | false | Active fish feeding | GLOBE Observer App | LarvaeVisibleNo | NaN | [Midlands Overwatch ] | NaN | 0 | NaN | 3 | automatic | 2026-08-28 15:29:00 | 0 | 34.142900 | -80.957900 | NaN | NaN | 51228 | false | NaN | 161454935 | puddle or still water next to a creek, stream ... | NaN | flowing: still water found next to river or st... | 17043304 | United States of America Citizen Science | mosquito_habitat_mapper | 419154 | 17SNT038780 | POINT (-80.95878 34.14289) | 2026-08-28 | 0.0 |
| 4 | IND | India | 25.4 | NaN | true | NaN | GLOBE Observer App | LarvaeVisibleNo | NaN | NaN | NaN | 0 | NaN | 38 | automatic | 2026-08-28 11:26:00 | 0 | 19.203800 | 72.982100 | NaN | NaN | 51225 | false | NaN | 193290387 | puddle, vehicle or animal tracks | NaN | still: lake/pond/swamp | 17179766 | India Citizen Science | mosquito_habitat_mapper | 424695 | 43QBB878246 | POINT (72.98171 19.20373) | 2026-08-28 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 49039 | USA | United States | 485.3 | NaN | false | NaN | GLOBE Observer App | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2017-05-29 19:08:00 | 485.3 | 34.124374 | -117.749344 | NaN | NaN | 4 | NaN | NaN | 11114 | fountain or bird bath | https://data.globe.gov/system/photos/2017/05/2... | container: artificial | 12993805 | Canyon Weather | mosquito_habitat_mapper | 35802 | 11SMT309762 | POINT (-117.74934 34.12437) | 2017-05-29 | NaN |
| 49040 | USA | United States | 467.6 | NaN | true | NaN | GLOBE Observer App | NaN | NaN | NaN | NaN | 0 | identify | NaN | NaN | 2017-05-29 19:12:00 | 467.6 | 34.123473 | -117.749336 | false | false | 6 | false | NaN | 11114 | cement, metal or plastic tank | https://data.globe.gov/system/photos/2017/05/2... | container: artificial | 12993805 | Canyon Weather | mosquito_habitat_mapper | 35822 | 11SMT309761 | POINT (-117.74934 34.12347) | 2017-05-29 | 0.0 |
| 49041 | USA | United States | 467.6 | NaN | true | NaN | GLOBE Observer App | NaN | NaN | NaN | NaN | 0 | identify-siphon-pecten | NaN | NaN | 2017-05-29 19:10:00 | 467.6 | 34.123473 | -117.749336 | false | false | 5 | false | NaN | 11114 | fountain or bird bath | https://data.globe.gov/system/photos/2017/05/2... | container: artificial | 12993805 | Canyon Weather | mosquito_habitat_mapper | 35822 | 11SMT309761 | POINT (-117.74934 34.12347) | 2017-05-29 | 0.0 |
| 49042 | USA | United States | 74.5 | NaN | true | NaN | GLOBE Observer App | NaN | NaN | NaN | NaN | 0 | identify | NaN | NaN | 2017-05-29 20:30:00 | 74.5 | 38.857788 | -77.153990 | true | false | 7 | false | NaN | 6972382 | dish or pot | https://data.globe.gov/system/photos/2017/05/2... | container: artificial | 22467264 | Institute for Global Environmental Strategies ... | mosquito_habitat_mapper | 51934 | 18SUJ131032 | POINT (-77.15399 38.85779) | 2017-05-29 | 0.0 |
| 49043 | BRA | Brazil | 572.9 | NaN | false | NaN | GLOBE Observer App | NaN | Anopheles | NaN | https://data.globe.gov/system/photos/2017/05/2... | 1-25 | identify-no-siphon | NaN | NaN | 2017-05-29 17:27:00 | 572.9 | -23.174382 | -45.886155 | true | false | 230 | false | NaN | 24709556 | pond | https://data.globe.gov/system/photos/2017/05/2... | still: lake/pond/swamp | 17459532 | Brazil Citizen Science | mosquito_habitat_mapper | 55704 | 23KMQ093369 | POINT (-45.88616 -23.17438) | 2017-05-29 | 13.0 |
49044 rows × 36 columns
A copy of this dataset is on GitHub, and we will use this in future chapters. You do not need to download the dataset to your computer, as we will load the data directly using the link.
Explore the Data#
Let’s make a simple graph showing the number of contributions submitted by citizen scientists over time!
data_by_day = data[['SiteId', 'MeasuredDate']].groupby(['MeasuredDate'], as_index=False).count()
plt.figure(figsize=(8,4))
plt.plot(data_by_day['MeasuredDate'], data_by_day['SiteId'])
plt.xlabel("Date")
plt.ylabel("Daily Contribution")
plt.title("Daily Mosquito Habitat Mapper Contributions Over Time")
plt.show()
Let’s plot the number of contributions by country:
data_by_county = data[['SiteId', 'CountryName']].groupby(['CountryName'], as_index=False).count().sort_values(by='SiteId').tail(10)
plt.barh(data_by_county['CountryName'], data_by_county['SiteId'])
plt.xlabel("Total Contributions")
plt.ylabel("Country")
plt.title("Mosquito Mapper Contributions by Country")
plt.show()
Land Cover#
Now, we’ll review the Land Cover dataset. We’ll load it in the same way as the Mosquito dataset.
url = "https://github.com/geo-di-lab/geoemerge/raw/refs/heads/main/data/land_covers_orig.parquet"
data = gpd.read_parquet(BytesIO(requests.get(url).content))
Optional: get the most recent data
The file above is a saved copy of the GLOBE database, including data submitted up to August 2026. If you want the newest observations instead, you can download them directly from the GLOBE API.
Go to the GLOBE API Interface.
Click the second option,
GET /v1/measurement/protocol/measureddate/ Find measurements by protocol and measured date range.Click
Try it outin the expanded section.For “protocols” choose
land_covers.Enter your desired start and end date in the format Year-Month-Day (like 2026-08-31).
Set “geojson” to
TRUE.Set “sample” to
FALSE.Click
Executeand wait for it to finish loading.Scroll down to “Response body” and click
Download.Upload the file to this Google Colab page using the folder icon on the left.
Replace the cell above with the name of your file, in quotes like below, and re-run that cell.
data = gpd.read_file("")
View a list of the columns in the dataset:
data.info()
<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 67936 entries, 0 to 67935
Data columns (total 63 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 countryCode 67901 non-null str
1 countryName 67901 non-null str
2 elevation 67936 non-null str
3 landcoversDataSource 67936 non-null str
4 landcoversDownwardCaption 67936 non-null str
5 landcoversDownwardExtraData 67936 non-null str
6 landcoversDownwardPhotoUrl 67936 non-null str
7 landcoversDryGround 67936 non-null str
8 landcoversEastCaption 67936 non-null str
9 landcoversEastClassifications 67936 non-null str
10 landcoversEastExtraData 67936 non-null str
11 landcoversEastPhotoUrl 67936 non-null str
12 landcoversFeature1Caption 67936 non-null str
13 landcoversFeature1ExtraData 67936 non-null str
14 landcoversFeature1PhotoUrl 67936 non-null str
15 landcoversFeature2Caption 67936 non-null str
16 landcoversFeature2ExtraData 67936 non-null str
17 landcoversFeature2PhotoUrl 67936 non-null str
18 landcoversFeature3Caption 67936 non-null str
19 landcoversFeature3ExtraData 67936 non-null str
20 landcoversFeature3PhotoUrl 67936 non-null str
21 landcoversFeature4Caption 67936 non-null str
22 landcoversFeature4ExtraData 67936 non-null str
23 landcoversFeature4PhotoUrl 67936 non-null str
24 landcoversFieldNotes 67936 non-null str
25 landcoversGlobeTeams 33819 non-null str
26 landcoversLandCoverId 67936 non-null str
27 landcoversLeavesOnTrees 67936 non-null str
28 landcoversLocationAccuracyM 67936 non-null str
29 landcoversLocationMethod 67936 non-null str
30 landcoversMeasuredAt 67936 non-null datetime64[ms]
31 landcoversMeasurementElevation 59623 non-null str
32 landcoversMeasurementLatitude 59623 non-null str
33 landcoversMeasurementLongitude 59623 non-null str
34 landcoversMucCode 67936 non-null str
35 landcoversMucDescription 67936 non-null str
36 landcoversMucDetails 67936 non-null str
37 landcoversMuddy 67936 non-null str
38 landcoversNorthCaption 67936 non-null str
39 landcoversNorthClassifications 67936 non-null str
40 landcoversNorthExtraData 67936 non-null str
41 landcoversNorthPhotoUrl 67936 non-null str
42 landcoversRainingSnowing 67936 non-null str
43 landcoversSnowIce 67936 non-null str
44 landcoversSouthCaption 67936 non-null str
45 landcoversSouthClassifications 67936 non-null str
46 landcoversSouthExtraData 67936 non-null str
47 landcoversSouthPhotoUrl 67936 non-null str
48 landcoversStandingWater 67936 non-null str
49 landcoversUpwardCaption 67936 non-null str
50 landcoversUpwardExtraData 67936 non-null str
51 landcoversUpwardPhotoUrl 67936 non-null str
52 landcoversUserid 67936 non-null str
53 landcoversWestCaption 67936 non-null str
54 landcoversWestClassifications 67936 non-null str
55 landcoversWestExtraData 67936 non-null str
56 landcoversWestPhotoUrl 67936 non-null str
57 organizationId 67936 non-null str
58 organizationName 67902 non-null str
59 protocol 67936 non-null str
60 siteId 67936 non-null str
61 siteName 67936 non-null str
62 geometry 67936 non-null geometry
dtypes: datetime64[ms](1), geometry(1), str(61)
memory usage: 96.5 MB
# Let's remove the "landcovers" in front of all the column names to make it easier to see the names
new_column_names = data.columns.str.replace("landcovers", "")
# Make the first letter of each column name capitalized (for consistency) except for geometry
new_column_names = [name[0].upper() + name[1:] if name != 'geometry' else name for name in new_column_names]
data.columns = new_column_names
data.info()
<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 67936 entries, 0 to 67935
Data columns (total 63 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 CountryCode 67901 non-null str
1 CountryName 67901 non-null str
2 Elevation 67936 non-null str
3 DataSource 67936 non-null str
4 DownwardCaption 67936 non-null str
5 DownwardExtraData 67936 non-null str
6 DownwardPhotoUrl 67936 non-null str
7 DryGround 67936 non-null str
8 EastCaption 67936 non-null str
9 EastClassifications 67936 non-null str
10 EastExtraData 67936 non-null str
11 EastPhotoUrl 67936 non-null str
12 Feature1Caption 67936 non-null str
13 Feature1ExtraData 67936 non-null str
14 Feature1PhotoUrl 67936 non-null str
15 Feature2Caption 67936 non-null str
16 Feature2ExtraData 67936 non-null str
17 Feature2PhotoUrl 67936 non-null str
18 Feature3Caption 67936 non-null str
19 Feature3ExtraData 67936 non-null str
20 Feature3PhotoUrl 67936 non-null str
21 Feature4Caption 67936 non-null str
22 Feature4ExtraData 67936 non-null str
23 Feature4PhotoUrl 67936 non-null str
24 FieldNotes 67936 non-null str
25 GlobeTeams 33819 non-null str
26 LandCoverId 67936 non-null str
27 LeavesOnTrees 67936 non-null str
28 LocationAccuracyM 67936 non-null str
29 LocationMethod 67936 non-null str
30 MeasuredAt 67936 non-null datetime64[ms]
31 MeasurementElevation 59623 non-null str
32 MeasurementLatitude 59623 non-null str
33 MeasurementLongitude 59623 non-null str
34 MucCode 67936 non-null str
35 MucDescription 67936 non-null str
36 MucDetails 67936 non-null str
37 Muddy 67936 non-null str
38 NorthCaption 67936 non-null str
39 NorthClassifications 67936 non-null str
40 NorthExtraData 67936 non-null str
41 NorthPhotoUrl 67936 non-null str
42 RainingSnowing 67936 non-null str
43 SnowIce 67936 non-null str
44 SouthCaption 67936 non-null str
45 SouthClassifications 67936 non-null str
46 SouthExtraData 67936 non-null str
47 SouthPhotoUrl 67936 non-null str
48 StandingWater 67936 non-null str
49 UpwardCaption 67936 non-null str
50 UpwardExtraData 67936 non-null str
51 UpwardPhotoUrl 67936 non-null str
52 Userid 67936 non-null str
53 WestCaption 67936 non-null str
54 WestClassifications 67936 non-null str
55 WestExtraData 67936 non-null str
56 WestPhotoUrl 67936 non-null str
57 OrganizationId 67936 non-null str
58 OrganizationName 67902 non-null str
59 Protocol 67936 non-null str
60 SiteId 67936 non-null str
61 SiteName 67936 non-null str
62 geometry 67936 non-null geometry
dtypes: datetime64[ms](1), geometry(1), str(61)
memory usage: 96.5 MB
# Add new column for date
data['MeasuredDate'] = data['MeasuredAt'].dt.date
data.head(10)
| CountryCode | CountryName | Elevation | DataSource | DownwardCaption | DownwardExtraData | DownwardPhotoUrl | DryGround | EastCaption | EastClassifications | EastExtraData | EastPhotoUrl | Feature1Caption | Feature1ExtraData | Feature1PhotoUrl | Feature2Caption | Feature2ExtraData | Feature2PhotoUrl | Feature3Caption | Feature3ExtraData | Feature3PhotoUrl | Feature4Caption | Feature4ExtraData | Feature4PhotoUrl | FieldNotes | GlobeTeams | LandCoverId | LeavesOnTrees | LocationAccuracyM | LocationMethod | MeasuredAt | MeasurementElevation | MeasurementLatitude | MeasurementLongitude | MucCode | MucDescription | MucDetails | Muddy | NorthCaption | NorthClassifications | NorthExtraData | NorthPhotoUrl | RainingSnowing | SnowIce | SouthCaption | SouthClassifications | SouthExtraData | SouthPhotoUrl | StandingWater | UpwardCaption | UpwardExtraData | UpwardPhotoUrl | Userid | WestCaption | WestClassifications | WestExtraData | WestPhotoUrl | OrganizationId | OrganizationName | Protocol | SiteId | SiteName | geometry | MeasuredDate | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | USA | United States | 5.1 | GLOBE Observer App | null | null | https://data.globe.gov/system/photos/2026/08/3... | true | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | (none) | [IEOPCNP] | 94653 | true | 1414 | automatic | 2026-08-31 15:55:00 | 4.9 | 40.0199 | -75.0422 | null | null | false | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | false | false | null | null | null | null | false | null | null | https://data.globe.gov/system/photos/2026/08/3... | 2514462 | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | 2620510 | Institute for Earth Observations School | land_covers | 424899 | 18TVK963299 | POINT (-75.04336 40.0193) | 2026-08-31 | |
| 1 | USA | United States | 235.5 | GLOBE Observer App | null | null | https://data.globe.gov/system/photos/2026/08/3... | false | null | 10% MUC 11 (n) [Trees, Loosely Spaced, Evergre... | null | https://data.globe.gov/system/photos/2026/08/3... | null | null | null | null | null | null | null | null | null | null | null | null | (none) | NaN | 94655 | true | 8 | automatic | 2026-08-31 17:47:00 | 235.9 | 39.1857 | -86.7783 | M43 | Herbaceous/Grassland, Short Grass | false | null | 80% MUC 02 (b) [Trees, Closely Spaced, Deciduo... | null | https://data.globe.gov/system/photos/2026/08/3... | false | false | null | 30% MUC 11 (n) [Trees, Loosely Spaced, Evergre... | null | https://data.globe.gov/system/photos/2026/08/3... | false | null | null | https://data.globe.gov/system/photos/2026/08/3... | 24905746 | null | 10% MUC 11 (n) [Trees, Loosely Spaced, Evergre... | null | https://data.globe.gov/system/photos/2026/08/3... | 17043304 | United States of America Citizen Science | land_covers | 52378 | 16SEJ191374 | POINT (-86.77885 39.18563) | 2026-08-31 | |
| 2 | USA | United States | 369.3 | GLOBE Observer App | null | null | https://data.globe.gov/system/photos/2026/08/3... | true | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | null | null | null | null | null | null | null | null | null | null | null | null | (none) | NaN | 94657 | true | 9 | automatic | 2026-08-31 20:29:00 | 369.8 | 41.4361 | -81.4686 | null | null | false | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | false | false | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | false | null | null | https://data.globe.gov/system/photos/2026/08/3... | 148669632 | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | 17043304 | United States of America Citizen Science | land_covers | 391652 | 17TMF608872 | POINT (-81.4692 41.43541) | 2026-08-31 | |
| 3 | USA | United States | 2.3 | GLOBE Observer App | null | null | https://data.globe.gov/system/photos/2026/08/3... | false | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | null | ((compassData.heading: 15, compassData.horizon... | https://data.globe.gov/system/photos/2026/08/3... | Delaware River | ((compassData.heading: 301, compassData.horizo... | https://data.globe.gov/system/photos/2026/08/3... | null | null | null | null | null | null | Tacony palmyra bridge nj-pa | [IEOPCNP] | 94652 | true | 187 | automatic | 2026-08-31 14:57:00 | 2.5 | 40.0088 | -75.0408 | null | null | true | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | false | false | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | false | null | null | https://data.globe.gov/system/photos/2026/08/3... | 2514462 | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | 2620510 | Institute for Earth Observations School | land_covers | 42112 | 18TVK965287 | POINT (-75.04101 40.00849) | 2026-08-31 | |
| 4 | USA | United States | 78.2 | GLOBE Observer App | null | null | null | true | null | 10% MUC 12 (b) [Trees, Loosely Spaced, Deciduo... | null | null | null | null | null | null | null | null | null | null | null | null | null | null | (none) | [Oregon State University GEOGRAPHY] | 94647 | true | null | manual | 2026-08-31 05:12:00 | 78.7 | 53.413 | -6.8349 | M91 | Urban, Residential Property | false | null | 10% MUC 12 (b) [Trees, Loosely Spaced, Deciduo... | null | null | false | false | null | 10% MUC 12 (b) [Trees, Loosely Spaced, Deciduo... | null | null | false | null | null | null | 189554409 | null | 10% MUC 12 (b) [Trees, Loosely Spaced, Deciduo... | null | null | 17043304 | United States of America Citizen Science | land_covers | 424872 | 29UPV439203 | POINT (-6.83495 53.41212) | 2026-08-31 | |
| 5 | USA | United States | 10.0 | GLOBE Observer App | null | null | https://data.globe.gov/system/photos/2026/08/3... | true | null | 20% MUC 11 (n) [Trees, Loosely Spaced, Evergre... | null | https://data.globe.gov/system/photos/2026/08/3... | null | null | null | null | null | null | null | null | null | null | null | null | (none) | [Oregon State University GEOGRAPHY] | 94646 | true | 4 | automatic | 2026-08-31 03:47:00 | 10 | -43.6444 | 172.4635 | M43 | Herbaceous/Grassland, Short Grass | true | null | 20% MUC 11 (n) [Trees, Loosely Spaced, Evergre... | null | https://data.globe.gov/system/photos/2026/08/3... | true | false | null | 20% MUC 11 (n) [Trees, Loosely Spaced, Evergre... | null | https://data.globe.gov/system/photos/2026/08/3... | false | null | null | https://data.globe.gov/system/photos/2026/08/3... | 189472835 | null | 30% MUC 11 (n) [Trees, Loosely Spaced, Evergre... | null | https://data.globe.gov/system/photos/2026/08/3... | 17043304 | United States of America Citizen Science | land_covers | 424867 | 59GPM180665 | POINT (172.4631 -43.64514) | 2026-08-31 | |
| 6 | USA | United States | 267.9 | GLOBE Observer App | null | null | https://data.globe.gov/system/photos/2026/08/3... | true | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | null | ((compassData.heading: 260, compassData.horizo... | https://data.globe.gov/system/photos/2026/08/3... | null | ((compassData.heading: 336, compassData.horizo... | https://data.globe.gov/system/photos/2026/08/3... | null | ((compassData.heading: 279, compassData.horizo... | https://data.globe.gov/system/photos/2026/08/3... | null | ((compassData.heading: 289, compassData.horizo... | https://data.globe.gov/system/photos/2026/08/3... | (none) | NaN | 94654 | true | 10 | automatic | 2026-08-31 18:43:00 | 269.7 | 39.8439 | -86.3833 | null | null | false | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | false | false | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | false | null | null | https://data.globe.gov/system/photos/2026/08/3... | 126092159 | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | 17043304 | United States of America Citizen Science | land_covers | 361103 | 16SEK527106 | POINT (-86.38402 39.84378) | 2026-08-31 | |
| 7 | USA | United States | 176.9 | GLOBE Observer App | null | null | https://data.globe.gov/system/photos/2026/08/3... | false | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | null | ((compassData.heading: null, compassData.horiz... | https://data.globe.gov/system/photos/2026/08/3... | null | ((compassData.heading: null, compassData.horiz... | https://data.globe.gov/system/photos/2026/08/3... | null | ((compassData.heading: null, compassData.horiz... | https://data.globe.gov/system/photos/2026/08/3... | null | ((compassData.heading: null, compassData.horiz... | https://data.globe.gov/system/photos/2026/08/3... | (none) | [GLOBE New York City, NASA Response Mappers, P... | 94649 | true | 50 | automatic | 2026-08-31 04:59:00 | 172.7 | 26.0026 | 79.1663 | null | null | true | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | true | false | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | false | null | null | https://data.globe.gov/system/photos/2026/08/3... | 59830782 | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | 10532858 | GLOBE Implementation Office GLOBE v-School | land_covers | 378172 | 44RLP164772 | POINT (79.16559 26.00205) | 2026-08-31 | |
| 8 | USA | United States | 176.9 | GLOBE Observer App | null | null | https://data.globe.gov/system/photos/2026/08/3... | false | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | null | ((compassData.heading: null, compassData.horiz... | https://data.globe.gov/system/photos/2026/08/3... | null | ((compassData.heading: null, compassData.horiz... | https://data.globe.gov/system/photos/2026/08/3... | null | ((compassData.heading: null, compassData.horiz... | https://data.globe.gov/system/photos/2026/08/3... | null | ((compassData.heading: null, compassData.horiz... | https://data.globe.gov/system/photos/2026/08/3... | (none) | [GLOBE New York City, NASA Response Mappers, P... | 94650 | true | 11 | automatic | 2026-08-31 08:03:00 | 174 | 26.0029 | 79.1665 | null | null | true | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | true | false | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | false | null | null | https://data.globe.gov/system/photos/2026/08/3... | 59830782 | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | 10532858 | GLOBE Implementation Office GLOBE v-School | land_covers | 378172 | 44RLP164772 | POINT (79.16559 26.00205) | 2026-08-31 | |
| 9 | UKR | Ukraine | 250.2 | GLOBE Observer App | null | null | https://data.globe.gov/system/photos/2026/08/3... | true | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | null | null | null | null | null | null | null | null | null | null | null | null | (none) | [GLOBE Cup Europe Eurasia] | 94648 | true | 10 | automatic | 2026-08-31 06:45:00 | 252.4 | 48.9218 | 24.6934 | null | null | false | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | false | false | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | false | null | null | https://data.globe.gov/system/photos/2026/08/3... | 13068171 | null | null | null | https://data.globe.gov/system/photos/2026/08/3... | 13068008 | Ivano-Frankivsk City Environmental Station | land_covers | 160487 | 35ULQ310213 | POINT (24.69298 48.92155) | 2026-08-31 |
Replace null values:
data['FieldNotes'] = data['FieldNotes'].replace('(none)', np.nan)
data = data.replace('null', np.nan)
data.info()
<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 67936 entries, 0 to 67935
Data columns (total 64 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 CountryCode 67901 non-null str
1 CountryName 67901 non-null str
2 Elevation 67936 non-null str
3 DataSource 67936 non-null str
4 DownwardCaption 22678 non-null str
5 DownwardExtraData 22660 non-null str
6 DownwardPhotoUrl 54074 non-null str
7 DryGround 59680 non-null str
8 EastCaption 24139 non-null str
9 EastClassifications 18149 non-null str
10 EastExtraData 23904 non-null str
11 EastPhotoUrl 58002 non-null str
12 Feature1Caption 825 non-null str
13 Feature1ExtraData 3772 non-null str
14 Feature1PhotoUrl 3775 non-null str
15 Feature2Caption 334 non-null str
16 Feature2ExtraData 2286 non-null str
17 Feature2PhotoUrl 2286 non-null str
18 Feature3Caption 184 non-null str
19 Feature3ExtraData 1740 non-null str
20 Feature3PhotoUrl 1740 non-null str
21 Feature4Caption 143 non-null str
22 Feature4ExtraData 1510 non-null str
23 Feature4PhotoUrl 1510 non-null str
24 FieldNotes 23767 non-null str
25 GlobeTeams 33819 non-null str
26 LandCoverId 67936 non-null str
27 LeavesOnTrees 59680 non-null str
28 LocationAccuracyM 49496 non-null str
29 LocationMethod 59681 non-null str
30 MeasuredAt 67936 non-null datetime64[ms]
31 MeasurementElevation 59623 non-null str
32 MeasurementLatitude 59623 non-null str
33 MeasurementLongitude 59623 non-null str
34 MucCode 26651 non-null str
35 MucDescription 26648 non-null str
36 MucDetails 59681 non-null str
37 Muddy 59680 non-null str
38 NorthCaption 24439 non-null str
39 NorthClassifications 18199 non-null str
40 NorthExtraData 24204 non-null str
41 NorthPhotoUrl 58308 non-null str
42 RainingSnowing 59680 non-null str
43 SnowIce 59680 non-null str
44 SouthCaption 23999 non-null str
45 SouthClassifications 18144 non-null str
46 SouthExtraData 23774 non-null str
47 SouthPhotoUrl 57712 non-null str
48 StandingWater 59680 non-null str
49 UpwardCaption 23185 non-null str
50 UpwardExtraData 23159 non-null str
51 UpwardPhotoUrl 54918 non-null str
52 Userid 59681 non-null str
53 WestCaption 23965 non-null str
54 WestClassifications 18118 non-null str
55 WestExtraData 23751 non-null str
56 WestPhotoUrl 57544 non-null str
57 OrganizationId 67903 non-null str
58 OrganizationName 67902 non-null str
59 Protocol 67936 non-null str
60 SiteId 67936 non-null str
61 SiteName 67936 non-null str
62 geometry 67936 non-null geometry
63 MeasuredDate 67936 non-null object
dtypes: datetime64[ms](1), geometry(1), object(1), str(61)
memory usage: 90.4+ MB
Similar to the mosquito dataset, we will filter out any points that do not fall within country boundaries. If the point is over water, then we assume the coordinates were incorrectly reported and remove it from the final dataset.
data = gpd.sjoin(data, countries, how="inner", predicate='intersects') \
.drop(columns=['index_right', 'COUNTRY']) \
.reset_index(drop=True)
data
| CountryCode | CountryName | Elevation | DataSource | DownwardCaption | DownwardExtraData | DownwardPhotoUrl | DryGround | EastCaption | EastClassifications | EastExtraData | EastPhotoUrl | Feature1Caption | Feature1ExtraData | Feature1PhotoUrl | Feature2Caption | Feature2ExtraData | Feature2PhotoUrl | Feature3Caption | Feature3ExtraData | Feature3PhotoUrl | Feature4Caption | Feature4ExtraData | Feature4PhotoUrl | FieldNotes | GlobeTeams | LandCoverId | LeavesOnTrees | LocationAccuracyM | LocationMethod | MeasuredAt | MeasurementElevation | MeasurementLatitude | MeasurementLongitude | MucCode | MucDescription | MucDetails | Muddy | NorthCaption | NorthClassifications | NorthExtraData | NorthPhotoUrl | RainingSnowing | SnowIce | SouthCaption | SouthClassifications | SouthExtraData | SouthPhotoUrl | StandingWater | UpwardCaption | UpwardExtraData | UpwardPhotoUrl | Userid | WestCaption | WestClassifications | WestExtraData | WestPhotoUrl | OrganizationId | OrganizationName | Protocol | SiteId | SiteName | geometry | MeasuredDate | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | USA | United States | 5.1 | GLOBE Observer App | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | true | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | [IEOPCNP] | 94653 | true | 1414 | automatic | 2026-08-31 15:55:00 | 4.9 | 40.0199 | -75.0422 | NaN | NaN | false | NaN | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | false | false | NaN | NaN | NaN | NaN | false | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | 2514462 | NaN | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | 2620510 | Institute for Earth Observations School | land_covers | 424899 | 18TVK963299 | POINT (-75.04336 40.0193) | 2026-08-31 | |
| 1 | USA | United States | 235.5 | GLOBE Observer App | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | false | NaN | 10% MUC 11 (n) [Trees, Loosely Spaced, Evergre... | NaN | https://data.globe.gov/system/photos/2026/08/3... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 94655 | true | 8 | automatic | 2026-08-31 17:47:00 | 235.9 | 39.1857 | -86.7783 | M43 | Herbaceous/Grassland, Short Grass | false | NaN | 80% MUC 02 (b) [Trees, Closely Spaced, Deciduo... | NaN | https://data.globe.gov/system/photos/2026/08/3... | false | false | NaN | 30% MUC 11 (n) [Trees, Loosely Spaced, Evergre... | NaN | https://data.globe.gov/system/photos/2026/08/3... | false | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | 24905746 | NaN | 10% MUC 11 (n) [Trees, Loosely Spaced, Evergre... | NaN | https://data.globe.gov/system/photos/2026/08/3... | 17043304 | United States of America Citizen Science | land_covers | 52378 | 16SEJ191374 | POINT (-86.77885 39.18563) | 2026-08-31 | |
| 2 | USA | United States | 369.3 | GLOBE Observer App | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | true | NaN | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 94657 | true | 9 | automatic | 2026-08-31 20:29:00 | 369.8 | 41.4361 | -81.4686 | NaN | NaN | false | NaN | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | false | false | NaN | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | false | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | 148669632 | NaN | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | 17043304 | United States of America Citizen Science | land_covers | 391652 | 17TMF608872 | POINT (-81.4692 41.43541) | 2026-08-31 | |
| 3 | USA | United States | 2.3 | GLOBE Observer App | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | false | NaN | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | NaN | ((compassData.heading: 15, compassData.horizon... | https://data.globe.gov/system/photos/2026/08/3... | Delaware River | ((compassData.heading: 301, compassData.horizo... | https://data.globe.gov/system/photos/2026/08/3... | NaN | NaN | NaN | NaN | NaN | NaN | Tacony palmyra bridge nj-pa | [IEOPCNP] | 94652 | true | 187 | automatic | 2026-08-31 14:57:00 | 2.5 | 40.0088 | -75.0408 | NaN | NaN | true | NaN | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | false | false | NaN | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | false | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | 2514462 | NaN | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | 2620510 | Institute for Earth Observations School | land_covers | 42112 | 18TVK965287 | POINT (-75.04101 40.00849) | 2026-08-31 | |
| 4 | USA | United States | 78.2 | GLOBE Observer App | NaN | NaN | NaN | true | NaN | 10% MUC 12 (b) [Trees, Loosely Spaced, Deciduo... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | [Oregon State University GEOGRAPHY] | 94647 | true | NaN | manual | 2026-08-31 05:12:00 | 78.7 | 53.413 | -6.8349 | M91 | Urban, Residential Property | false | NaN | 10% MUC 12 (b) [Trees, Loosely Spaced, Deciduo... | NaN | NaN | false | false | NaN | 10% MUC 12 (b) [Trees, Loosely Spaced, Deciduo... | NaN | NaN | false | NaN | NaN | NaN | 189554409 | NaN | 10% MUC 12 (b) [Trees, Loosely Spaced, Deciduo... | NaN | NaN | 17043304 | United States of America Citizen Science | land_covers | 424872 | 29UPV439203 | POINT (-6.83495 53.41212) | 2026-08-31 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 66366 | HRV | Croatia | 10.0 | GLOBE Data Entry Site Definition | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 19188 | NaN | NaN | NaN | 2010-01-23 00:00:00 | NaN | NaN | NaN | M814 | Cultivated Land, Agriculture, Other Agriculture | NaN | NaN | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | 312299 | Srednja skola Vela Luka | land_covers | 5009 | Vela Luka | POINT (16.7186 42.9617) | 2010-01-23 |
| 66367 | HRV | Croatia | 10.0 | GLOBE Data Entry Site Definition | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NO | NaN | 19636 | NaN | NaN | NaN | 2010-01-23 00:00:00 | NaN | NaN | NaN | M814 | Cultivated Land, Agriculture, Other Agriculture | NaN | NaN | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | 312299 | Srednja skola Vela Luka | land_covers | 5009 | Vela Luka | POINT (16.7186 42.9617) | 2010-01-23 |
| 66368 | HRV | Croatia | 10.0 | GLOBE Data Entry Site Definition | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 19189 | NaN | NaN | NaN | 2010-01-23 00:00:00 | NaN | NaN | NaN | M814 | Cultivated Land, Agriculture, Other Agriculture | NaN | NaN | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | 312299 | Srednja skola Vela Luka | land_covers | 5009 | Vela Luka | POINT (16.7186 42.9617) | 2010-01-23 |
| 66369 | HRV | Croatia | 10.0 | GLOBE Data Entry Site Definition | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 19203 | NaN | NaN | NaN | 2010-01-23 00:00:00 | NaN | NaN | NaN | M814 | Cultivated Land, Agriculture, Other Agriculture | NaN | NaN | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | NaN | NaN | NaN | NaN | https://data.globe.gov/system/photos/2015/10/2... | 312299 | Srednja skola Vela Luka | land_covers | 5009 | Vela Luka | POINT (16.7186 42.9617) | 2010-01-23 |
| 66370 | USA | United States | 1612.0 | GLOBE Data Entry Site Definition | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Closed Forest, Mainly Evergreen, Tropical and ... | NaN | 19079 | NaN | NaN | NaN | 2010-01-01 00:00:00 | NaN | NaN | NaN | M0131 | Closed Forest, Mainly Evergreen, Tropical and ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 37502 | NCAR Foothills Lab | land_covers | 31964 | Dave and Mike test site:LCS-01 | POINT (-105.2441 40.0353) | 2010-01-01 |
66371 rows × 64 columns
Like the mosquito data, a copy of this dataset is on GitHub, and we will use this in future chapters. You do not need to download the dataset to your computer, as we will load the data directly using the link.
Explore the Data#
data_by_day = data[['SiteId', 'MeasuredDate']].groupby(['MeasuredDate'], as_index=False).count()
plt.figure(figsize=(8,4))
plt.plot(data_by_day['MeasuredDate'], data_by_day['SiteId'])
plt.xlabel("Date")
plt.ylabel("Daily Contribution")
plt.title("Daily Land Cover Contributions Over Time")
plt.show()
data_by_county = data[['SiteId', 'CountryName']].groupby(['CountryName'], as_index=False).count().sort_values(by='SiteId').tail(10)
plt.barh(data_by_county['CountryName'], data_by_county['SiteId'])
plt.xlabel("Total Contributions")
plt.ylabel("Country")
plt.title("Mosquito Mapper Contributions by Country")
plt.show()
A powerful feature of the Land Cover dataset is that users submit pictures of the area. Let’s view some of these images.
# Get the first observation where all photos were submitted
entry = data.dropna(subset=['DownwardPhotoUrl', 'EastPhotoUrl', 'NorthPhotoUrl', 'SouthPhotoUrl', 'WestPhotoUrl', 'UpwardPhotoUrl',
'Feature1PhotoUrl', 'Feature2PhotoUrl', 'Feature3PhotoUrl', 'Feature4PhotoUrl']).head(1)
url_list = []
col_list = []
for col in entry.columns:
if 'Url' in col:
print(f'{col}: {entry[col].values[0]}')
url_list.append(entry[col].values[0])
col_list.append(col)
display(entry)
DownwardPhotoUrl: https://data.globe.gov/system/photos/2026/08/31/5139503/original.jpg
EastPhotoUrl: https://data.globe.gov/system/photos/2026/08/31/5139499/original.jpg
Feature1PhotoUrl: https://data.globe.gov/system/photos/2026/08/31/5139504/original.jpg
Feature2PhotoUrl: https://data.globe.gov/system/photos/2026/08/31/5139505/original.jpg
Feature3PhotoUrl: https://data.globe.gov/system/photos/2026/08/31/5139506/original.jpg
Feature4PhotoUrl: https://data.globe.gov/system/photos/2026/08/31/5139507/original.jpg
NorthPhotoUrl: https://data.globe.gov/system/photos/2026/08/31/5139498/original.jpg
SouthPhotoUrl: https://data.globe.gov/system/photos/2026/08/31/5139500/original.jpg
UpwardPhotoUrl: https://data.globe.gov/system/photos/2026/08/31/5139502/original.jpg
WestPhotoUrl: https://data.globe.gov/system/photos/2026/08/31/5139501/original.jpg
| CountryCode | CountryName | Elevation | DataSource | DownwardCaption | DownwardExtraData | DownwardPhotoUrl | DryGround | EastCaption | EastClassifications | EastExtraData | EastPhotoUrl | Feature1Caption | Feature1ExtraData | Feature1PhotoUrl | Feature2Caption | Feature2ExtraData | Feature2PhotoUrl | Feature3Caption | Feature3ExtraData | Feature3PhotoUrl | Feature4Caption | Feature4ExtraData | Feature4PhotoUrl | FieldNotes | GlobeTeams | LandCoverId | LeavesOnTrees | LocationAccuracyM | LocationMethod | MeasuredAt | MeasurementElevation | MeasurementLatitude | MeasurementLongitude | MucCode | MucDescription | MucDetails | Muddy | NorthCaption | NorthClassifications | NorthExtraData | NorthPhotoUrl | RainingSnowing | SnowIce | SouthCaption | SouthClassifications | SouthExtraData | SouthPhotoUrl | StandingWater | UpwardCaption | UpwardExtraData | UpwardPhotoUrl | Userid | WestCaption | WestClassifications | WestExtraData | WestPhotoUrl | OrganizationId | OrganizationName | Protocol | SiteId | SiteName | geometry | MeasuredDate | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6 | USA | United States | 267.9 | GLOBE Observer App | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | true | NaN | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | NaN | ((compassData.heading: 260, compassData.horizo... | https://data.globe.gov/system/photos/2026/08/3... | NaN | ((compassData.heading: 336, compassData.horizo... | https://data.globe.gov/system/photos/2026/08/3... | NaN | ((compassData.heading: 279, compassData.horizo... | https://data.globe.gov/system/photos/2026/08/3... | NaN | ((compassData.heading: 289, compassData.horizo... | https://data.globe.gov/system/photos/2026/08/3... | NaN | NaN | 94654 | true | 10 | automatic | 2026-08-31 18:43:00 | 269.7 | 39.8439 | -86.3833 | NaN | NaN | false | NaN | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | false | false | NaN | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | false | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | 126092159 | NaN | NaN | NaN | https://data.globe.gov/system/photos/2026/08/3... | 17043304 | United States of America Citizen Science | land_covers | 361103 | 16SEK527106 | POINT (-86.38402 39.84378) | 2026-08-31 |
# Plot all of the images
plt.figure(figsize=(20, 6))
for i, (url, title) in enumerate(zip(url_list, col_list)):
response = requests.get(url)
img = Image.open(BytesIO(response.content))
# Create plot with 2 rows, 5 columns
plt.subplot(2, 5, i + 1)
plt.imshow(img)
plt.title(title)
plt.axis('off')
plt.tight_layout()
plt.show()
