Open in Colab

Mosquito & Land Cover Stats#

This lesson shows how to investigate the GLOBE data, calculate statistics, and create charts & maps.

import pandas as pd
pd.set_option("display.max_columns", None)
import geopandas as gpd
import matplotlib.pyplot as plt
import seaborn as sns
import folium

Mosquito#

Let’s load the data directly from the link (no need to download anything to your computer).

mosquito = gpd.read_file('https://github.com/geo-di-lab/emerge-lessons/raw/refs/heads/main/docs/data/globe_mosquito.zip')
mosquito.head()
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 MeasuredDate LarvaeCountProcessed geometry
0 BRA Brazil 6.3 NaN false NaN GLOBE Observer App LarvaeVisibleNo NaN [COLUNSLZ] NaN 0 NaN 13 automatic 2024-12-31 17:16:00 0 -2.5617 -44.2657 NaN NaN 46287 false NaN 137422629 ovitrap https://data.globe.gov/system/photos/2024/12/3... container: artificial 17459532 Brazil Citizen Science mosquito_habitat_mapper 371514 23MNT816168 2024-12-31 0.0 POINT (-44.26597 -2.56197)
1 BRA Brazil 6.3 NaN false NaN GLOBE Observer App LarvaeVisibleNo NaN [COLUNSLZ] NaN 0 NaN 13 automatic 2024-12-31 17:20:00 0 -2.5617 -44.2657 NaN NaN 46290 false NaN 137422629 ovitrap https://data.globe.gov/system/photos/2024/12/3... container: artificial 17459532 Brazil Citizen Science mosquito_habitat_mapper 371514 23MNT816168 2024-12-31 0.0 POINT (-44.26597 -2.56197)
2 BRA Brazil 7.4 NaN true NaN GLOBE Observer App LarvaeVisibleNo NaN [COLUNSLZ] NaN 0 NaN 51 automatic 2024-12-31 22:32:00 0 -2.5163 -44.3023 NaN NaN 46482 false NaN 137420190 cement, metal or plastic tank NaN container: artificial 17459532 Brazil Citizen Science mosquito_habitat_mapper 372864 23MNT775218 2024-12-31 0.0 POINT (-44.30288 -2.51676)
3 BRA Brazil 20.6 NaN true NaN GLOBE Observer App LarvaeVisibleNo NaN [COLUNSLZ] NaN 0 NaN 66 automatic 2024-12-31 00:05:00 0 -2.8639 -44.0549 NaN NaN 46203 false NaN 137419937 can or bottle NaN container: artificial 17459532 Brazil Citizen Science mosquito_habitat_mapper 373085 23MPS050834 2024-12-31 0.0 POINT (-44.05526 -2.86396)
4 BRA Brazil 20.6 NaN true NaN GLOBE Observer App LarvaeVisibleNo NaN [COLUNSLZ] NaN 0 NaN 28 automatic 2024-12-31 00:23:00 0 -2.8639 -44.0550 NaN NaN 46223 false NaN 137419937 lake NaN still: lake/pond/swamp 17459532 Brazil Citizen Science mosquito_habitat_mapper 373085 23MPS050834 2024-12-31 0.0 POINT (-44.05526 -2.86396)

See the list of columns:

mosquito.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 43012 entries, 0 to 43011
Data columns (total 36 columns):
 #   Column                    Non-Null Count  Dtype         
---  ------                    --------------  -----         
 0   CountryCode               42928 non-null  str           
 1   CountryName               42928 non-null  str           
 2   Elevation                 43012 non-null  str           
 3   AbdomenCloseupPhotoUrls   874 non-null    str           
 4   BreedingGroundEliminated  42951 non-null  str           
 5   Comments                  4002 non-null   str           
 6   DataSource                43012 non-null  str           
 7   ExtraData                 12448 non-null  str           
 8   Genus                     4364 non-null   str           
 9   GlobeTeams                16259 non-null  str           
 10  LarvaFullBodyPhotoUrls    8632 non-null   str           
 11  LarvaeCount               24691 non-null  str           
 12  LastIdentifyStage         29911 non-null  str           
 13  LocationAccuracyM         13814 non-null  str           
 14  LocationMethod            18205 non-null  str           
 15  MeasuredAt                43012 non-null  datetime64[ms]
 16  MeasurementElevation      42995 non-null  str           
 17  MeasurementLatitude       42995 non-null  float64       
 18  MeasurementLongitude      42995 non-null  float64       
 19  MosquitoAdults            16852 non-null  str           
 20  MosquitoEggs              16859 non-null  str           
 21  MosquitoHabitatMapperId   43012 non-null  str           
 22  MosquitoPupae             41210 non-null  str           
 23  Species                   1155 non-null   str           
 24  Userid                    43012 non-null  str           
 25  WaterSource               43012 non-null  str           
 26  WaterSourcePhotoUrls      34302 non-null  str           
 27  WaterSourceType           43012 non-null  str           
 28  OrganizationId            42928 non-null  str           
 29  OrganizationName          42928 non-null  str           
 30  Protocol                  43012 non-null  str           
 31  SiteId                    43012 non-null  str           
 32  SiteName                  43012 non-null  str           
 33  MeasuredDate              43012 non-null  str           
 34  LarvaeCountProcessed      24688 non-null  float64       
 35  geometry                  43012 non-null  geometry      
dtypes: datetime64[ms](1), float64(3), geometry(1), str(31)
memory usage: 25.4 MB

How many rows are in the dataset?

len(mosquito)

43012

There were 43,012 citizen science contributions from 2018 to 2024. Now, let’s see the number of countries where people submitted data.

len(mosquito['CountryCode'].unique())

95

Let’s see the types of the habitats (water sources) the citizen scientists recorded.

# Broader water source types
mosquito['WaterSourceType'].value_counts()

WaterSourceType
container: artificial                                 33167
still: lake/pond/swamp                                 6277
container: natural                                     2202
flowing: still water found next to river or stream     1366
Name: count, dtype: int64

These are the general types of water sources that citizen scientists reported to NASA. It looks like most data were collected about artificial containers. Let’s see some of the more specific types in the other column:

# More specific water source types
mosquito['WaterSource'].value_counts()

WaterSource
cement, metal or plastic tank                             7528
dish or pot                                               4102
well or cistern                                           2790
jar                                                       2399
fountain or bird bath                                     2350
ovitrap                                                   2243
adult mosquito trap                                       2073
pond                                                      2029
other                                                     1915
can or bottle                                             1888
ditch                                                     1886
tire                                                      1885
animal trough or water bowl                               1177
puddle or still water next to a creek, stream or river     993
flower or plant pot/tray                                   932
trash container                                            882
plant clumps (bamboo etc)                                  768
puddle, vehhicle or animal tracks                          644
tree holes                                                 571
public works - culvert, bridge, road                       570
discarded: other                                           511
puddle, vehicle or animal tracks                           475
swamp or wetland                                           467
plant husk (areca, coconut etc)                            453
lake                                                       415
rain gutter or other architectural feature                 277
estuary                                                    148
pool                                                       128
old car or boat                                            123
reservoir                                                  112
grill or outdoor appliance                                 108
refrigerator drainage                                       69
animal shell (tortoise, mollusk etc)                        65
bay or ocean                                                36
Name: count, dtype: int64

Let’s make a pie chart using the broader column, WaterSourceType

# Here are some options for color palettes
display(sns.color_palette(palette='Set2'))
display(sns.color_palette(palette='twilight_shifted'))
display(sns.color_palette(palette='Paired'))

# Pie chart of water types
types = (mosquito[['SiteId', 'WaterSourceType']]
         .groupby('WaterSourceType', as_index=False).count()
         .sort_values(by='WaterSourceType', ascending=False))

plt.figure(figsize=(5, 5))
patches, texts = plt.pie(x = types['SiteId'],
                         colors = sns.color_palette('Paired'))
plt.title("GLOBE Mosquito Sightings: Water Source Types (General)")
plt.legend(patches, types['WaterSourceType'],
           loc = 'center left', bbox_to_anchor=(1, 0.5), frameon=False)
plt.show()
../../_images/fb641925391569d3f716ae1596b222180f8243a015a43f03ed5809db890cbd9d.png

What is the average larvae count by country?

mosquito_avg = mosquito.groupby('CountryCode')['LarvaeCountProcessed'].mean()
mosquito_avg

CountryCode
ARE      5.000000
ARG    116.108108
AUS      2.500000
BEL           NaN
BEN     38.598198
          ...    
UKR           NaN
URY      0.043478
USA    667.935961
VNM     22.686567
ZAF     17.800000
Name: LarvaeCountProcessed, Length: 94, dtype: float64

Let’s make a map showing the larvae count by country. The country boundaries (generalized) are from Esri, Garmin, and U.S. Central Intelligence Agency (The World Factbook). The boundaries are generalized to allow data processing and visualizations to load faster. The ISO alpha-3 codes come from the World Countries layer from Esri, Garmin, U.S. Central Intelligence Agency (The World Factbook), and International Organization for Standardization (ISO).

countries = gpd.read_file('https://github.com/geo-di-lab/emerge-lessons/raw/refs/heads/main/docs/data/world_countries_general.geojson').to_crs(epsg=4326)
mosquito_avg = countries.merge(mosquito_avg, left_on='iso3', right_on='CountryCode', how='left')

fig, ax = plt.subplots(figsize = (10, 4))

mosquito_avg.plot(column = 'LarvaeCountProcessed', cmap = 'viridis',
                     legend = True, vmin = 0, vmax = 50, ax = ax,
                     missing_kwds = {'color': 'lightgrey'})
plt.title('GLOBE Mosquito Sightings: Average Larvae Count')
ax.axis('off')
plt.show()
../../_images/ba4b21968492ec9adb01930007b5750120472302742b5a44e5363ea1c13cf08f.png

Now, we’ll make an interactive map showing total GLOBE observations by country.

mosquito_obs = mosquito.groupby('CountryCode').size() \
                       .reset_index(name='GLOBE_Observations')
mosquito_obs = countries.merge(mosquito_obs, left_on='iso3', right_on='CountryCode', how='left')

We will build both interactive maps in this lesson the same way, so we’ll write the map code once as a reusable function. The function colors each country by its number of observations, adds a legend that lists the range for every color, and shows a pop-up when you hover over a country.

# The color scheme (yellow-green-blue) and the cutoffs between colors
BINS = [1, 50, 100, 500, 1000, 5000, 10000, 20000, 35000]
COLORS = ['#ffffd9', '#edf8b1', '#c7e9b4', '#7fcdbb', '#41b6c4', '#1d91c0', '#225ea8', '#0c2c84']
NO_DATA = '#e8e8e8'

def observation_color(value):
    """Pick the color for a country based on its number of observations."""
    if pd.isna(value):
        return NO_DATA
    for color, upper in zip(COLORS, BINS[1:]):
        if value < upper:
            return color
    return COLORS[-1]

def make_legend(title):
    """Create legend for the map."""
    rows = ""
    for i, color in enumerate(COLORS):
        low, high = BINS[i], BINS[i + 1]
        label = f"{low:,} &ndash; {high - 1:,}" if i < len(COLORS) - 1 else f"{low:,}+"
        rows += (f"<div style='margin:2px 0'><span style='display:inline-block;width:20px;height:13px;"
                 f"background:{color};border:1px solid #999;vertical-align:-2px'></span> {label}</div>")
    rows += (f"<div style='margin:2px 0'><span style='display:inline-block;width:20px;height:13px;"
             f"background:{NO_DATA};border:1px solid #999;vertical-align:-2px'></span> No data</div>")
    return folium.Element(f"""
    <div style="position:fixed; bottom:25px; left:15px; z-index:9999; background:white;
                padding:10px 14px; border:1px solid #bbb; border-radius:6px;
                box-shadow:0 1px 5px rgba(0,0,0,.3); font-family:Helvetica,Arial,sans-serif;
                font-size:13px; color:#222; line-height:1.4;">
      <div style="font-weight:bold; margin-bottom:6px;">{title}</div>
      {rows}
    </div>""")

def observation_map(data, tooltip_fields, tooltip_aliases, legend_title):
    """Make an interactive map of GLOBE observations by country."""
    data = data.copy()

    # Format the counts and say "No data" instead of showing an empty value
    data['Observations'] = data['GLOBE_Observations'].apply(
        lambda value: f"{value:,.0f}" if pd.notna(value) else "No data")
    for field in tooltip_fields:
        data[field] = data[field].fillna("No data")

    map = folium.Map(location=[20, 0], zoom_start=2, min_zoom=2, tiles=None, max_bounds=True)

    # Add Esri's light gray basemap
    folium.TileLayer(
        tiles="https://server.arcgisonline.com/ArcGIS/rest/services/Canvas/World_Light_Gray_Base/MapServer/tile/{z}/{y}/{x}",
        attr="Tiles &copy; Esri &mdash; Esri, DeLorme, NAVTEQ",
        name="Light Gray Canvas",
        no_wrap=True,
        bounds=[[-85, -180], [85, 180]],
    ).add_to(map)

    # Add GLOBE data with observation counts
    folium.GeoJson(
        data,
        style_function=lambda feature: {
            'fillColor': observation_color(feature['properties']['GLOBE_Observations']),
            'color': 'white',
            'weight': 0.7,
            'fillOpacity': 0.85,
        },
        highlight_function=lambda feature: {'weight': 2.5, 'color': '#333', 'fillOpacity': 0.95},
        tooltip=folium.GeoJsonTooltip(
            fields=tooltip_fields,
            aliases=tooltip_aliases,
            sticky=True,
            style=("background-color:white; border:1px solid #999; border-radius:4px; "
                   "box-shadow:0 1px 4px rgba(0,0,0,.3); padding:6px 8px; font-size:13px;"),
        ),
    ).add_to(map)

    map.get_root().html.add_child(
        folium.Element("<style>.leaflet-container{background:#f4f6f8;}</style>"))
    map.get_root().html.add_child(make_legend(legend_title))
    return map

map = observation_map(
    mosquito_obs,
    tooltip_fields=['name', 'Observations'],
    tooltip_aliases=['Country:', 'Observations:'],
    legend_title="GLOBE Mosquito Observations",
)

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

Land Cover#

Load the land cover data directly from the link.

land_cover = gpd.read_file('https://github.com/geo-di-lab/emerge-lessons/raw/refs/heads/main/docs/data/globe_land_cover.zip')
land_cover.head()
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 MeasuredDate geometry
0 ITA Italy 489.2 GLOBE Observer App NaN NaN https://data.globe.gov/system/photos/2024/12/3... true NaN NaN NaN https://data.globe.gov/system/photos/2024/12/3... Snag C. sativa, 40 cm, cl 2, #01 #04 #12 ((compassData.heading: 182, compassData.horizo... https://data.globe.gov/system/photos/2024/12/3... Log 70 cm, C. sativa, cl 2, #04 #01 #12 ((compassData.heading: 182, compassData.horizo... https://data.globe.gov/system/photos/2024/12/3... Stump of C. sativa 230 cm ((compassData.heading: null, compassData.horiz... https://data.globe.gov/system/photos/2024/12/3... NaN NaN NaN Old Coppice of Castanea sativa [Conservazione Natura Universita Tuscia] 78608 false 8 automatic 2024-12-31 15:07:00.000 492.4 42.1818 12.1825 NaN NaN false NaN NaN NaN https://data.globe.gov/system/photos/2024/12/3... false false NaN NaN NaN https://data.globe.gov/system/photos/2024/12/3... false NaN NaN https://data.globe.gov/system/photos/2024/12/3... 128342138 NaN NaN NaN https://data.globe.gov/system/photos/2024/12/3... 17453129 Italy Citizen Science land_covers 376869 33TTG673738 2024-12-31 POINT (12.18229 42.18175)
1 MDG Madagascar 1350.1 GLOBE Observer App NaN NaN https://data.globe.gov/system/photos/2024/12/3... true NaN 90% MUC 01 (n) [Trees, Closely Spaced, Evergre... NaN https://data.globe.gov/system/photos/2024/12/3... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Arbres plantés par l'équipe GLOBE avec la comm... [Africa 2024 Regional Meeting, Coordinating Of... 77695 true 10 automatic 2024-12-31 11:29:00.000 1340.6 -18.7576 47.5615 M01 Trees, Closely Spaced, Evergreen - Needle Leaved n false NaN 90% MUC 01 (n) [Trees, Closely Spaced, Evergre... NaN https://data.globe.gov/system/photos/2024/12/3... false false NaN 90% MUC 01 (n) [Trees, Closely Spaced, Evergre... NaN https://data.globe.gov/system/photos/2024/12/3... false NaN NaN https://data.globe.gov/system/photos/2024/12/3... 2538037 NaN 90% MUC 01 (n) [Trees, Closely Spaced, Evergre... NaN https://data.globe.gov/system/photos/2024/12/3... 6508873 Madagascar GLOBE v-School land_covers 373647 38KQE700240 2024-12-31 POINT (47.56096 -18.75807)
2 MDG Madagascar 1324.7 GLOBE Observer App NaN NaN https://data.globe.gov/system/photos/2024/12/3... true NaN 60% MUC 93 [Urban, Roads and Parking] NaN https://data.globe.gov/system/photos/2024/12/3... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN [Africa 2024 Regional Meeting, Coordinating Of... 77691 true 10 automatic 2024-12-31 12:07:00.000 1324.8 -18.7944 47.5799 M93 Urban, Roads and Parking false NaN 60% MUC 93 [Urban, Roads and Parking] NaN https://data.globe.gov/system/photos/2024/12/3... false false NaN 60% MUC 93 [Urban, Roads and Parking] NaN https://data.globe.gov/system/photos/2024/12/3... false NaN NaN https://data.globe.gov/system/photos/2024/12/3... 2538037 NaN 60% MUC 93 [Urban, Roads and Parking] NaN https://data.globe.gov/system/photos/2024/12/3... 6508873 Madagascar GLOBE v-School land_covers 373642 38KQE719199 2024-12-31 POINT (47.57953 -18.79484)
3 USA United States 182.2 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 NaN NaN 77689 NaN NaN NaN 2024-12-31 16:12:03.111 NaN NaN NaN M4 Herbaceous Vegetation NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 52107 Crestwood High School land_covers 373628 Hillcrest Elementary Trail 2024-12-31 POINT (-83.27705 42.3465)
4 GRC Greece 3.0 GLOBE Observer App NaN NaN https://data.globe.gov/system/photos/2024/12/3... true NaN 90% MUC 91 [Urban, Residential Property]; 10% ... NaN https://data.globe.gov/system/photos/2024/12/3... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN [tinycore lab, tinycorelab] 77683 false NaN manual 2024-12-31 11:45:00.000 3.2 37.939 23.697 M91 Urban, Residential Property false NaN 90% MUC 91 [Urban, Residential Property]; 10% ... NaN https://data.globe.gov/system/photos/2024/12/3... false false NaN 20% MUC 91 [Urban, Residential Property]; 80% ... NaN https://data.globe.gov/system/photos/2024/12/3... false NaN NaN https://data.globe.gov/system/photos/2024/12/3... 65209921 NaN 10% MUC 91 [Urban, Residential Property]; 90% ... NaN https://data.globe.gov/system/photos/2024/12/3... 6508393 Greece GLOBE v-School land_covers 373613 34SGH370024 2024-12-31 POINT (23.6969 37.9383) 
land_cover.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 50783 entries, 0 to 50782
Data columns (total 64 columns):
 #   Column                Non-Null Count  Dtype         
---  ------                --------------  -----         
 0   CountryCode           48376 non-null  str           
 1   CountryName           48376 non-null  str           
 2   Elevation             50783 non-null  str           
 3   DataSource            50783 non-null  str           
 4   DownwardCaption       22409 non-null  str           
 5   DownwardExtraData     22405 non-null  str           
 6   DownwardPhotoUrl      39585 non-null  str           
 7   DryGround             43453 non-null  str           
 8   EastCaption           23641 non-null  str           
 9   EastClassifications   14314 non-null  str           
 10  EastExtraData         23636 non-null  str           
 11  EastPhotoUrl          42406 non-null  str           
 12  Feature1Caption       337 non-null    str           
 13  Feature1ExtraData     1359 non-null   str           
 14  Feature1PhotoUrl      1360 non-null   str           
 15  Feature2Caption       156 non-null    str           
 16  Feature2ExtraData     676 non-null    str           
 17  Feature2PhotoUrl      676 non-null    str           
 18  Feature3Caption       96 non-null     str           
 19  Feature3ExtraData     448 non-null    str           
 20  Feature3PhotoUrl      448 non-null    str           
 21  Feature4Caption       77 non-null     str           
 22  Feature4ExtraData     337 non-null    str           
 23  Feature4PhotoUrl      337 non-null    str           
 24  FieldNotes            21043 non-null  str           
 25  GlobeTeams            23723 non-null  str           
 26  LandCoverId           50783 non-null  str           
 27  LeavesOnTrees         43453 non-null  str           
 28  LocationAccuracyM     36355 non-null  str           
 29  LocationMethod        43453 non-null  str           
 30  MeasuredAt            50783 non-null  datetime64[ms]
 31  MeasurementElevation  43400 non-null  str           
 32  MeasurementLatitude   43400 non-null  str           
 33  MeasurementLongitude  43400 non-null  str           
 34  MucCode               21835 non-null  str           
 35  MucDescription        21832 non-null  str           
 36  MucDetails            43453 non-null  str           
 37  Muddy                 43453 non-null  str           
 38  NorthCaption          23929 non-null  str           
 39  NorthClassifications  14345 non-null  str           
 40  NorthExtraData        23929 non-null  str           
 41  NorthPhotoUrl         42773 non-null  str           
 42  RainingSnowing        43453 non-null  str           
 43  SnowIce               43453 non-null  str           
 44  SouthCaption          23509 non-null  str           
 45  SouthClassifications  14311 non-null  str           
 46  SouthExtraData        23507 non-null  str           
 47  SouthPhotoUrl         42292 non-null  str           
 48  StandingWater         43453 non-null  str           
 49  UpwardCaption         22905 non-null  str           
 50  UpwardExtraData       22898 non-null  str           
 51  UpwardPhotoUrl        40348 non-null  str           
 52  Userid                43453 non-null  str           
 53  WestCaption           23472 non-null  str           
 54  WestClassifications   14284 non-null  str           
 55  WestExtraData         23482 non-null  str           
 56  WestPhotoUrl          42197 non-null  str           
 57  OrganizationId        48377 non-null  str           
 58  OrganizationName      48377 non-null  str           
 59  Protocol              50783 non-null  str           
 60  SiteId                50783 non-null  str           
 61  SiteName              50783 non-null  str           
 62  MeasuredDate          50783 non-null  str           
 63  geometry              50783 non-null  geometry      
dtypes: datetime64[ms](1), geometry(1), str(62)
memory usage: 69.0 MB

len(land_cover)

50783

There are slightly more observations submitted for GLOBE Land Cover (50,766 observations) than Mosquito Habitat Mapper.

len(land_cover['CountryCode'].unique())

98

A helpful part of the land cover dataset is the MUC classifications. MUC, Modified UNESCO Classification, is a classification system with different land use types.

# Let's see the different MUC codes
len(land_cover['MucCode'].unique())

154

# What are the most common MUC codes by country?

muc = land_cover.groupby('CountryCode')['MucDescription'] \
    .apply(lambda x: x.value_counts().idxmax() if not x.value_counts().empty else None).reset_index(name='MucDescription')

# Add a column for the number of the MUC code
muc['Count'] = land_cover.groupby('CountryCode')['MucDescription'] \
    .apply(lambda x: x.value_counts().max()).values

# Add a column for the total number of GLOBE observations
muc['GLOBE_Observations'] = land_cover.groupby('CountryCode').size().values

muc
CountryCode MucDescription Count GLOBE_Observations
0 ARE Barren, Bare Rock 1.0 25
1 ARG Cultivated Land, Non-Agriculture, Parks and At... 135.0 1109
2 ARM NaN NaN 1
3 AUS Herbaceous/Grassland, Short Grass 8.0 346
4 AUT Barren, Dirt/Other 1.0 10
... ... ... ... ...
92 UKR Urban, Other 8.0 224
93 URY Herbaceous Vegetation 8.0 35
94 USA Herbaceous/Grassland, Short Grass 2788.0 30122
95 VNM Urban, Commercial Property 6.0 56
96 ZAF Herbaceous/Grassland, Short Grass 4.0 107

97 rows × 4 columns

# We can check that this is correct by checking one of the countries
print('Count of most common MUC:', len(land_cover[(land_cover['CountryCode'] == 'USA') &
 (land_cover['MucDescription'] == 'Herbaceous/Grassland, Short Grass')]))
print('Total GLOBE Observations:', len(land_cover[(land_cover['CountryCode'] == 'USA')]))

Count of most common MUC: 2788
Total GLOBE Observations: 30122

We can see that the number of rows matches the number in the dataset above, so we know this was calculated correctly.

muc = countries.merge(muc, left_on='iso3', right_on='CountryCode', how='left')

Some of the MUC descriptions are similar, so we’ll group by the general categories of Barren, Closed Forest, Cultivated, and others below.

muc_list = ['Barren', 'Closed Forest', 'Cultivated', 'Herbaceous', 'Open Water', 'Trees', 'Urban', 'Wetlands', 'Woodland']

for muc_code in muc_list:
    muc.loc[muc['MucDescription'].str.contains(muc_code, na=False), 'MucDescriptionShort'] = muc_code

fig, ax = plt.subplots(figsize = (11, 5))

muc.plot(column = 'MucDescriptionShort', cmap = 'viridis',
                     legend = True, ax = ax,
                     missing_kwds = {'color': 'lightgrey', 'label': 'No Data'},
                     legend_kwds={'loc': 'lower left', 'frameon': False})
plt.title('GLOBE Land Cover: Most Common MUC Codes')
plt.show()
../../_images/fbe6218e51d522bb2effd0f1dc9ba3f6afbc1ab118e073be32decf7ea188961e.png

We’ll make an interactive map of the number of land cover observations and these MUC codes.

map = observation_map(
    muc,
    tooltip_fields=['name', 'MucDescriptionShort', 'Observations'],
    tooltip_aliases=['Country:', 'Most common MUC:', 'Observations:'],
    legend_title="GLOBE Land Cover Observations",
)

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