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	None	false	None	GLOBE Observer App	LarvaeVisibleNo	None	[COLUNSLZ]	None	0	None	13	automatic	2024-12-31 17:16:00	0	-2.5617	-44.2657	None	None	46287	false	None	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	None	false	None	GLOBE Observer App	LarvaeVisibleNo	None	[COLUNSLZ]	None	0	None	13	automatic	2024-12-31 17:20:00	0	-2.5617	-44.2657	None	None	46290	false	None	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	None	true	None	GLOBE Observer App	LarvaeVisibleNo	None	[COLUNSLZ]	None	0	None	51	automatic	2024-12-31 22:32:00	0	-2.5163	-44.3023	None	None	46482	false	None	137420190	cement, metal or plastic tank	None	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	None	true	None	GLOBE Observer App	LarvaeVisibleNo	None	[COLUNSLZ]	None	0	None	66	automatic	2024-12-31 00:05:00	0	-2.8639	-44.0549	None	None	46203	false	None	137419937	can or bottle	None	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	None	true	None	GLOBE Observer App	LarvaeVisibleNo	None	[COLUNSLZ]	None	0	None	28	automatic	2024-12-31 00:23:00	0	-2.8639	-44.0550	None	None	46223	false	None	137419937	lake	None	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  object        
 1   CountryName               42928 non-null  object        
 2   Elevation                 43012 non-null  object        
 3   AbdomenCloseupPhotoUrls   874 non-null    object        
 4   BreedingGroundEliminated  42951 non-null  object        
 5   Comments                  4002 non-null   object        
 6   DataSource                43012 non-null  object        
 7   ExtraData                 12448 non-null  object        
 8   Genus                     4364 non-null   object        
 9   GlobeTeams                16259 non-null  object        
 10  LarvaFullBodyPhotoUrls    8632 non-null   object        
 11  LarvaeCount               24691 non-null  object        
 12  LastIdentifyStage         29911 non-null  object        
 13  LocationAccuracyM         13814 non-null  object        
 14  LocationMethod            18205 non-null  object        
 15  MeasuredAt                43012 non-null  datetime64[ms]
 16  MeasurementElevation      42995 non-null  object        
 17  MeasurementLatitude       42995 non-null  float64       
 18  MeasurementLongitude      42995 non-null  float64       
 19  MosquitoAdults            16852 non-null  object        
 20  MosquitoEggs              16859 non-null  object        
 21  MosquitoHabitatMapperId   43012 non-null  object        
 22  MosquitoPupae             41210 non-null  object        
 23  Species                   1155 non-null   object        
 24  Userid                    43012 non-null  object        
 25  WaterSource               43012 non-null  object        
 26  WaterSourcePhotoUrls      34302 non-null  object        
 27  WaterSourceType           43012 non-null  object        
 28  OrganizationId            42928 non-null  object        
 29  OrganizationName          42928 non-null  object        
 30  Protocol                  43012 non-null  object        
 31  SiteId                    43012 non-null  object        
 32  SiteName                  43012 non-null  object        
 33  MeasuredDate              43012 non-null  object        
 34  LarvaeCountProcessed      24688 non-null  float64       
 35  geometry                  43012 non-null  geometry      
dtypes: datetime64[ms](1), float64(3), geometry(1), object(31)
memory usage: 11.8+ 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='tab20'))

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

plt.figure(figsize=(5, 5))
patches, texts = plt.pie(x = types['SiteId'],
                         colors = sns.color_palette('Set2'))
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()

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()

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')

map = folium.Map(location=[0, 0], zoom_start=3, tiles="CartoDB positron")

# Create the map with a color scale for the number of observations submitted to GLOBE
folium.Choropleth(
    geo_data=mosquito_obs.to_json(),
    name="Choropleth",
    data=mosquito_obs,
    columns=['name', 'GLOBE_Observations'],
    key_on="feature.properties.name",
    fill_color="YlGnBu",
    fill_opacity=0.7,
    bins=[1, 50, 100, 500, 1000, 5000, 10000, 20000],
    legend_name="Number of GLOBE Observations (2018-2024)",
).add_to(map)

# Add pop-up when you hover over the area
folium.GeoJson(
    geo_data=mosquito_obs.to_json(),
    data=mosquito_obs,
    key_on="feature.properties.name",
    tooltip=folium.features.GeoJsonTooltip(fields=['name', 'GLOBE_Observations'], aliases=['Country:', 'Observations:']),
    style_function=lambda feature: {'color': 'white', 'weight': 1}
).add_to(map)

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	None	None	https://data.globe.gov/system/photos/2024/12/3...	true	None	None	None	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...	None	None	None	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	None	None		false	None	None	None	https://data.globe.gov/system/photos/2024/12/3...	false	false	None	None	None	https://data.globe.gov/system/photos/2024/12/3...	false	None	None	https://data.globe.gov/system/photos/2024/12/3...	128342138	None	None	None	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	None	None	https://data.globe.gov/system/photos/2024/12/3...	true	None	90% MUC 01 (n) [Trees, Closely Spaced, Evergre...	None	https://data.globe.gov/system/photos/2024/12/3...	None	None	None	None	None	None	None	None	None	None	None	None	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	None	90% MUC 01 (n) [Trees, Closely Spaced, Evergre...	None	https://data.globe.gov/system/photos/2024/12/3...	false	false	None	90% MUC 01 (n) [Trees, Closely Spaced, Evergre...	None	https://data.globe.gov/system/photos/2024/12/3...	false	None	None	https://data.globe.gov/system/photos/2024/12/3...	2538037	None	90% MUC 01 (n) [Trees, Closely Spaced, Evergre...	None	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	None	None	https://data.globe.gov/system/photos/2024/12/3...	true	None	60% MUC 93 [Urban, Roads and Parking]	None	https://data.globe.gov/system/photos/2024/12/3...	None	None	None	None	None	None	None	None	None	None	None	None	None	[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	None	60% MUC 93 [Urban, Roads and Parking]	None	https://data.globe.gov/system/photos/2024/12/3...	false	false	None	60% MUC 93 [Urban, Roads and Parking]	None	https://data.globe.gov/system/photos/2024/12/3...	false	None	None	https://data.globe.gov/system/photos/2024/12/3...	2538037	None	60% MUC 93 [Urban, Roads and Parking]	None	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	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	77689	None	None	None	2024-12-31 16:12:03.111	None	None	None	M4	Herbaceous Vegetation	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	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	None	None	https://data.globe.gov/system/photos/2024/12/3...	true	None	90% MUC 91 [Urban, Residential Property]; 10% ...	None	https://data.globe.gov/system/photos/2024/12/3...	None	None	None	None	None	None	None	None	None	None	None	None	None	[tinycore lab, tinycorelab]	77683	false	None	manual	2024-12-31 11:45:00.000	3.2	37.939	23.697	M91	Urban, Residential Property		false	None	90% MUC 91 [Urban, Residential Property]; 10% ...	None	https://data.globe.gov/system/photos/2024/12/3...	false	false	None	20% MUC 91 [Urban, Residential Property]; 80% ...	None	https://data.globe.gov/system/photos/2024/12/3...	false	None	None	https://data.globe.gov/system/photos/2024/12/3...	65209921	None	10% MUC 91 [Urban, Residential Property]; 90% ...	None	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  object        
 1   CountryName           48376 non-null  object        
 2   Elevation             50783 non-null  object        
 3   DataSource            50783 non-null  object        
 4   DownwardCaption       22409 non-null  object        
 5   DownwardExtraData     22405 non-null  object        
 6   DownwardPhotoUrl      39585 non-null  object        
 7   DryGround             43453 non-null  object        
 8   EastCaption           23641 non-null  object        
 9   EastClassifications   14314 non-null  object        
 10  EastExtraData         23636 non-null  object        
 11  EastPhotoUrl          42406 non-null  object        
 12  Feature1Caption       337 non-null    object        
 13  Feature1ExtraData     1359 non-null   object        
 14  Feature1PhotoUrl      1360 non-null   object        
 15  Feature2Caption       156 non-null    object        
 16  Feature2ExtraData     676 non-null    object        
 17  Feature2PhotoUrl      676 non-null    object        
 18  Feature3Caption       96 non-null     object        
 19  Feature3ExtraData     448 non-null    object        
 20  Feature3PhotoUrl      448 non-null    object        
 21  Feature4Caption       77 non-null     object        
 22  Feature4ExtraData     337 non-null    object        
 23  Feature4PhotoUrl      337 non-null    object        
 24  FieldNotes            21043 non-null  object        
 25  GlobeTeams            23723 non-null  object        
 26  LandCoverId           50783 non-null  object        
 27  LeavesOnTrees         43453 non-null  object        
 28  LocationAccuracyM     36355 non-null  object        
 29  LocationMethod        43453 non-null  object        
 30  MeasuredAt            50783 non-null  datetime64[ms]
 31  MeasurementElevation  43400 non-null  object        
 32  MeasurementLatitude   43400 non-null  object        
 33  MeasurementLongitude  43400 non-null  object        
 34  MucCode               21835 non-null  object        
 35  MucDescription        21832 non-null  object        
 36  MucDetails            43453 non-null  object        
 37  Muddy                 43453 non-null  object        
 38  NorthCaption          23929 non-null  object        
 39  NorthClassifications  14345 non-null  object        
 40  NorthExtraData        23929 non-null  object        
 41  NorthPhotoUrl         42773 non-null  object        
 42  RainingSnowing        43453 non-null  object        
 43  SnowIce               43453 non-null  object        
 44  SouthCaption          23509 non-null  object        
 45  SouthClassifications  14311 non-null  object        
 46  SouthExtraData        23507 non-null  object        
 47  SouthPhotoUrl         42292 non-null  object        
 48  StandingWater         43453 non-null  object        
 49  UpwardCaption         22905 non-null  object        
 50  UpwardExtraData       22898 non-null  object        
 51  UpwardPhotoUrl        40348 non-null  object        
 52  Userid                43453 non-null  object        
 53  WestCaption           23472 non-null  object        
 54  WestClassifications   14284 non-null  object        
 55  WestExtraData         23482 non-null  object        
 56  WestPhotoUrl          42197 non-null  object        
 57  OrganizationId        48377 non-null  object        
 58  OrganizationName      48377 non-null  object        
 59  Protocol              50783 non-null  object        
 60  SiteId                50783 non-null  object        
 61  SiteName              50783 non-null  object        
 62  MeasuredDate          50783 non-null  object        
 63  geometry              50783 non-null  geometry      
dtypes: datetime64[ms](1), geometry(1), object(62)
memory usage: 24.8+ 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	None	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()

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

map = folium.Map(location=[0, 0], zoom_start=3, tiles="CartoDB positron")

# Create the map with a color scale for the number of observations submitted to GLOBE
folium.Choropleth(
    geo_data=muc.to_json(),
    name="Choropleth",
    data=muc,
    columns=['name', 'GLOBE_Observations'],
    key_on="feature.properties.name",
    fill_color="YlGnBu",
    fill_opacity=0.7,
    bins=[1, 50, 100, 500, 1000, 5000, 10000, 20000, 30200],
    legend_name="Number of GLOBE Observations (2018-2024)",
).add_to(map)

# Add pop-up when you hover over the area
folium.GeoJson(
    geo_data=muc.to_json(),
    data=muc,
    key_on="feature.properties.name",
    tooltip=folium.features.GeoJsonTooltip(fields=['name', 'MucDescriptionShort', 'GLOBE_Observations'], aliases=['Country:', 'Most common MUC:', 'Observations:']),
    style_function=lambda feature: {'color': 'white', 'weight': 1}
).add_to(map)

display(map)