Population density

In [1]:

# Uncomment below to run in Google Collab
# pip install ecospat

# Uncomment below to run in Google Collab # pip install ecospat

Calculating and visualizing population density change¶

In [2]:

import ecospat.ecospat as ecospat_full
from ecospat.stand_alone_functions import (
    process_species_historical_range,
    analyze_species_distribution,
    calculate_rate_of_change_first_last,
    create_interactive_map,
)

import ecospat.ecospat as ecospat_full from ecospat.stand_alone_functions import ( process_species_historical_range, analyze_species_distribution, calculate_rate_of_change_first_last, create_interactive_map, )

Classify historic and modern range edges¶

In [3]:

classified_modern, classified_historic = analyze_species_distribution(
    "Populus angustifolia", record_limit=1000, continent="north_america"
)
classified_modern.head()

classified_modern, classified_historic = analyze_species_distribution( "Populus angustifolia", record_limit=1000, continent="north_america" ) classified_modern.head()

Modern records (>= 1976): 1000
Historic records (< 1976): 254

Out[3]:

	point_geometry	year	eventDate	geometry	geometry_id	cluster	AREA	category	density
0	POINT (-111.840163 40.880712)	2025	2025-01-05	POLYGON ((-112.16175 40.79402, -112.1358 40.81...	cb869cb2640256dc655c5ffd650a009f	1	52264.281507	core	0.003597
1	POINT (-111.467794 40.775008)	2025	2025-02-07	POLYGON ((-112.16175 40.79402, -112.1358 40.81...	cb869cb2640256dc655c5ffd650a009f	1	52264.281507	core	0.003597
2	POINT (-110.869423 39.731437)	2025	2025-03-29	POLYGON ((-112.16175 40.79402, -112.1358 40.81...	cb869cb2640256dc655c5ffd650a009f	1	52264.281507	core	0.003597
3	POINT (-111.826781 40.765911)	2025	2025-04-27	POLYGON ((-112.16175 40.79402, -112.1358 40.81...	cb869cb2640256dc655c5ffd650a009f	1	52264.281507	core	0.003597
4	POINT (-111.830586 40.497927)	2025	2025-05-25	POLYGON ((-112.16175 40.79402, -112.1358 40.81...	cb869cb2640256dc655c5ffd650a009f	1	52264.281507	core	0.003597

Calculate rate of change in individuals per range edge though time.¶

In [4]:

# Remember that this is a proportional metric; a density change of -0.8 in the leading edge means that though the time period examined, the leading edge has proportionally less individuals out of the total individuals then it did in the past.
pop_change = calculate_rate_of_change_first_last(
    classified_historic, classified_modern, "Populus angustifolia", custom_end_year=2025
)
pop_change

# Remember that this is a proportional metric; a density change of -0.8 in the leading edge means that though the time period examined, the leading edge has proportionally less individuals out of the total individuals then it did in the past. pop_change = calculate_rate_of_change_first_last( classified_historic, classified_modern, "Populus angustifolia", custom_end_year=2025 ) pop_change

Out[4]:

	collapsed_category	start_time_period	end_time_period	rate_of_change_first_last
0	core	1970-1976	2024-2025	1.115629
1	leading	1970-1976	2024-2025	1.212121
2	relict	1970-1976	2024-2025	1.363636
3	trailing	1970-1976	2024-2025	1.374046

In [5]:

# Making the df more self-explanatory

pop_change = pop_change.rename(
    columns={
        "collapsed_category": "Category",
        "rate_of_change_first_last": "Rate of Change",
        "start_time_period": "Start Years",
        "end_time_period": "End Years",
    }
)


pop_change["Category"] = pop_change["Category"].str.title()
pop_change

# Making the df more self-explanatory pop_change = pop_change.rename( columns={ "collapsed_category": "Category", "rate_of_change_first_last": "Rate of Change", "start_time_period": "Start Years", "end_time_period": "End Years", } ) pop_change["Category"] = pop_change["Category"].str.title() pop_change

Out[5]:

	Category	Start Years	End Years	Rate of Change
0	Core	1970-1976	2024-2025	1.115629
1	Leading	1970-1976	2024-2025	1.212121
2	Relict	1970-1976	2024-2025	1.363636
3	Trailing	1970-1976	2024-2025	1.374046

Plot population density on a 3D map.¶

In [6]:

# Running this code will open a popup in a browser displaying the map. Set if_save = True if you want to save the map to your local device
create_interactive_map(classified_modern, if_save=False)

# Running this code will open a popup in a browser displaying the map. Set if_save = True if you want to save the map to your local device create_interactive_map(classified_modern, if_save=False)