Visiting National Parks in New South Wales

Python

Geopandas

NSW

National Parks

Author

José R. Ferrer-Paris

Published

January 25, 2026

Modified

January 29, 2026

My iNaturalist Observations in National Parks and reserves of New South Wales

A large part of my iNat activity has been accumulating in the past years, since I moved to Australia in 2019.

See my previous posts regarding my timeline of observations or the summary of observations around the world

Here I want to zoom in into these observations in New South Wales to figure out how many species I have recorded in the terrestrial National Parks and reserves of this wonderful state.

Overview

I divided this post in four steps:

Download iNaturalist observations from around the world.
Download spatial data representing Australia and its subdivisions.
Intersect observations with state and regional boundaries.
Aggregate observations to explore number of observations at different levels.

Tools and Libraries

I will be using a Python environment with a selection of my favourite libraries, as explained here.

We will be using the following libraries in this blog post:

import geopandas as gpd
import zipfile, requests, io
import pandas as pd
from pyinaturalist import get_observations, get_places_by_id
import matplotlib.pyplot as plt
import numpy as np
import pyprojroot
from IPython.display import display, HTML, Markdown

Declare the root folder for the repository:

repodir = pyprojroot.find_root(pyprojroot.has_dir(".git"))

Step-by-Step Guide

Step 1: Downloading iNaturalist Observations

Let’s begin by fetching iNaturalist observations with pyinaturalist. We will need a selection of global observations so we select the user neomapas¹, and see where in the world they have been.

username = 'neomapas'
place = 6825 # New South Wales AU
observations = get_observations(user_id=username, place_id=place, per_page=0)
n_obs = observations['total_results']

Let’s print an overview of these total results:

print("user {} has {} observations in the target region (place_id: {}) in iNaturalist".format(username,n_obs,place))

user neomapas has 777 observations in the target region (place_id: 6825) in iNaturalist

The maximum number of observations we can download in each query 200, so we need to use pagination to get all results. For each query we will extract a minimum selection of fields that we will use for summarising the data (coordinates, place and species guess), but there are many other fields that could be important to include for more in depth explorations.

records=list()
j=1
while len(records) < n_obs:
    print("Requesting observations from user _{}_: page {}, total of {} observations downloaded".format(username,j,min(j*200,n_obs)))
    observations = get_observations(user_id='neomapas',place_id=place,per_page=1000,page=j)
    for obs in observations['results']:
        record = {
            'uuid': obs['uuid'],
            'quality': obs['quality_grade'],
            'description': obs['description'],
            'location': obs['place_guess'],
            'longitude': obs['location'][1],
            'latitude': obs['location'][0],
            'iconic taxon': obs['taxon']['iconic_taxon_name'],
            'species guess': obs['species_guess'],
            'observed on': obs['observed_on'],
            'points': obs['faves_count'] * 10 + obs['comments_count'] + obs['identifications_count'] * 3,
        }
        if len(obs['observation_photos'])>0:
            record['url'] = obs['observation_photos'][0]['photo']['url']
            record['attribution'] = obs['observation_photos'][0]['photo']['attribution']
        records.append(record)
    j=j+1

Requesting observations from user _neomapas_: page 1, total of 200 observations downloaded
Requesting observations from user _neomapas_: page 2, total of 400 observations downloaded
Requesting observations from user _neomapas_: page 3, total of 600 observations downloaded
Requesting observations from user _neomapas_: page 4, total of 777 observations downloaded

Later in the code, I will need to format a html string to define figures with captions, let’s do this now for each record in this list of records:

for record in records:
    record['figure']="<figure class='mini'><a href='https://www.inaturalist.org/observations/%s' target=_blank><img src='%s' height=50><figcaption class='mini'>%s: %s</figcaption></a></figure>" % (
        record['uuid'],
        record['url'],
        record['iconic taxon'],
        record['species guess'])

Now we need to bundle all these records into a data frame with geospatial information using geopandas. We define a data frame with pandas:

inat_obs=pd.DataFrame(records)

And transform the numeric variables latitude and longitude into a geometry with a explicit Coordinate Reference System (CRS):

gs = gpd.points_from_xy(inat_obs.longitude, inat_obs.latitude, crs="EPSG:4326")
inat_obs_xy=gpd.GeoDataFrame(inat_obs, geometry=gs).to_crs(3112)

Step 2: Downloading spatial data for Australia

This can very easy to do thanks to the great features of geopandas.read_file function and the excellent data service of the Australian Bureau of Statistics.

I am using the Collaborative Australian Protected Areas Database (CAPAD) 2024 - Terrestrial data updated in November 2025. The data is available in different formats. I downloaded a GeoJSON version and keep it in my local folder.

I read the vector file from the data folder:

CAPAD24 = gpd.read_file(repodir / "data" / "Collaborative_Australian_Protected_Areas_Database_(CAPAD)_%E2%80%93_Terrestrial.geojson")

And

NSW_parks = CAPAD24.loc[CAPAD24.STATE == 'NSW']

How many parks and reserves are there in New South Wales? A lot…

NSW_parks.groupby('TYPE').agg({'GAZ_AREA':['count','sum']})

	GAZ_AREA
	count	sum
TYPE
Aboriginal Area	16	1.567268e+04
CCA Zone 1 National Park	34	1.328628e+05
CCA Zone 2 Aboriginal Area	5	2.166141e+04
CCA Zone 3 State Conservation Area	23	1.965327e+05
Conservation Reserve	10	3.048435e+04
Flora Reserve	106	6.994412e+04
Historic Site	8	2.805091e+03
Indigenous Protected Area	14	1.939915e+04
Karst Conservation Reserve	4	5.228100e+03
NRS Addition - Gazettal in Progress	1	0.000000e+00
National Park	212	5.571571e+06
Nature Reserve	430	9.672521e+05
Permanent Park Preserve	1	1.340810e+03
Private Nature Reserve	2	0.000000e+00
Regional Park	23	2.166177e+04
State Conservation Area	129	7.401583e+05

It will take some time to visit them all.

Step 3: Intersect observations

We’ll overlay these observations on administrative boundaries using another geopandas function: sjoin_nearest. This will find the nearest feature, but we will need to use an appropriate projection (here we use the coordinate reference system EPSG:3112):

join_nnb_df = gpd.sjoin_nearest(inat_obs_xy, NSW_parks.to_crs(3112), distance_col="distances", how="left")

This is useful to include some observations near the boundaries of National Parks (for example less than 100m) :

obs_in_parks=join_nnb_df.loc[join_nnb_df.distances<100,]

print("From my {} observations in NSW, I have {} observations in terrestrial parks and reserves".format(n_obs,obs_in_parks.shape[0]))

From my 777 observations in NSW, I have 248 observations in terrestrial parks and reserves

Step 4: Summarise the data

Finally, we’ll look at how many observations I have in each of the parks and reserves I have visited so far:

obs_in_parks.groupby(['TYPE','NAME']).agg({'observed on': 'count',
'species guess': 'nunique'})

		observed on	species guess
TYPE	NAME
Flora Reserve	Letts Mountain	1	1
Flora Reserve	Maxwells	1	1
National Park	Blue Mountains	22	20
	Brisbane Water	3	3
	Deua	10	9
	Kamay Botany Bay	42	37
	Lane Cove	12	8
	Malabar Headland	12	12
	Monga	9	8
	Morton	2	2
	Royal	32	27
	South East Forest	1	1
	Sydney Harbour	46	39
Nature Reserve	Tarawi	44	35
Nature Reserve	Windsor Downs	6	6
Regional Park	Killalea	1	1
State Conservation Area	Garawarra	4	3

obs_in_parks.groupby(['iconic taxon']).agg({'species guess':['count', 'nunique'], 'NAME':'unique'})

	species guess		NAME
	count	nunique	unique
iconic taxon
Actinopterygii	1	1	[Malabar Headland]
Amphibia	1	1	[Garawarra]
Animalia	5	4	[Tarawi, Kamay Botany Bay, Malabar Headland]
Arachnida	10	10	[Letts Mountain, Deua, Brisbane Water, Tarawi,...
Aves	54	32	[Monga, Lane Cove, Tarawi, Malabar Headland, B...
Chromista	1	1	[Kamay Botany Bay]
Fungi	6	5	[Sydney Harbour, Lane Cove, Tarawi, Blue Mount...
Insecta	75	63	[Deua, Sydney Harbour, Killalea, Garawarra, Ka...
Mammalia	9	6	[South East Forest, Deua, Tarawi, Royal]
Mollusca	2	2	[Malabar Headland]
Plantae	63	49	[Maxwells, Monga, Morton, Lane Cove, Tarawi, R...
Reptilia	18	10	[Deua, Lane Cove, Kamay Botany Bay, Royal, Tar...

These lines of code will show fotos of the most popular observations of each iconic taxon in each park. I group the data twice, first I do the selection based on the points column for each combination of park and iconic taxon, then I iterate across the parks and join the figures in a container.

selection = (
    obs_in_parks
    .sort_values('points')
    .groupby(['TYPE','NAME','iconic taxon'])
    .first()
    .groupby(['TYPE','NAME'])
    .agg({'figure':'unique'})
)


sections = list()
for idx,row in selection.iterrows():
    sectionfigures="&nbsp;".join(row['figure'])
    sectionname="<figure class='mini'><p class='figsection'>%s <i>%s</i></p></figure>" % idx
    sections.append(sectionname + sectionfigures)

allsections="<div class='container'>%s</div>" % ("".join(sections))

display(HTML(allsections))

Flora Reserve Letts Mountain

Flora Reserve Maxwells

National Park Blue Mountains

Arachnida: Australian Funnel-web Spiders

National Park Brisbane Water

National Park Deua

National Park Kamay Botany Bay

National Park Lane Cove

National Park Malabar Headland

National Park Monga

National Park Morton

National Park Royal

National Park South East Forest

National Park Sydney Harbour

Nature Reserve Tarawi

Nature Reserve Windsor Downs

Regional Park Killalea

State Conservation Area Garawarra

The look of the output html code depends on the site’s css style definitions. Look at this file if you want to reuse/adapt my style.

This is still a small number of parks and reserves with iNat observations, I will need to plan my future trips around the state to increase these numbers.

Conclusion

In summary, we:

Downloaded observations with pyinaturalist.
Intersected observations with national park boundaries using geopandas.
Grouped and aggregated the data

Hope you find this useful!

Footnotes

My alter ego in the iNat world↩︎