Since early versions, the cGIS Pro platform has provided a programming interface for accessing data. This allows developers to create applications and services, integrate cGIS Pro with third-party systems, and increase the flexibility of the entire solution. cGIS Pro now includes a Python library that exposes the hierarchy of platform objects in terms of: projects – layers – data.
Python is one of the most popular languages for working with geodata due to its simplicity, rich ecosystem of libraries (pandas, GeoPandas, NumPy, Shapely, PyProj), active community and fast development. It allows you to quickly create reliable and scalable solutions for automation, analysis and integration of cGIS Pro data with the company's application stack.
– enumeration of collections: projects, layers, metadata;
– search by names and IDs in collections;
– access to layer data with server-side filtering, selection of only the necessary attributes and pagination support;
– cross‑integration with popular GIS tools and data analysis environments (e.g., QGIS, Jupyter);
– quick analysis of map layers: data validation, building thematic graphs and diagrams.
– time savings due to automation: regular uploads, updates, and quality checks of data can all be performed on a schedule or on events, without manual operations;
– scalability: filtering and pagination allow you to work with large datasets without overloading network and memory;
– data quality improvement: automatic integrity checks, detection of duplicates, topology violations and business rules before data is included in reports;
– flexible integration: combining cGIS Pro layers with external sources (databases, files, API) into a single analytical pipeline;
– reduced dependency risks: the standard Python stack simplifies migrations and code reuse;
– transparency and reproducibility: scripts document processes, simplifying auditing and maintenance;
– fast data understanding: from query to visualization and report – in one environment.
– urban analytics: automatic selection of road layers filtered by street class, calculation of speed profiles, formation of daily summaries and maps of "bottlenecks" in Jupyter;
– asset management: regular audit of engineering network layers for missing attributes and geometric errors, with the results uploaded to the dashboard;
– ecology and monitoring: combining land cover layers with external telemetry data, automatic classification and building thematic maps in GeoPandas.
The example is illustrative in nature, the names of modules and methods may depend on the version of the cGIS Pro data access library.
python
# ILLUSTRATIVE EXAMPLE
from pycgispro import cGISPro, cGISProLayerData
from pycgispro.filters import *
cgispro = cGISPro(
base_url=os.environ[“CGISPRO_BASE_URL”],
rest_api_key=os.environ[“CGISPRO_REST_API_KEY”]
)
# project and data layer
project = cgispro.projects.find_by(‘SampleProject’)[0]
layers = project.layers
lrs = layers.find_by (‘Building')
data = lrs[0].data
# list of fields and filters
field_list = [‘Number of living quarters', ‘Year of construction’, ‘Area’] data_filter = filter_(‘and’,
filter_ (‘not null', ‘Number of living quarters'),
filter_ (‘not null', ‘Year of construction’),
filter_ (‘not null', ‘Area')
)
# select-data query (limited to a small amount)
rows_count = 50
res = data.select(page_size=rows_count, page=1, field_list=field_list,
filter=data_filter)
# analytics in pandas/GeoPandas
import pandas as pd
columns = [field[‘label’] for field in res[‘field_list’]]
df = pd.DataFrame(res[‘value_list’], columns=columns)
summary = df.groupby ([‘Year of construction’]).sum().reset_index()
print(summary)
print(summary)
Данные проекта “© how-old-is-this.house, 2024″
– server–side filtering: use filters and field selection to reduce the amount of data transferred.
– pagination: adjust the page size based on network bandwidth and memory;
– indexing: for frequently used filters, make sure that indexes for key fields are created in the data source;
– vector formats: for intensive geoanalytics, use GeoPandas and efficient formats (GeoParquet) for intermediate data storage;
– caching: cache immutable directories and layers by version or hash;
– logging and monitoring: log query parameters and response time, perform repeated query attempts with exponential pause;
– security: store tokens in environment variables/secret repositories, apply minimal access privileges.
Q: What do I need to get started?
A: Access to cGIS Pro, access token, installed Python data access library and basic analysis stack (Pandas/GeoPandas, Jupyter).
Q: Is it possible to use the library with Jupyter?
A: Yes. This is a convenient way to quickly prototype queries, visualize results, and create reproducible reports.
Q: Is integration with other GIS tools supported?
A: Yes. Cross‑integration with popular GIS tools and data analysis ecosystems, including QGIS and Jupyter, is possible.
The Python data access library cGIS Pro makes access to projects, layers, and data predictable, fast, and secure. It speeds up integrations, improves data quality, and reduces data analysis time. Start with simple usage examples and gradually move on to full-fledged data analysis and reporting pipelines.
Для тестирования работы cGIS Pro Python API использовались данные картографического издательства «Кон-Тики» https://kontikimaps.ru
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