Python is an advanced, contemporary language that may be used for a wide range of programming jobs. It is frequently employed as a scripting language to automate and streamline processes at the operating system level, but it may also be used to create extremely complicated applications. Python has been used to create scientific programming, desktop applications, games, web-based systems, utilities, and other higher-level components of numerous operating systems.
The Python interpreter is available for all popular operating systems as open-source software. Python is ideally suited for all programming tasks, from writing brief one-off scripts to creating enormous and complicated systems. It can even be used in interactive (command-line) format, which enables you to enter commands and get results right away.
The forms of geospatial data are numerous. Over the years, several Geographical Information System (GIS) manufacturers have created their file formats, and numerous organizations have also established their standards. When adding geospatial data to your database, it is frequently required to use a Python module to read files in the right format. Python libraries are the best addition to GIS because they let you enhance its fundamental features. Since many (or most) of the many GIS Software programs (such as ArcGIS, QGIS, PostGIS, etc.) offer an interface to do analysis using Python scripting, Python is an incredibly helpful language to learn for GIS. In essence, Python scripts are command-based scripts that contain the data types, statements, and functions needed to carry out the geoprocessing instructions.
1. Arcpy
You're probably already aware of the ArcPy library if you use Esri ArcGIS. For geoprocessing operations, ArcPy is designed. However, it is also used for data translation, management, and map creation with Esri ArcGIS in addition to spatial analysis. The raster, vector, and spatial information workflows in ArcGIS can be automated and expanded using this Python geoprocessing module from Esri. It's crucial to remember that the library needs a valid Esri license.
2. Geopandas
Geopandas is like a GIS with pandas. The Geopandas package, however, provides a geographic component in place of simple tabular analysis. Geopandas employs its Python libraries, Fiona and Shapely, for overlay operations. By adding functionality to the already helpful Pandas library, GeoPandas makes it possible to handle geometric data in Pandas DataFrames instead of requiring a spatial relational database. Fiona, which can read and write, and Shapely, which does computational geometry operations, are both relied upon by GeoPandas.
3. PyProj
The PyProj library's interaction with geographical referencing systems serves as its primary function. With a variety of geographic reference systems, it can project and convert coordinates. For each given datum, PyProj can also calculate geodetic distances. a collection of functions that encompass the C library PROJ (Cartographic Projections and Coordinate Transformations Library). Converting coordinates between different spatial references is possible with PyProj.
4. GDAL/OGR
The GIS format and extensions are translated using the GDAL/OGR library. The center of gravity for raster processing is the Geospatial Data Abstraction Library (GDAL). It is a crucial component of any Extract, Transform, and Load (ETL) workflow since it offers incredibly versatile reading and writing features for both raster (the GDAL component) and vector (OGR) formats. The great majority of GIS software, as well as the entire geospatial industry, rely on GDAL. Although GDAL is developed in C++ and executed via the command line, it also has a collection of Python bindings that are updated regularly by the GDAL community. It is utilized in some capacity for translation in QGIS, ArcGIS, ERDAS, ENVI, GRASS GIS, and nearly all GIS software. Currently, GDAL/OGR supports 162 raster drivers and 97 vector drivers.
5. Rasterio
Rasterio, created by MapBox, is an alternative to GDAL for accessible raster processing. Rasterio is based on GDAL and offers a lot of the same capabilities, but it has a more Computationally efficient language style, which some users may find more comfortable. It should be noticed that there are incompatibilities between the Python bindings for Rasterio and GDAL, forcing you to select one over the other in the same Python context.
6. RSGISLiB
A collection of satellite image data tools for raster analysis and processing is included in the RSGISLib package. It analyses, filters, and computes statistics on imagery, to mention a few. The component for object-based classification and segmentation is my particular favorite (GEOBIA).
Python methods are available for several remote sensing activities, including image classification, zonal statistics, change detection, and time series analysis, through the Remote Sensing and Geographical Information Systems Library (RSGISLib).
7. Shapely
This library lets you interact with the three primary types of geometric objects: Point, LineString, and Polygons+ geometry collections. There are many more, including linear rings, multi-points, multi-polygons, etc., but for now, these will work just well because the approaches are extremely portable. It allows for PostGIS-style geometric computation outside of an RDBMS and without the use of SQL. It is necessary to combine Shapely with other libraries to access and create spatial files.
8. Fiona
Fiona interfaces well with other Python GIS tools like pyproj.dt.iz, Rtree, and Shapely and can read and write real-world data utilizing multi-layered GIS formats and compressed virtual file systems. Recall that Shapely requires additional libraries to read and create spatial files. Fiona, though, fills that gap. Fiona is a compact library of functions that wraps around OGR and is focused on reading and writing vector data. It uses Python's method of accessing data in memory rather than OGR Bindings' usage of C pointers, giving it a more Pythonic syntax than OGR.
9. GeoPlot
A top-notch library for geographic charting. It is a cartopy and matplotlib addon that simplifies mapping, similar to seaborn for geospatial. Since it works on top of numerous other well-known geospatial libraries, it makes the usual coding requirements simpler.
10. PDAL
The heart of point cloud processing is the Point Data Abstraction Library (PDAL). The library is excellent for creating data pipelines for point cloud processes. PDAL is just a C++ library having Python bindings, just as its cousin GDAL. Developers can create a pipeline in JSON format that can be executed from a Python script using the PDAL Python API. Additionally, it provides the ability to add unique Python functions that expand PDAL's default functionality. The drawback of PDAL is that it has a rather steep learning curve and syntax that Python-only users can find alien.
11. LasPy
For reading and writing point cloud data in the common LAS (or compressed version, LAZ) formats, LasPy is an easy-to-use Python library. Although LasPy has less capability than PDAL, it has the benefit of being simple to install and use in cloud-hosted settings, which may struggle with PDAL's C++ requirements.
12. NumPy
The attribute table is taken from the NumPy library and placed in a structured array. Any scientific computation is substantially faster once it is in an organized array. One of its best features is how you can use SciPy and other Python modules to perform complex statistical procedures. The traditional Python package for manipulating multidimensional array data structures is called Numpy. We can store point cloud data in a NumPy array and filter/process the data when combined with a reader/writer module like LasPy. Additionally useful for broad application in the geographic realm is NumPy.
13. Matplotlib
Usually plotting everything out is the best course of action when dealing with a large number of points. The matplotlib library is used by statisticians for visual display. Matplotlib handles everything. It produces maps, graphs, and charts. It does a respectable job of number crunching even with huge data. powerful 2-D and 3-D plotting with the ability to visualize maps using vector and raster data. Geographic animations and interactive visualizations are also supported by Matplotlib.
14. Ipyleaflet
Ipyleaflet, a combination of Jupyter notebook and Leaflet, is a tool for making interactive maps. A variety of adjustments, including basemap, geojson, and widget loading, are controllable. Additionally, a variety of map types, including choropleth, velocity data, and side-by-side views, are available. Within such a Jupyter Notebook environment, create interactive maps. Ipyleaflet is a gateway library that enables Python to use leaflet.js' Javascript mapping functionality. It is constructed using interactive UI widgets called Ipywidgets.
15. Folium
Folium is a bridge to leaflet.js and an alternative to Ipyleaflet. Folium is designed for static visualizations, but Ipyleaflet creates interactive widgets. This is where the two systems diverge. Folium has a helpful feature that makes it simple to convert an interactive map to HTML, making it an effective tool for web development. It provides you with the ability to handle your data with Python before visualizing it with the best JavaScript framework available.
16. Geemap
Geemap uses Google Earth Engine more for scientific research and data processing (GEE). The multi-petabyte GEE database of satellite images is explored by scientists and researchers expressly for their unique applications and uses with remote sensing data, even though anyone can use this Python library.
17. LiDAR
The LiDAR Python Package's sole function is to handle and display data from Light Detection and Ranging (LiDAR). It has capabilities, for instance, to extract topological features from digital elevation model (DEM) data and smooth, filter, and do these things. It fits its purpose for terrain and hydrological study, even though I can't see integration with raw LAS data.
Here is an example Ipyleaflet library with some different base maps.
Basic map of the world with zoom center to India.
Code:
import ipyleaflet
from ipyleaflet import Map,basemaps
lat,lon=28,72
bm=Map(center=(lat,lon)zoom=4)
bm
Hot map
Code:
Hot_map=Map(center=(lat,lon),zoom=3,basemap=basemaps.OpenStreetMap.HOT)
Hot_map
Esri World Map
Code:
Esri_map=Map(center=(lat,lon),zoom=5,basemap=basemaps.Esri.WorldStreetMap)
Esri_map
You can add a marker to your specific places with CSV data. I have created this map where there is a marker on some Mohalla clinic locations in Delhi. You can apply various styles to it. I have changed only the height and layout of the map.
Code:
import pandas as pd
from ipyleaflet import Marker
mhc=pd.read_csv("D:/Project/Abstracts/Dec_22/Gis_map_mhc.csv")
mh_c = Map(center=(28.621038805282232, 77.20972537025263), zoom=10, basemap = basemaps.Esri.WorldStreetMap)
for name, lon, lat in mhc[["Name of Aam Aadmi Mohalla Clinic", "long", "lat"]].values:
marker = Marker(location=(lat, lon), draggable=False, title=name, alt=name)
mh_c.add_layer(marker)
mh_c.layout.height="550px"
mh_c
With this Ipyleaflet you can change the default marker with your marker or image. The ipyleaflet's Icon() function can be used to build an icon. The URL of the location where an icon is stored must be sent to it. We can pass the icon size as well. That image will be downloaded and used as a marker by the ipyleaflet. Once an icon has been produced, it must be set as the icon object's attribute in the Marker() function.
Code:
mhc_2=Map(center=(28.625,77.2097), zoom=10, basemap=basemaps.Esri.NatGeoWorldMap)
for name, lon, lat in mhc[["Name of Aam Aadmi Mohalla Clinic", "long", "lat"]].values:
icon=Icon(icon_url='https://t4.ftcdn.net/jpg/00/82/56/61/240_F_82566172_tnGJVl0JzxJReC6qQFqCNRxlDMw2twjd.jpg', icon_size=[40, 40], icon_anchor=[22,94])
marker = Marker(location=(lat, lon), icon=icon, title=name, alt=name)
mhc_2.add_layer(marker)
mhc_2.layout.height="550px"
mhc_2
So these are the basic examples of the different basic maps with Ipyleaflet.This article lists some of the top Python-based geospatial data analysis and visualization tools. The best Python libraries for geospatial analysis and visualization have been discussed. If you believe we are missing any libraries, please let me know.
References
1. Research Guides: GIS, Cartographic and Spatial Analysis Tools: Python. guides.library.columbia.edu/geotools/Python.
2. ---. guides.library.columbia.edu/geotools/Python.
3. 22 Python libraries for Geospatial Data Analysis. (2021b, September 2). https://sungsoo.github.io/2021/09/02/geospatial-python.html
4. Solanki, S. (2020, September 16). ipyleaflet [Python] - Interactive Maps in Python based on leafletjs. https://coderzcolumn.com/tutorials/data-science/ipyleaflet-interactive-maps-in-python-based-on-leafletjs
