INTRODUCTION
The “vector and raster data models” introduced are the primary data models of geospatial data discussed in my previous blog. Simply, vector and raster elaborate on which type of location-based condition we have to work on.
VECTOR DATA
· Vector data is used to modify location-based information using points, lines, or polygons. Here we can highlight exact boundaries or areas by using coordinates and attributes.
“It is better than raster data because it gives proper scalability and is easily editable. ”
· Characteristics of vector data: So guys, let’s discuss the features that play a major role in executing vector data.
- Points: Represent locations (e.g., a tree, a well).
Lines: Represent linear features (e.g., rivers, roads).
Polygons: Represent area features (e.g., lakes, buildings).
2.Attributes: This class stores detailed information about features in tables (e.g., the population of a city or type of land use).
3.Storage: Lightweight as it only stores coordinates and attributes.
COMMON FORMATS
“Common formats are essential, as they support performing various operations on vector data on different software and we can also represent vector data in different file formats.”
1.Shapefiles (.shp):- It is one of the common file formats. It stores data as points, lines, and polygons.
2.GeoJSON(Geographical JavaScript Object Notation):- This is a type of open standard format that allows us to encode points, line strings, and polygons within provided data using JavaScript.
3.Keyhole Markup Language (KML):- It is used for tagging and labeling on Google Maps and Earth using color shading. The longitude and latitude coordinates are used to form a boundary from one point to another.
USE OF VECTOR DATA
“Vector data used to highlight and scale a particular location using coordinates of one to other. ”
LIBRARIES USED
1.GeoPandas
· Use: Geopandas is a Python-based library directly applicable to geographic data works.
· Documentation: https://geopandas.org/en/stable/
· Features:
o It contains built-in Pandas and supports common formats like GeoJSON, Shapefiles, and PostGIS.
o It performs geometric operations (e.g., buffer, intersection, union).
o Integration with matplotlib for data visualization.
· Popular For: Easy-to-use vector data manipulation and analysis.
2.Shapely
· Use: Geometric operations and spatial relationships.
· Documentation: https://pypi.org/project/shapely/
· Features:
o Perform operations like intersection, union, and buffering.
o Analyze spatial relationships (e.g., contains, intersects).
· Popular For: Geometry processing independent of file formats.
3.PostGIS
· Use: Extends PostgreSQL for advanced vector data handling.
· Documentation: https://postgis.net/documentation/manual/
· Features:
o Spatial queries using SQL.
o Handles complex spatial datasets and analysis.
· Popular For: Storing and querying large vector datasets.
RASTER DATA
· Raster data represents the satellite image, aerial image, and scanned maps into a matrix of cells or grid lines. According to the provided data, some symbols or colors designate each cell to represent information.
“ Raster data represents continuous surfaces, like elevation or temperature, across a geographic area ”
· Characteristics of raster data:- So guys, let’s talk about the features that play a major role in executing raster data.
Structure: Matrix pixel formation (cells). Each pixel has a uniform value, e.g., elevation, temperature, or land cover.
Resolution: Determined using pixel size. Smaller pixels offer finer detail but increase file size.
Continuous Representation: Ideal for phenomena that change gradually (e.g., rainfall, soil moisture).
COMMON FORMATS
“Common formats in raster data to store grid-based image data into different formats like GeoTiff, JPEG, PNG, etc.”
1.Geographic Tagged Image File Format (GeoTIFF):- This format contains geographic metadata used to describe the actual location in space that each pixel in an image represents.
2.JPEG 2000:- Also known as pixel-based raster file format, JPEG can easily produce high-quality images by compressing an image.
3.NetCDF:- This feature is very useful for representing data like temperature, population density, and air index. They vary according to location and can be represented within a range.
USE OF RASTER DATA
“ Storing and representing geographical information, such as elevation models and city grids.”
LIBRARIES USED
1.Rasterio:-
· Use: Officially made to work on raster data using python.
· Documentation: https://rasterio.readthedocs.io/en/stable/
· Features:
I. Reads and writes raster formats with simple syntax.
II. Integration with NumPy for raster operations.
III. Metadata access and coordinate transformations.
· Popular For: User-friendly raster processing.
2.xarray:-
· Use: Handles multi-dimensional raster data.
· Documentation: https://docs.xarray.dev/en/stable/
· Features:
I. Works with NetCDF, HDF, and GeoTIFF.
II. Supports operations on gridded data (e.g., time-series analysis).
III. Excellent for environmental modeling and climate studies.
CONCLUSION
In my previous and this, I explain geospatial data, vector data, and raster data. What is the use of these data, In which format do they gather and display images, what kind of libraries do they use to perform tasks, and what are their applications that’s all.
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