Synthetic Generation of Spatial Polygons On Cloud

Spatial Data generated from varied resources are collected for applications in analysis of land-patterns and population growth among many. While the data collected is vast, majority of it is unavailable in formats or quantity required for research projects. Current data generation tools produce over...

Full description

Saved in:
Bibliographic Details
Published in2024 3rd International Conference for Innovation in Technology (INOCON) pp. 1 - 6
Main Authors D, Ankur, D, Mrunalini Thamankar, Rai, Nireeksha D, G, Veeresh R, Auradkar, Prafullata
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2024
Subjects
Benchmark testing
Convex Hull
Data Generator
Generators
MBR
Polygon Validation
Sociology
Spark
Spatial databases
Spatial Polygons
Statistics
Synthetic data
Technological innovation
Voronoi
Online AccessGet full text

Cover

More Information
Summary:Spatial Data generated from varied resources are collected for applications in analysis of land-patterns and population growth among many. While the data collected is vast, majority of it is unavailable in formats or quantity required for research projects. Current data generation tools produce overly rigid geometric objects which do not reflect real-world spatial variations of polygons. We present a web based synthetic generator tool for spatial polygons capable of generating an impressive 0.7 million polygons which would take a range of user inputs including spikiness, irregularity and dataset size to generate requirement quenching data. Generated polygons are thus subject to rigorous benchmarking to ensure that they are close to real-world in addition to being geometrically valid. We further explore the computational problems and propose a spark-based approach of parallel computation, resulting in a remarkable 50% reduction in processing time.
DOI:10.1109/INOCON60754.2024.10511892