Vector Data

GISBox is a one-stop 3D GIS data editing, conversion and publishing platform that supports editing in multiple GIS formats such as OSGB/GEOTIFF/RVT, converting to 3DTiles/Terrain and publishing.

Introduction

Vector data is data that uses x and y coordinates to represent the location and shape of map graphics or geographic entities in rectangular coordinates. Vector data generally records the coordinates to accurately represent the spatial location of geographic entities as much as possible.

Data Format

The following are common vector data formats:

Shapefile (.shp)
GeoJSON (.geojson)
KML/KMZ (.kml/.kmz)
File Geodatabase (.gdb)
SpatiaLite (.sqlite)
GeoPackage (.gpkg)
GML (.gml)
PostGIS (PostgreSQL)
DXF (.dxf)
TopoJSON (.topojson)
MapInfo TAB (.tab)
Esri Personal Geodatabase (.mdb)
Esri ArcInfo Coverage
OpenStreetMap PBF (.pbf)
CSV with Geometry (.csv)

These formats cover a wide range of options from general exchange formats to professional GIS data storage formats.

Pros

Tight data structure and low redundancy: Vector data has a compact structure and can store geographic data efficiently, reducing data redundancy.
High data accuracy: Vector data uses discrete points, lines, surfaces or combinations to represent spatial entities, and identifiers are used to describe the attributes of content and spatial entities, so it has high accuracy and is suitable for geographical phenomena that need to be accurately represented.
Clear spatial topological relationship: Vector data can fully describe the topological structure, which is convenient for network analysis, spatial retrieval and other operations.
Good graphic display quality: The graphics output by vector data are accurate and beautiful, suitable for making high-quality maps and visual displays.
Easy to update and restore: The recovery, update and integration of vector data structure and attribute data can be realized, which is convenient for data maintenance and update.

Cons

Complex data structure: Vector data structure is relatively complex, which is not conducive to data standardization and normalization. This may lead to difficulties in data exchange and sharing.
Difficulty in combining overlay analysis with raster images: When performing polygon overlay analysis, vector data is difficult to combine with raster images, and its ability to express spatial changes is relatively poor.
Difficulty in mathematical simulation: Due to the structural characteristics of vector data, it may be difficult to perform certain mathematical simulations.
High requirements for software and hardware technology: Processing and analyzing vector data requires high software and hardware technical support, which may increase costs and technical barriers.
High display and drawing costs: Although the graphics output by vector data is of high quality, the corresponding display and drawing costs are also high.

Application Scenario

Vector data can accurately describe the geographical elements of a city, such as buildings, roads, green spaces, etc., and provide basic data support for urban planning and land management. It can be used for land use analysis, land use planning, building height analysis, etc.