Hello! I'm ippuku_time, a GIS implementation support consultant.
This is the 10th installment of the "Learn SuperMap iDesktopX in 5 Minutes"!
In the previous article, we learned about queries, which help you extract the data you need. Today, let's introduce spatial analysis — the most powerful feature in GIS, which uses extracted data to generate new insights. Spatial analysis goes beyond simple map creation; it is the key to solving geographic problems and making future predictions possible.
1. What is spatial analysis?
Spatial analysis refers to a set of techniques that reveal the meaning and patterns behind geographical phenomena by analyzing the location, shape, and attributes of geospatial data.
iDesktopX provides comprehensive spatial analysis tools that support both vector and raster data.
Conceptual image: Deriving new geographical knowledge from data
2. Vector Analysis: Analyzing Relationships Based on Shapes
Vector analysis handles spatial data with clear shapes such as points, lines, and polygons.
(1) Buffer Analysis
This tool creates an area (buffer) within a specified distance from a feature.
Examples:
“Areas within a 500m walking radius of each station in Minato Ward”
“Areas within 50m of the Metropolitan Expressway where noise impacts are expected”
(2) Overlay Analysis
Overlaying multiple layers to create a new layer is one of the most fundamental GIS techniques.
Example:
By overlaying a Minato Ward land-use map with a flood risk map, you can extract commercial areas at risk of flooding.
Overlay methods:
Intersect: Extracts only overlapping areas
Union: Combines multiple layers into one
Clip: Cuts one layer using the boundary of another
3. Raster Analysis: Analyzing Continuous Surfaces
Raster data represents the Earth's surface as a grid of pixels. It is suitable for analyzing continuous phenomena such as elevation, temperature, and precipitation.
(1) Surface Analysis
Extracts terrain information from DEM (digital elevation model) data:
Slope and aspect analysis: eva1uates steepness and the direction slopes face (useful for hill gradients or building sunlight exposure in Minato Ward)
Viewshed analysis: Identifies visible areas from a viewpoint (e.g., the Tokyo Tower observation deck)
Cross-sectional analysis: Extracts elevation profiles along lines (e.g., along the Hibiya Subway Line)
(2) Density Analysis
Calculates and visualizes the distribution of point data as a heat map.
Useful for identifying "hot spots," such as areas with cafe clusters or frequent traffic accidents.
4. Hydrological Analysis: Simulating Water Flow
This specialized analysis uses DEM data to simulate how water flows and accumulates. It plays a critical role in urban disaster prevention, helping predict flood-prone areas in the event of heavy rainfall.
5. Spatial Statistical Analysis: Discovering Geographic Patterns
This category applies statistical methods to identify patterns hidden in spatial data.
Cluster (Hot Spot) Analysis: Detects where a phenomenon is significantly concentrated or sparse (e.g., crime hotspots)
Geographically Weighted Regression (GWR): eva1uates how multiple factors influence a phenomenon (e.g., housing prices) while accounting for spatial variability
Summary
This article introduced spatial analysis, one of the most powerful capabilities of GIS. iDesktopX offers a wide range of analytical tools, from basic vector techniques (buffers and overlays) to raster-based topography analysis and advanced spatial statistics. Combining these tools allows users to move beyond visualization and gain scientifically grounded insights for decision-making.
Next time in Part 11: “Which is the best route? Transportation Network Analysis.”
We’ll explore features that solve everyday transportation problems, such as shortest routes and service area calculations. Stay tuned!
