Spatial Analysis

 Query in GIS

The fundamental purpose of any GIS is to retrieval of desired information. This is achieved through a process known as query. Query are of two types - spatial and attribute. 

Spatial Query

A spatial Query is a fundamental analytical operation used to retrieve spatial features based on their  topological relationships, geometric properties or thematic properties within a defined geographic space.

 Example for topological query - 

The Scenario: Urban Planning

Imagine you are working with a city's GIS database containing three layers:

1. Parcels (Polygons representing land ownership)
2. Zoning Districts (Polygons for Residential, Industrial, etc.)
3. Utility Lines (Lines representing water pipes)

1. Adjacency (What is next to what?)

The Query: "Find all residential parcels that share a boundary with an industrial zone."

2. Containment (What is inside what?)

The Query: "Select all water valves located within the District."

3. Connectivity (What is linked to what?)

The Query: "Identify all pipes that will lose water pressure if Valve A is closed."

In geometric query, selection of feature is based on a particular query related to a geometric parameter, as for example “select all the industries that are located less than 100m away from the river” or “select tea shops located within 100m near schools”.

 

A thematic query is based on a single or particular theme. For example, a query could “Select all plantations with teak as major species” from the data given, or “select all springs” located in the given spatial unit.

 Attribute Query 

Attribute query focuses entirely on the non-spatial data stored in the database table. An attribute query uses Structured Query Language (SQL) to filter the data based on their characteristics. Example – in a dataset showing population of India, query should be “Select places in India with a population of more than 10 lakhs.”

Map Algebra 

Map algebra refers to conducting various mathematical, logical and spatial analysis operations, based on raster data. 

These techniques were first developed by Dana Tomlin 

Map algebra can be applied to a single or multiple raster layers to produce a new raster layer.

 Map algebra operators include 

• Arithmetic operators such as sum, multiplication, divide and subtraction

• Logical operators such as less than, greater than, less than or equal to

• Conditional operators - 
  conditional operators allow you to
  apply operations selectively to cells based on whether they meet specific criteria. Example - Reclassify areas with a slope less than 15 degrees as "Good" (value 10) for construction and all other areas as "Unsuitable" (value 1).  

 Overlay Analysis

Overlay Analysis is one of the most powerful functions in GIS. 

It involves superimposing multiple layers of different geographic data (representing the same area) to create a new, single output layer that contains the combined information of both. 

It is essential for suitability mapping, site selection, and identifying relationships between different spatial themes. 

Overlay analysis in GIS is broadly categorized Vector and Raster overlay.

1. Vector Overlay (Feature-on-Feature)

Vector overlay involves combining points, lines, and polygons. It is mathematically complex because it creates new geometries (new points, line segments, or polygons) where features intersect. Vector overlay mainly is of three types.

• Point-in-Polygon: Identifies which points fall inside which polygon (e.g., "Which water wells are in this county?").
• Line-in-Polygon: Determines which line segments fall within a polygon (e.g., "How many miles of highway are inside the city limits?").
• Polygon-on-Polygon: The most common; combines two sets of areas (e.g., "Where are residential zones located on steep slopes?").
  
  

2. Raster Overlay (Cell-by-Cell)

Raster overlay is often faster and is used for modelling continuous data (like elevation or rainfall). It is performed using Map Algebra.

• Mathematical Overlay: Adding, subtracting, or multiplying cell values. For example, Elevation Layer - Water Table Layer = Depth to Water.
• Boolean Overlay: Using logical operators (AND, OR, NOT) to create "True/False" maps (e.g., "Where is it both sunny AND flat?").
• Weighted Overlay: Weighted overlay is a powerful spatial analysis technique used to solve multi-criteria problems, such as finding the "best" location for a new hospital, identifying areas at high risk for landslides, or determining the most suitable habitat for an endangered species. It allows to rank the importance of different factors relative to one another.  

  

 

 

 Proximity Analysis

A geospatial technique that measures, maps, and analyzes the relationships and distances between geographic features (points, lines, polygons) to determine accessibility, influence, or nearness. 

Common methods include creating buffer zones, identifying nearest neighbors, and calculating straight-line or network-based distances. It is crucial for site selection, risk assessment, and resource planning. 

It evaluates “how near or how far” objects are located in geographic space and is fundamental in understanding spatial interaction between environmental and anthropogenic variables.

Union

It is a spatial overlay tool that combines two or more polygon layers into a single output, preserving all features, boundaries, and attributes from the input layers. 

It creates new features in areas of overlap and keeps non-overlapping areas, often used for multi-criteria analysis to combine spatial data. 

 

Intersection

It is a geoprocessing overlay operation that calculates the spatial intersection of two or more vector datasets (points, lines, or polygons)datasets. 

It creates a new feature class containing only the overlapping areas, lines, or points, preserving attributes from all input layers, making it ideal for identifying coincidental spatial features.

 

Network Analysis

Network analysis in GIS is

a set of techniques used to model and analyze the movement of resources, vehicles, or people along interconnected networks, such as road systems or pipelines.

 It leverages graph theory and topology to determine optimal routes, service areas, and facility locations, crucial for transportation planning, logistics, and infrastructure management. 

 

Spatial Interpolation - exact and inexact methods

Spatial interpolation predicts values for unknown locations using sampled point data, divided into exact (matches sample values) and inexact/approximate (smooths data, reducing noise) methods. 

Inexact interpolators are often referred to as smoothing interpolators because they produce smoother surfaces 

Common exact methods include Inverse Distance Weighted (IDW) and Splines, 

Kriging and Global/Local Polynomials are typically inexact, offering better handling of data errors.