Using Gis For Spatial Analysis

Spatial analysis in GIS involves three types of operations: Attribute Query-also known as non-spatial (or spatial) query, Spatial Query and Generation of new data sets from the original database (Bwozough, 1987). The scope of spatial analysis ranges from a simple query about the spatial phenomenon to complicated combinations of attribute queries, spatial queries, and alterations of original data.

Attribute Query: Requires the processing of attribute data exclusive of spatial information. In other words, it's a process of selecting information by asking logical questions.

Example: From a database of a city parcel map where every parcel is listed with a land use code, a simple attribute query may require the identification of all parcels for a specific land use type. Such a query can be handled through the table without referencing the parcel map (Fig. 1). Because no spatial information is required to answer this question, the query is considered an attribute query. In this example, the entries in the attribute table that have land use codes identical to the specified type are identified.

Parcel No.

Size

Value

Land Use

102

7,500

200,000

Commercial

103

7,500

160,000

Residential

104

9,000

250,000

Commercial

105

6,600

125,000

Residential

A sample parcel map Attribute table of the sample parcel map

Figure 1: Listing of Parcel No. and value with land use = 'commercial' is an attribute query. Identification of all parcels within 100-m distance is a spatial query

Spatial Query: Involves selecting features based on location or spatial relationships, which requires processing of spatial information. For instance a question may be raised about parcels within one mile of the freeway and each parcel. In this case, the answer can be obtained either from a hardcopy map or by using a GIS with the required geographic information (Fig. 2).

B! Parcels for rezoning

Parcels for notification

Figure 2: Land owners within a specified distance from the parcel to be rezoned identified through spatial query

Example: Let us take one spatial query example where a request is submitted for rezoning, all owners whose land is within a certain distance of all parcels that may be rezoned must be notified for public hearing. A spatial query is required to identify all parcels within the specified distance. This process cannot be accomplished without spatial information. In other words, the attribute table of the database alone does not provide sufficient information for solving problems that involve location.

While basic spatial analysis involves some attribute queries and spatial queries, complicated analysis typically require a series of GIS operations including multiple attribute and spatial queries, alteration of original data, and generation of new data sets. The methods for structuring and organizing such operations are a major concern in spatial analysis. An effective spatial analysis is one in which the best available methods are appropriately employed for different types of attribute queries, spatial queries, and data alteration. The design of the analysis depends on the purpose of study.

GIS Usage in Spatial Analysis

GIS can interrogate geographic features and retrieve associated attribute information, called identification. It can generate new set of maps by query and analysis. It also evolves new information by spatial operations. Here are described some analytical procedures applied with a GIS. GIS operational procedure and analytical tasks that are particularly useful for spatial analysis include:

• Single layer operations

• Multi layer operations/ Topological overlay

• Spatial modeling

• Geometric modeling

□ Calculating the distance between geographic features

□ Calculating area, length and perimeter

□ Geometric buffers.

• Point pattern analysis

• Network analysis

• Surface analysis

• Raster/Grid analysis

• Fuzzy Spatial Analysis

• Geostatistical Tools for Spatial Analysis

Single layer operations are procedures, which correspond to queries and alterations of data that operate on a single data layer.

Example: Creating a buffer zone around all streets of a road map is a single layer operation as shown in the Figure 3.

— Streets

Buffer zones

Figure 3: Buffer zones extended from streets

Figure 3: Buffer zones extended from streets

Multi layer operations: are useful for manipulation of spatial data on multiple data layers. Figure 4 depicts the overlay of two input data layers representing soil map and a land use map respectively. The overlay of these two layers produces the new map of different combinations of soil and land use.

Figure 4: The overlay of two data layers creates a map of combined polygons

Topological overlays: These are multi layer operations, which allow combining features from different layers to form a new map and give new information and features that were not present in the individual maps. This topic will be discussed in detail in section of vector-based analysis.

Point pattern analysis: It deals with the examination and evaluation of spatial patterns and the processes of point features. A typical biological survey map is shown in Figure 5, in which each point feature denotes the observation of an endangered species such as big horn sheep in southern California. The objective of illustrating point features is to determine the most favourable environmental conditions for this species. Consequently, the spatial distribution of species can be examined in a point pattern analysis. If the distribution illustrates a random pattern, it may be difficult to identify significant factors that influence species distribution. However, if observed locations show a systematic pattern such as the clusters in this diagram, it is possible to analyze the animals' behaviour in terms of environmental characteristics. In general, point pattern analysis is the first step in studying the spatial distribution of point features.

Figure 5: Distribution of an endangered species examined in a point pattern analysis

Figure 5: Distribution of an endangered species examined in a point pattern analysis

Network analysis: Designed specifically for line features organized in connected networks, typically applies to transportation problems and location analysis such as school bus routing, passenger plotting, walking distance, bus stop optimization, optimum path finding etc.

Figure 6 shows a common application of GIS-based network analysis. Routing is a major concern for the transportation industry. For instance, trucking companies must determine the most cost-effective way of connecting stops for pick-up or delivery. In this example, a route is to be delineated for a truck to pick up packages at five locations. A routing application can be developed to identify the most efficient route for any set of pick-up locations. The highlighted line represents the most cost-effective way of linking the five locations.

Figure 6: The most cost effective route links five point locations on the street map

Surface analysis deals with the spatial distribution of surface information in terms of a three-dimensional structure.

The distribution of any spatial phenomenon can be displayed in a three-dimensional perspective diagram for visual examination. A surface may represent the distribution of a variety of phenomena, such as population, crime, market potential, and topography, among many others. The perspective diagram in Figure 7 represents topography of the terrain, generated from digital elevation model (DEM) through a series of GIS-based operations in surface analysis.

Figure 7: Perspective diagram representing topography of the terrain derived from a surface analysis

Grid analysis involves the processing of spatial data in a special, regularly spaced form. The following illustration (Figure 8) shows a grid-based model of fire progression. The darkest cells in the grid represent the area where a fire is currently underway. A fire probability model, which incorporates fire behaviour in response to environmental conditions such as wind and topography, delineates areas that are most likely to burn in the next two stages. Lighter shaded cells represent these areas. Fire probability models are especially useful to fire fighting agencies for developing quick-response, effective suppression strategies.

In most cases, GIS software provides the most effective tool for performing the above tasks.

m

Current burn Time 2 Time 3 Unburned

F

i IS

f!

Ï m

is Fi

i i;i

ÎÏ

\k

tl

u

Figure 8: A fire behaviour model delineates areas of fire progression based on a grid analysis

Figure 8: A fire behaviour model delineates areas of fire progression based on a grid analysis

Fuzzy Spatial Analysis

Fuzzy spatial analysis is based on Fuzzy set theory. Fuzzy set theory is a generalization of Boolean algebra to situations where zones of gradual transition are used to divide classes, instead of conventional crisp boundaries. This is more relevant in many cases where one considers 'distance to certain zone' or 'distance to road', in which case the influence of this factor is more likely to be some function of distance than a binary 'yes' or 'no'. Also in fuzzy theory maps are prepared showing gradual change in the variable from very high to very low, which is a true representation of the real world (Bonhan-Carter,

As stated above, the conventional crisp sets allow only binary membership function (i.e. true or false), whereas a fuzzy set is a class that admits the possibility of partial membership, so fuzzy sets are generalization of crisp sets to situations where the class membership or class boundaries are not, or cannot be, sharply defined.

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