Cartographic modeling by GIS analysis - procedure with an example

CARTOGRAPHIC MODELING

Cartographic modeling involves the use of basic GIS functions in a logical sequence to solve complex spatial problems.

It was developed to model:

Statement of conditions or assumptions

1. Land-use planning alternatives and
2. Applications that require integrated analysis of multiple geographically distributed factors

• The term was coined by Dana Tomlin in 1983.
• Cartographic modeling lies completely under the raster domain. 
• The nature of analysis is purely additive or subtractive and this complements the values assigned to the raster format of the data
• The digitised data is layered and these layers are combined to construct constraint maps that can be analysed with reference to any specific geographic problem to arrive at the best alternative.

A cartographic model has the ability to form a logical sequence. The process of cartographic modeling is characterised by working backward to insure that all data that will be needed are identified. This helps to avoid collecting data that will not be needed. The process insures that any judgements to be made are explicitly identified. Hence, subjective judgements are an integral part of cartographic modeling.

Cartographic modeling is a common way of expressing and organising the methods by which spatial variables and spatial operations are selected and used to develop an analytical solution within a GIS.

Cartographic modeling is based on the concept of data layers, operations and procedures. Cartographic modeling capabilities are found in most GIS software. 

Modeling is a logical or mathematical formulation that attempts to simulate some aspect of the real world.

The five steps involved in cartographic modeling are listed below and elaborated subsequently:

1. Statement of problem or objectives
2. Statement of conditions or assumptions
3. Methodology
4. Implementation and
5. Evaluation

Statement of problem involves dividing the problem into sub-problems. The objective provides a direction and a clear end to the activity. It helps by the possible routes to solving for the objectives

Statement of conditions or assumptions includes the conditions of the problem. For example: current state, background or case history of the problem. Assumptions in the model define the limitations of the analysis. An assumption of most models is that the processes of the past will continue in the future.

Methodology involves:

1. Assembly of sub-models into a model, which can be sub-divided into:
1. Identification of sub-problems (analogous to the concept of divide and conquer)
2. Development of sub-models that address the sub-problems
3. Development of a strategy for integrating the sub-models
4. Developing a flowchart that shows the parts in the context of the whole
2. Identification of:
1. Data sets needed
2. Spatial operations
3. Non-spatial operations and
4. Interaction of spatial and non-spatial data

Implementation involves:

Implementing the model using the analytic tools available in GIS. It also involves implementation of techniques to circumvent the limitations of the GIS system.

Evaluation

This involves testing the effectiveness of the model. If the model does not conform to expectations,  its assumptions and components should be re-examined and adjusted where necessary. The above procedures should be performed in an iterative fashion until the objectives are achieved.

EXAMPLE

Problem: The municipal corporation of a city would like to measure the environmental equity as compared to the siting of waste transfer stations

Restating the problem: Is one particular income class bearing the burden of waste transfer stations ?

Conditions and assumptions: 

1. Impact of waste transfer sites on surrounding communities is negligible beyond 500m
2. Within 500m the effect is uniform
3. Income classes are distributed evenly throughout a census tract

Methodology:

• Data sets needed
• income by census tract
• location of waste transfer sites
• Spatial operations
• points in polygon
• buffer
• overlay
• Operations on attributes
• select
• reclass
• calculate area estimates and
• generate statistics

Implementation:

• Outline for flow of implementation
• select only waste transfer sites in the city
• generate a 500m buffer around these sites
• select income data from census data and reclass into three income classes: low, medium and high
• add a field to hold the original area of each census tract prior to polygon intersection
• recalculate the income classes based on percentage of census tract left in the intersected polygons. Use the original area field that was brought along in the intersection
• calculate the totals for each of three generated income classes for the entire city
• generate pie charts for the number in each income class for:
•  the entire city and 
• affected areas
• create a map showing output

Evaluation

• The results obtained should be evaluated against the methodology used to test the validity of the model.
• The model should accurately represent the process being modeled.
• A statistical analyses that includes both qualitative and quantitative observations should be performed.
• Based on the above listed criteria, changes to improve the model should be documented and the modeling should be repeated.