26 Spatial Analysis – Network Analysis

Dr Dinesh Kumar

 

CONTENTS

 

1.  Learning Objectives

 

2.  Introduction

 

3.  Network analysis

 

4.  Network data Model and Application

 

5.  Summary

 

6.  References

 

1. Learning Objective

 

To understand the concept of Spatial Analyst and Network data model in GIS Environment and its application

 

2. Introduction

 

Everything is distributed on earth’s surface; in other words, everything can be spatially defined or located. Phenomenon, event, processes and features happen to be within earth’s environment and are spatially distributed. Monitoring or observing these things becomes nearly impossible in a conventional or traditional way. In era of information technology, information about anything should be managed properly in order to maintain the records which becomes baseline or current state of a region. Geographic Information system has the capacity and potential to define, locate and analyze above mentioned things spatially. Structure and functioning of GI system is capable to analyze or modify or model the spatial trend and pattern of events, features, processes in 2-D, 3D and 4D aspects. The core of GIS functions as an efficient, fast and accurate tool to look deeper into spatially distributed things, making us to understand the real world in a scientific and better way. The process of enhancing new information underplaying within spatially located things, is carried out with the help of spatial analyst on GIS platform. Spatial analysis is an integrated and core function of GIS technology as GIS lays its foundation on spatial data either in form of vector or raster or reports.

 

It is indeed rightly said “everything is related to everything else but near things are more related than distant things” which is also the first rule of geography. In other way, spatial analysis searches out and highlights the properties and relationships among spatially located events, processes, and features termed as geographic phenomenon.

 

Spatial analysis is the process by which we transform raw data into useful information and it is used for spatial and non-spatial (attribute) query.

 

In the process of attribute query, it selects the information on the basis of logical questions. For example; if we have attribute table for age group, male female ratio, gross income per family; the query for “particular female age group having a particular income range” is attribute query as it does not require spatial information.

 

While in spatial query, such information based on spatial location requires the processing of spatial based information to meet conditions expressed in spatial query. An example might explain it more clearly: Spatial query for hospitals and fire brigade centers within 5km from city center or particular location, will select and process the information based on location.

 

Table 1: Example of Attribute and spatial query for spatial analysis

Spatial analysis does have more complicated analysis other than above mentioned methods. Spatial analysis is generally applied for Spatial and attribute query/analysis/modeling on GIS platform. Spatial analysis method can be categorized into:

3. Network Analysis

 

A lot of natural features appear as lines such as river streams, geological faults and sometimes the transitional boundaries. Humans have built various infrastructures which have linear appearance such as roads, rail tracks, pipelines, sever and underground optical routes. These natural and manmade linear features when put together spatially, create a network. Scientific analysis of such network requires network analysis methods. Considering the importance of such network, for example, road traffic network, rail network, river stream and other networks; optimizing the network methods becomes the need to transform network data into useful information and application.

 

In order to understand network analysis, it is important to highlight some basic entities such as network characteristics

 

Network characteristics includes

  • Set of linear feature
  • End or starting point of lines (nodes)
  • Links which joins nodes

Figure 1: Diagrammatic representation of network with nodes and arcs

 

To represent the network and its required information in GIS environment, network data model or network topology acts as conceptual model.

 

3.1. Network data model

 

Network data model accounts two core entities i.e. a node (zero dimensional) and an arc (one-dimensional), and consist of a peculiar nod-arc-area topology. It is a set of arcs with nodes at arc intersections. The 2-D conceptualization of network data model retains the network topology consistency.

 

The widely used network data model (geo-relational data model) organizes spatial and attribute data into

  • Logical spatial data model which maintains the geometry and associated topological information of vector data model
  • Relational database management RDBMS table which manages the attribute of the network. Geo-relational network data model assigns unique identifiers (UIs) to each spatial entity (node, arc) and data on entity’s attributes.

Figure 2: Diagrammatic representation of relational network data model

 

Geo-relational network data model has been summarized in Table-1 where UIs and attributes have been assigned for a network of a road.

 

4. Application of network data model

 

A number of applications of network data model is being practiced which can be categorized as:

  • Routing
  • Service areas
  • Closest facility
  • Origin-destination Cost Matrix

Table2: Relational data model representations of the arcs and nodes of a network (adapted from Goodchild, 1998)

 

4.1 Routing: It is used to find route between source and destination or in comlex sutivation, “”best” route in conditional situvation such considering 10-stop signals (fig-3).

 

 

4.2 Service Areas: It processes the information to estimate an area based on time or distance from a source. It can be used to find out a population exhibiting a particular feature (fig-4)

Fig-3: Routing between two places (Image source: Google map)

 

Fig-4: Service Area search within a distance using network data model Image source: ESRI

 

4.3. Closest Facility:

 

This feature is used to find out the N number of facility from a point of interest. This function can be used for ATM location, medical centers, and fire brigades within a distance range (fig-5).

Fig-5: Closest Facility estimation within a distance

Image source: ESRI

 

4.4. Origin-destination Cost Matrix:

 

It is updated or advanced option for routing in which inputs (source) and destinations are multiple in numbers and their relative distance or travel time can be estimated and compared. Further, comparative time and distance estimation for multiple start and designation points can provide cost-effective outcomes for decision making process.

Fig-6: OD Cost-Matrix estimation using network data model

Image source: ESRI

 

Other than above mentioned application of network topology, many new creative application based on the user’s intelligence can be created. Modeling and analysis of river streams, flood zone estimation, natural fault line and its impact, gas and sever pipeline are many relevant domain which are using network topology for effective management of services to the society.

 

5. Summary

 

All the events, surface features like water, rocks, vegetation, and processes are spatially located on earth. GIS environment provides a number of conceptual models which provides near real problem solving capacity to the decision makers. Network topology is one of the GIS environment capability which enables to solve, search, estimate many network based problems. Geo-relational network data model is widely accepted network data model which have flexible, user friendly spatial and attribute based query making problems to be easily solved. A number of application such as finding routes, closet facility, service area estimation and OD Cost-Matrix are among the many options provided my network analysis methods. Natural geological fault line, stream line analysis, flood zone mapping are other applications of network data model.

 

In summary, we can say that network analysis have strengthen the GIS user communities to offer more society based services which more user friendly option and space to grow in many creative way particularly by evolving network data models.

 

6.References

  • © 2005 Nigel Trodd
  • Goodchild, M. F. (1998) Geographic information systems and disaggregate transportation modeling Geographical Systems, 5, 19-44.
  • Manfred M. Fischer (2003); GIS and Network analysis, Handbook 5 Transport Geography and Spatial Systems Pergamon.
  • P.L.N. Raju, Spatial data analysis, Geoinformatics Division, Indian Institute of Remote Sensing, Dehra Dun, Satellite Remote Sensing and GIS Applications in Agricultural Meteorology pp. 151-174