chapter – 2nptel.ac.in/courses/105108073/module1/lecture2.pdf · course title: geo-informatics in...

Chapter-2: Land Use and Transportation Data for GIS NPTEL Web Course (12th Aug. 2011)

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Course Title: Geo-informatics in Transportation Engineering Course Co-ordinator: Dr. Ashish Verma, IISc Bangalore

Chapter – 2

Land Use and Transportation Data for GIS

Key words: Spatial data for land use, point, line and polygon features

2.1 Significance of Data in GIS

A GIS is no more than a paper map, if not supported by a strong data set. It is the firm backup

of spatial and attributes data, attached to GIS, which gives the capability to manipulate,

analyze, and produce outcomes in a way desired by the user. As mentioned in Section-

1.4.2.1, GIS developers are often encouraged to adopt a data-centered approach, which is

collected and compiled by a person or organization to support the goal of the user. The other

components of GIS are to provide support needed to process that data.

The spatial data gives unique location identification to each object and also every event on

the GIS map, and this sets it apart from any ordinary Data Base Management System

(DBMS). For example; a DBMS references accidents by some unique index or combination

of indexes, such as the date of occurrence, the vehicle make, or the weather conditions. In

contrast, information is all about a geographic description of the surface of the earth in a GIS.

Each accident record is a geographic event in the sense that it is tied to a unique location

defined in a given referencing framework (global, national or local datum). With the spatial

data of the objects, topology of the data can be defined, which in turn enables a host of spatial

query operations of objects and set of objects. For instance, the task of identifying all

accidents that occurred within 100 meters of any intersection on urban arterials will require

little effort because of the spatial indexing of all accidents and roadway link objects in the

GIS databases.

The attributes refer to the properties of spatial entities. They are often referred to as non-

spatial data since they do not in themselves represent location information. The attribute data,

along with a Relational Data Base Management System (RDBMS) gives GIS the capability

to perform tasks, which are unique to it like, overlay analysis, proximity analysis, buffering

of layers, etc. It also enables GIS to carry out shortest path analysis, and other transportation

modeling task in a user interactive manner and with a strong graphical interface. For

example; the buffering and overlay analysis can be used to find out the total population or

household (the attribute data attached to TAZ) living within say 1 kilometer radius around a

rail transit station, and which can help in judging the amount of passengers expected at a


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transit station. The rest of the chapter will discuss different types of spatial and attribute data

required for land use – transportation studies in GIS, the various methods of collecting the

same, and the issues involved with data collection.

2.2 Spatial Data for Land use – Transportation Analysis

As discussed earlier in section – 1.4.5.2, there are two types of data models in GIS viz. raster

and vector models. Out of these, vector model is mostly used for transportation applications

because the three features of vector model (point, line and polygon) are able to best describe

the location and characteristics of transportation system and are also able to address most of

the application specific needs. According to Michael F. Goodchild, who is Professor of

Geography at the University of California, Santa Barbara; three classes of GIS models can be

identified in the context of transportation:-

Field models or representation of the continuous variation of a phenomenon over space.

Terrain elevation uses this model.

Discrete models, according to which discrete entities (points, lines, or polygons) populate

space. Highway rest areas, toll barriers, urbanized areas may use this model.

Network models to represent topologically connected linear entities (such as roads, rail

lines, or airlines) that are fixed in the continuous reference surface.

While all three models may be useful in transportation, the network model built around the

concepts of arc and node plays the most prominent role in this application domain because

single and multi – modal infrastructure networks are vital in enabling and supporting

passenger and freight movement. In fact, many transportation applications only require a

network model to represent data. Examples of such applications include:

Pavement and other facility management systems;

Real-time and off-line routing procedures, including emergency vehicle dispatching and

traffic assignment in the four-step urban transportation planning process

Web-based traffic information systems and trip planning engines;

In-vehicle navigation systems;

Real-time congestion management and accident detection.

The following paragraphs describe the point, line, and polygon features in detail.


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2.2.1 Point Feature

For a transportation system a point feature may correspond to; traffic intersection, bus stop,

rail transit station, airport terminal, centroid of a Traffic Analysis Zone (TAZ) etc. Depending

on the size of the study area, some of these transportation features can be treated as polygons

also, for example; an airport terminal may be treated as a point feature if the study area is a

state, country or an international region, whereas, the same can be treated as a polygon if the

study area is limited to a city or part of a city. Generally, if the study area is at the city level,

features like; traffic intersection, bus stop, urban rail transit station, TAZ, centroid etc. are

treated as point features, and their spatial details are collected accordingly. As mentioned in

the section – 1.4.5.1, digitization converts all features on a map to points, lines, or polygons.

Each point is stored by its location (X, Y) together with the table attribute of this point.

Figure – 2.1 shows 4 points with their coordinate location in (X, Y).

Figure 2.1: Point Feature

Depending on the study requirement, the coordinates (X, Y) for a point feature, can be

recorded in terms of actual latitude and longitude or in terms of any local datum or any other

relative reference point. Figure – 2.2 shows a node (point) layer in GIS showing road

intersections and zone centroids for Thane city with corresponding spatial and attribute data.


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Figure 2.2: Node layer in Trans-CAD

2.2.2 Line Feature

A line feature in GIS may correspond to transportation features like; a road link between two

intersections, a rail link between two stations, an air link between two airports, a water

transport link between two terminals, or a bus route link between two bus stops. The line

feature is also used to represent transportation features such as; access/egress link to/from a

terminal, centroid connectors to link TAZ centroid to the nearest node for the purpose of

assigning an Origin-Destination (O-D) matrix etc. Each line is stored by the sequence of first

and last point together with the associated table attribute of this line. Figure-2.3 shows three

lines (a, b and c) have their first and last node to distinguish their location. Here, each node

has coordinate (X, Y) that is stored in another table. Because the first and end node

coordinates of each line is known, the length of a line or poly-line (sequence of lines) can be

easily computed.


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Figure 2.3: Line Feature

In most GIS software nowadays, you can use arc instead of line (Figure-2.3). These arcs have

endpoints, but they are also assigned a direction indicated by the arrowheads. The starting

point of the vector is referred to as the "from node" and the destination the "to node." The

orientation of a given vector can be assigned in either direction, as long as this direction is

recorded and stored in the database. By keeping track of the orientation of arcs, it is possible

to use this information to establish routes from node to node or place to place. Thus, if one

wants to move from node 2 to node 1, we can locate the necessary connections in the

database. Figure – 2.4 shows a road (line) layer in GIS showing roads between intersections

and centroid connectors for Thane city with corresponding spatial and attribute data.

Figure 2.4: Road network representation in Trans-CAD


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2.2.3 Polygon Feature

Polygon feature are those whose boundary encloses a homogeneous area represented by a

closed string of co-ordinates which encompass an area (Figure-2.5). An area fully

encompassed by a series of connected lines. Because lines have direction, the system can

determine the area that falls within the lines comprising the polygon. Polygons are often an

irregular shape; an area bounded by a closed line. For example an airport may be described as

a point in large scale maps while on smaller scale maps a point would totally misinterpret the

size and location of the airport. Polygon features are used in service planning as well as trip

planning. TAZ used for transport planning are typically digitized as polygons in GIS. Figure-

2.6 shows an example of TAZ (polygon) layer for Greater Mumbai in GIS showing total

employment in each traffic analysis zone (TAZ).

Figure 2.5: Polygon Feature

2.3 Non- Spatial Data for Land use – Transportation Analysis

2.3.1 Data Collection

The data in relation to transport and its statistics is a primary source for developing any

transport strategies, comprehensive mobility plans, traffic management measures etc.

Insufficient traffic data makes it difficult to evaluate the implementation of transportation

policy and to make decision through qualified data. An additional purpose of collecting data


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is to calibrate the models, to identify more precisely problems, problem domains, objectives

and constraints.

Figure 2.6: Employment data representation in GIS

There are two type of data collection:

Primary Data – Data collected through actual field surveys i.e. through primary sources. It

includes the following

– Inventories of objects and their characteristics derived from observation, e.g., the

location and properties of a road link (starting node, end node, length, number of

lanes, presence of parking, surface quality, gradient, etc.), the route and

characteristics of a bus service (operating hours, stops served, headways, type of

vehicle, etc.) and the services offered by a location (number and type of activities

possible, size of facilities, opening hours, price level, service quality).

– Data on current behavior obtained from observation, in particular counts, or from

traveler surveys, frequently referred to as revealed preference data.

– Data on traveler attitudes and values provided by the travelers through responses in

surveys.


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– Decisions and assessments in hypothetical situations or markets obtained from the

users through surveys, frequently referred to in transport as stated preference data.

Secondary Data – Data collected through indirect (secondary sources). Such as,

– Census of persons or firms, detailing their characteristics obtained from primary

surveys of the persons or firms concerned or derived from other primary sources, such

as population registers and decennial censuses.

– Population, land use, vehicle ownership, employment data, economic data,

establishment data etc.

2.3.2 Transport Surveys

Surveying or land surveying is the technique and science of accurately determining the

terrestrial or three-dimensional space position of points and the distances and angles between

them. It is also a detailed study or inspection, as by gathering information through

observations, measurements in the field, questionnaires, or research of legal instruments, and

data analysis in the support of planning, designing, and establishing of property boundaries.

Survey scope in transportation lies in identifying the total number of trips occurring in a

study area which may be broadly divided in to four classes. These are trips with origin and

destination inside study area (internal movements), trips with origin inside study area and

destination outside study area (internal-external movement), trips with origin outside study

area and destination inside study area (external- internal movements) and trips with neither

origin nor destination inside study area (through movements). Surveys done for identifying

the above mentioned movements are generally called Origin- Destination surveys. This

section will only give a brief overview of some of these surveys; however for more details

any standard book on traffic engineering/transport planning may be referred.

O-D Surveys

The O-D survey is an initiative to collect current and reliable information about trip patterns

and travel choices of residents which is again classified into the following:-

1. Home Interview Survey for Internal Movements and internal-external movements.

2. Road Side O-D Surveys for through movement, external-Internal movements and also

internal-External movement. These are also called non-house hold surveys.


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Home Interview Survey

In a home interview survey, there are three broad items under which data is collected.

– Household Information: It includes household size, dwelling unit type, vehicle

ownership, family income, etc.

– Person Information: It consists of relation to head of household, age, sex, occupation,

income, possession of driving license, etc.

– Trip Information: The residents are generally asked to describe their movements on

the previous day (A typical working day).

Non house-hold surveys

Roadside surveys:

There is a need to gain the equivalent of on board data from private vehicles at strategic

locations on the roadway. Most often, these surveys involve using the police to stop vehicles

at a survey station, where the driver is interviewed briefly and then allowed to proceed. This

may also be conducted by handing drivers a postcard to be completed and mailed back.

Typically road side surveys are done at inner and outer Cordon lines.

Various methods of road side O-D surveys are

– Road Side Interview Method

– Post Card Questionnaire Method

– Registration Number Plate Survey

– Tag On Vehicle Method

Figure-2.7 shows a typical O-D trip matrix obtained from a O-D Survey.

Figure-2.7: A typical O-D trip matrix obtained from O-D survey


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The following are other important transport surveys that are carried out for data collection.

Traffic volume surveys

These are non-participatory surveys that provide part of the demand-side data required by the

transport planner. They are usually conducted by using automated traffic counters, but can

also be conducted using human surveyors, or video cameras. Automated traffic counters may

use pneumatic tubes laid across the road surface, or may use some type of electronic or

magnetic loop detection system. In either case, the principle is the same and involves

counting the number of axle crossings and the time between axle crossings. The vehicles

being surveyed simply drive over the device without any change in behavior because of the

presence of the counting device. Similarly if human surveyors are used, they will take up

station at an appropriate location where they can see the point at which the count is to be

made and will record on paper, voice-recorder, hand-held computer, or manual counters, the

passing of vehicles, possibly by classification or specific behavior. Traffic counting surveys

are able to determine volumes at a point, vehicle mix, and speed of movement. Both volumes

and speeds are often used to check accuracy of the travel-forecasting models developed from

data such as those obtained in a household travel survey.

Network inventory

This is also a non-participatory survey that provides the bulk data on the supply side. It may

require actual measurement in the field, or may be performed principally from maps, and

other records maintained by the operating entities for the transport systems. For example, the

number of lanes of roadway, restrictions on use, traffic-signal timings, design speed, and

other determinants of capacity of the roadway system may exist in the highway engineering

offices of various government jurisdictions.

Land-use inventory

Another important inventory concerns the land uses, because these determine the amount of

travel taking place on the urban transport system. Unlike the situation with the networks, the

land uses are not usually maintained in comprehensive files by any jurisdiction. The

inventory cannot be performed with a sample survey, because it is not appropriate or relevant

to expand a sample to full urban region. Figure-2.8 shows land use data for Mumbai

Metropolitan Region (MMR).


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Figure-2.8: Land use data for Mumbai Metropolitan Region (MMR)

On-board survey


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A variety of situations exist in which the only satisfactory way to find a sufficient sample of

people using specific means of transport is to survey them while they are traveling, i.e., on

board the vehicle. Such surveys are mainly participatory, although there are some non-

participatory surveys that may be conducted on board. Participatory surveys generally

involve having surveyors on board the vehicle who either interview passengers as they ride,

or hand out survey forms to be filled out on the vehicle or later. The non-participatory survey

includes, vehicle occupancy count, boarding/alighting count etc.

Parking survey

This survey is done to understand the parking demand and various parking characteristics like

parking space available, type of parking provided etc. in a region/area.

Transport terminal user survey

A variety of characteristics of the travelers using the terminals either in a bus transport

system or in a sub-urban commuter rail system could be collected using this survey. Here the

survey forms are distributed to travelers in a terminal and information is collected or they are

interviewed.

Speed and delay survey

In this survey a person sitting inside a running vehicle will be collecting information about

the various delays occurring to that vehicle due to congestion, signals and also at bus stops.

Automated instruments are also used now-a-days for this purpose.

2.3.3 Sampling

All the surveys discussed above require a careful sampling strategy. Sampling is the process

of selecting units (e.g., people, households, organizations) from a population of interest so

that by studying the sample we may fairly generalize our results back to the population from

which they were chosen.

The sampling methods are as follows:

1. Random sampling: This is the simplest method of sampling and involves selecting a

random sample from a population, using a sampling frame with the units numbered.


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Using a suitable random number, source numbers are selected at random and the

members of the population are chosen to form the sample.

2. Stratified sampling: In this method, based on the prior information, the

population/households are divided into homogeneous groups or strata, based on some

measure or combination of measures that can be used to group subjects. The grouping

should result in those subjects in a group being similar to one another in relation to

measures of interest to the survey, while the groups are dissimilar to one another. For

example, assuming that car ownership affects trip making and that a survey of trip-

making is to be performed; grouping the population according to whether a household has

no cars, one car, two cars, or more than two cars should result in creating groups such that

households in a car ownership group have more similar trip-making characteristics than

the households in other groups.

3. Cluster sampling: This is a non-random-sampling method that is often adopted in face-to-

face household surveys, because it offers a potential of considerable cost savings for this

type of survey. In cluster sampling, the units of interest in the survey are aggregated into

clusters representing some type of proximity that affects survey economics. For example,

if the survey is of households, households may be aggregated into blocks, where a block

is defined by all the houses in a spatial area that is bounded by streets, but not cut by a

street.

4. Systematic sampling: This is a non-random sampling method that is particularly

important for roadside interviews and for sampling from very lengthy lists. It involves

selecting each nth

entry from a list or each nth

unit from a passing stream of units.

Selecting the first unit at random is quite a useful idea, but does not result in making the

systematic sample random.

5. Choice-based sampling: This sampling is not strictly random. It applies to any form of

sampling where those who are sampled are sampled because they have already made a

choice of relevance to the study. The sample may be drawn from within this subset of the

total population using any of the preceding methods. For example any type of on-board

transit survey, a roadside survey, and an intercept survey.


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2.4 Traffic Analysis Zone (TAZ), Screen Lines and Cordon Lines

2.4.1 Traffic Analysis Zone (TAZ)

TAZs are geographic units representing sizable portions of the region, which impact, or in

some cases are predicted to impact, the transportation and transit networks (Figure-2.9). For

this reason, TAZs in more heavily developed areas and rapid growth areas tend to be smaller

than those in outlying zones. Ideally, TAZs have distinct geographic boundaries with

relatively few access points to the region’s overall transportation network. Ideal boundaries

often include limited access highways, railroad lines, water boundaries and ridgelines.

Because the impact of different types of trips (e.g. home to work, home to shopping, etc.)

may be assessed, TAZs should be of fairly homogeneous land use. Of course, no urban area

follows these ideal criteria. Therefore, a good deal of judgment is involved in determining

appropriate TAZ boundaries. Two additional principles should be observed in delineated

TAZ boundaries. First, TAZ boundaries should coincide with jurisdictional boundaries.

Second, in order to compare previously developed Transportation Planning Data, adjusting

TAZ boundaries should be avoided, if possible. This does not preclude the subdivision of

existing zones, a natural process of individual zone urbanization.

The following points should be kept in mind while dividing the area into zones:-

Land-use is the most important factor in establishing zones for a transportation survey. It

is only when the origin and destination zones reflect properly the land-use can traffic

generated within the zones be predicted, measured and quantified accurately.

The zones should have homogeneous land-use so as to reflect accurately the associated

trip-making behavior.

Anticipated changes in land-use should be considered when sub-dividing the study area

into zones.

It would be advantageous if the sub division follows closely that adopted by other bodies

for data collection. This will facilitate correlation of data.

The zones should not be too large to cause considerable errors in data. At the same time

they should not be too small either, to cause difficulty in handling and analyzing the data.

The zones should preferably have regular geometric form for easily determining the

centroid which represents the origin and termination of travel.


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Sectors should represent the catchment of trips generated on a primary route.

Zones must be compatible with screen lines and cordon lines.

Natural or physical barriers such as canals, rivers etc. can form convenient zone

boundaries.

Figure 2.9: GIS map for Mumbai showing TAZ

2.4.2 Screen Lines

For the purpose of O-D matrix validation, the study area is divided into large sections by

running imaginary lines; know as screen lines, across it (Figure-2.10). In some cases natural

and manmade barriers such as river or railway track is used as screen lines. Traffic counts are

then taken at each point where a road/rail crosses the screen line. It is usual for screen lines to

be designed or chosen such that they are not crossed more than once by the same street.

Screen lines running through the study area are established to check the accuracy of data

collected from home interview surveys. The collection of data at these screen lines stations at

regular intervals facilitates the detection of variations in the traffic volume and traffic flow

direction due to changes in the land-use pattern of the area.


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2.4.3 Cordon Lines

Cordon lines are the imaginary lines constructed to define the boundary of the study area for

any transportation study purpose (Figure-2.10). The following are the salient features of

cordon line:-

It should include those areas which are related in terms of the average daily travel

forming a commuting catchment of the CBD.

It should also include those areas that are expected to become part of the “commuter

catchment”.

It should be located where the number of transport routes crossing the cordon can be

minimised.

The study area boundary should conform with the boundaries already established for

administrative purpose.

2.5 Network and Routes

A network is referred to as a pure network if only its topology and connectivity are

considered. If a network is characterized by its topology and flow characteristics (such as

capacity constraints, path choice, and link cost functions) it is referred to as a flow network.

A transportation network is a flow network representing the movement of people, vehicles, or

goods (Figure-2.11). A network is defined as a set of nodes and links. Nodes are locations

where flows start, end, or branch. Links are the conduits that carry flow from node to node.

Networks are used to analyze the way people and goods flow from one location to another. A

network is a mathematical graph that is an abstract representation of the transportation

system. Network data structures are optimized for efficient storage and rapid processing of

network algorithms such as finding the best path, identifying a minimum cost shipment

pattern, or performing a traffic assignment. The transportation network is deemed to represent

the supply side of the modeling effort, i.e. what the transport system offers to satisfy the

movement needs of trip makers in the study area. Every node and link in a network has an ID

and, optionally, any number of attributes. A line layer in a map consists of many line features,

each of which begins and ends at a point called an endpoint, and each of which is defined by


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a set of coordinates. A link in a network, on the other hand, always represents flow in a single

direction.

Figure 2.10: Cordon Line and Screen Lines

A route is a series of line segments that has at least one common attribute, such as a route

number, a service frequency, or its own attribute such as route distance, time, or cost. A route

system is defined as a series of one or more line features. The route system is one map layer

Cordon Line

Screen Line 6

Screen Line 5

Screen Line 4

Screen Line 3

Screen Line 2

Screen Line 1


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and the line features are stored in another layer in the map, for example: A designated

highway route composed of a series of highway line features. When defining a route system,

each line feature that is part of a route is referred to as a segment. Every route is made up of a

series of segments. Several routes in a route system may operate on the same segment. A

transportation route is a regular path that is followed by a movement of people or goods.

Ideally it shall follow the shortest possible distance.

Figure 2.11: Transportation network data for Mumbai in GIS

Exercise

1. Explain a point feature and a line feature in reference to transportation network in

GIS?

2. What is a polygon feature in GIS? Give examples related to transportation?

3. Explain the different types of typical surveys carried out for collecting data required

for transportation database in GIS?

4. Explain various sampling strategies for transportation data collection? Also explain

why sampling is done?

5. Define Traffic Analysis Zones (TAZs), screen lines and cordon lines?

Assignments

1. Create point, line and polygon features using any GIS software.

chapter – 2nptel.ac.in/courses/105108073/module1/lecture2.pdf · course title: geo-informatics in...

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