Spatial Data Structure of Taxi Parks in Lagos, Nigeria. Lab Reportnew

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Group D Svy818 Lab Spatial data Structure 2010/11Surv. & Geoinf. Eng, Unilag Lagos Nigeria.

Group D

SVY 818 LAB (Advance Spatial Data Structure)

Create a Spatial Data Structure of Taxi parks in Ikeja and Ifako-Ijaye Local

government areas of Lagos State, Nigeria.

2010/11 Session

By:

Falebita, Michael A. -030405013 (MSc. Surv. & Geoinf. Engineering) TelNo.:08053266713

Ipaye, Temitope George -109045009 (MGIT. Surv. & Geoinf. Engineering)

Durodola, John Adeoye -109045005 (PGD. Surv. &Geoinf. Engineering)

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Table of Contents

1.0 Introduction ...................................................................................................................................1

1.1 PROBLEM SET ................................................................................................................................................2

1.2 OBJECTIVES ...................................................................................................................................................3

1.3 STUDYAREA ..................................................................................................................................................3

2.0 Methodology: Principles Behind .................................................................................................5

2.1 DATABASE DESIGN .........................................................................................................................................5

2.2 CONCEPTUAL DESIGN .....................................................................................................................................5

2.3 IMPLEMENTATION STRATEGY ............................................................................................................................7

2.4 DATA ACQUISITION ........................................................................................................................................7

2.5 SPATIAL DATA ..............................................................................................................................................7

2.6 ATTRIBUTE DATA .................................................................................................................................8

2.7 DEVELOPMENTOFA LOGICAL DATA MODEL .....................................................................................................8

2.8 KEYBOARD ENTRY .........................................................................................................................................8

2.9 SCANNING .....................................................................................................................................................9

2.10 COORDINATE SYSTEM ...................................................................................................................................9

2.11 SOFTWAREAND TOOLS ...............................................................................................................................10

2.12 AUTOCAD ...............................................................................................................................................10

2.13 ARCGIS 9.3 ............................................................................................................................................10

3.0 Data Processing and Analysis ....................................................................................................11

3.1 GEODATABASECREATION ...............................................................................................................................11

3.2 GEOREFERENCING .........................................................................................................................................13

3.3 VECTORIZING ...............................................................................................................................................14

3.4 TOPOLOGYANALYSIS ....................................................................................................................................163.5 NETWORKDATASETANALYSIS ........................................................................................................................18

4.0 Results and Conclusion ..............................................................................................................20

4.1 RESULTS ......................................................................................................................................................20

4.2 TOPOLOGICALQUERIES ...................................................................................................................................23

4.3 NETWORKQUERIES ........................................................................................................................................24

4.4 CONCLUSION ................................................................................................................................................31

5.0 References ....................................................................................................................................33

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1.0 Introduction

Nowadays Geographic information systems (GIS) are widely used in different applications. GIS has

been confirmed to be a very efficient tool for processing and analyzing real life spatially distributed

linear and positional objects such as railways, roads, pipelines, hospitals and also taxi parks to

mention but a few. It is related to an evaluation in detail of physiographic factor, landscape,

engineering-geological and others requirements for the investigation area. It includes the study of all

relevant parameter such as; length of the route, calculation of intersections with local government

boundary lines, roads and existing railways.

An existing GIS spatial analysis capability gives possibility of operative evaluation. Modern GIS

software allow to automate complex operations such as intersection with different linear and

polygonal objects, positioning of new taxi parks, estimation of transport costs during construction

and operational service of taxi route, calculation of integrated construction cost etc.

A lot of studies around the world have focused on the issues of digitizing geospatial and acquiring

attribute information about our environment since the US Vice president Algore mention the concept

Digital earth. (Y.C Ding, C.C Hung Beijing 2008.)

The major components of Digital Earth are:

i. Collection of all kinds of information from different sources, especial from satellites, to build

geodatabase.

ii. Storing and retrieving this data efficiently.

iii. Sharing this information efficiently via the internet infrastructure and

iv. Presenting data in 2D and 3D formats.

Various GIS applications have been developed to solve real-life problems examples of which are:

Land information system, Geo resources Information system, and Route Network information

system to mention a few.

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1.1 Problem Set

In this lab, one was asked to create the spatial data structure of taxi parks in Ikeja and ifako-Ijaye

Local Govt Areas of Lagos State.

Given this problem, the major challenges are as follows:

- Acquiring GPS positional data of taxi parks in the study area.

- Road dataset digitization

- Local Government boundary determination.

- Land use dataset creation

- Acquisition of taxi park photographs.

The road dataset is an example of Linear Engineering Structure (LES). Because of the need of route

classification and selection, linear engineering structures require strategic planning, evaluation and

management. The operations to choose optimum route depends for instance on the effective

collection, processing, storing and analysis of spatial data such as topography, vegetation, geology,

soil type, land use, available facilities, existing road and landslide areas in the study area. This

situation requires using Geographical Information Systems (GIS) to providing effective data

management. In this case the satellite image from google map will be downloaded and visible

spatial information will be extracted and structured into a geodatabase.

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1.2 Objectives

The objectives of this lab include:

i. To download and georeference raster images of the study area.ii. To acquire GPS observations of taxi parks in the study area.

iii. To digitize and populate taxi park, route and local government dataset of the study area.

iv. And finally to create spatial data structure of the participating datasets.

1.3 Study area

The study area for this lab is Ikeja and Ifako-Ijaye Local Government Area of Lagos State, Nigeria.

The two Local Governments are located in the Northern part of Lagos state. It covers anapproximate area of 12.5 square kilometers. They are both densely populated local government areas

of Lagos state. Moreover, Ikeja local government is the state capital of Lagos. Ifako-Ijaye borders

Ikeja to the north.

The vector base map of the study area will be acquired and studied carefully to determine visible

points, line and polygonal features. Non-spatial attributes of these features will be compiled into the

geodatabase.

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Fig. 1. Vector map of the study area.

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2.0 Methodology: Principles Behind

A GIS can be said to be a software package with components and tools used to enter, manipulate,

analyze, visualize and present spatial data (Kufoniyi 1998). The components as earlier mentioned

consist of the Hardware, Software, Spatial data, Attribute data, management and analysis procedure

and the people to operate the GIS.

The performance distribution of GIS is subject to a list of data requirements. The sources of data

used in the GIS came from several different sources such as:

Field Survey: This involved going to the various locations of taxi parks in the study area to

capture their coordinates with a hand held GPS.

Attribute data: which involves going to these parks to collect other relevant non-spatial data.

2.1 Database Design

The major effort in developing a GIS application package is the establishing of a spatial database.

The database design in this case consists of the conceptual design and logical data model:

Conceptual Design

This refers to the human conceptualization of reality; where REALITY refers to the phenomenon, as

it actually exists including all aspects, which may or may not be conceived by the individual. The

VIEW OF REALITY is the mental abstraction for a particular application or group of applications

(Kufoniyi 1998). The entities and attributes which constitute the conceptual design in this case are:

Road Network

Taxi Parks locations

Local Government Boundaries of the study area

And other attribute data

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1

N N

N 1

1

Fig. 2.0 Conceptual model of Taxi parks created in the geodatabase.

2.2 Implementation Strategy

The methodology of this project can be divided into two parts: Data collection and Data analysis.

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2.3 Data Acquisition

The street map data was extracted from Lagos street map available on Map Source standalone

application and from existing Lagos acquisition map (Source: Lagos State Surveyor general office).

GPS coordinates of the taxi parks were downloaded from GPS 76CS device onto mapsource

application.

In the course of this project, two types of data were acquired namely; Spatial and Attribute data.

2.4 Spatial Data

Spatial data stores information about location, shape, and attributes of real life objects. These data

includes;

Taxi parks directory of Lagos state.

Lagos street map

Lagos Land Use

Taxi Point data of the study area.

This was done with the aid of GPS Garmin 76CS.

Maps as an origin of information have two types of functions:

i. Positional, i.e. give information about the exact location of objects

ii. Informational, i.e. give information about data type, name and class of objects including

topological properties and relationship of objects

2.5 ATTRIBUTE DATA

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Attribute is the characteristic of an entity selected for representation (DCDSTF 1988) usually non-

spatial but related to spatial character or phenomena under study. Attribute value is the value of the

attribute that has been measured and stored in the database.

A geodatabase was created to hold feature datasets and non-spatial data. All geographic objects

have attributes. Attributes of geographical objects have been collected at the same time as the vector

geometry, e.g.:

i. Name of the taxi parks

ii. Addresses of these parks

iii. Contact numbers and

iv. Photographic data to graphically identify the parks.

These attributes have been manually entered into the geodatabase.

Development of a Logical Data Model

After the data needs assessment had been completed, the physical design of the data began. The data

that had been deemed necessary were described and grouped by function or type. This led to creation

of themes in much the same way the geographic layers were grouped into themes. These themes

were arranged into an Analysis Diagram that exposes the relationships between features and objects.

Keyboard Entry

The keyboard entry often referred to as key coding, is the entry of data into a file at a computer

terminal using the keyboard as an input device. This technique was used for the attribute data that

are on paper e.g. statistical data of Lagos state.

Scanning

Scanning is the most commonly used method of automatic digitizing. It is an appropriate method of

encoding when raster data are required. Although street data were not scanned for digitizing,

photographic data were scanned and saved on disk for onward attachment to spatial data.

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The AutoCAD2006 produced by AutoDesk based in the USA was used in this work. The package

was used to vectorise some scanned paper maps. It was also used to extract point data from DXF file

format and finally used to compile all data before converting to shape file format.

2.9ArcGIS 9.3

ArcGIS software was deployed in designing and implementing this project. ArcGIS desktop is well

known in the world and its' the most used GIS software. It was developed by Environmental System

Research Institute Inc. (ESRI), Redlands, USA. In this work the components of ArcGIS desktop like

ArcMap, ArcCatalog, and ArcToolbox have been deployed extensively to create the geodatabase, for

editing, for data management and storage, georeferencing data from different sources, performing

spatial analysis and visualization of output data, implementation of geo-processing functions for

different tasks. One of the ArcGIS extension that was used is the Network Analyst. These extensions

have been strategically deployed to solve certain problems during and after implementation.

The spatial features contained in the geodatabase comprise feature datasets, feature class, geometric

network data, network dataset and table. The feature class identified are:

1. Point

2. Line and

3. Polygonal features

3.0 Data Processing and Analysis

3.1 Geodatabase creation.

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The features identified were used to create a geodatabase to enforce topological and geometric rules

on the resulting feature classes. The ArcGIS 9.3 ArcCatalog was used to create a geodatabase

created in C:\Spatial Data Structure\SpatialDS.mdb and populated with a Dataset feature dataset

having point, line and polygon feature classes in GCS_WGS_1984 coordinate system. Subtypes

were also created for these feature classes. Below is a table showing the feature classes, subtypes,

fields and other attributes.

Features

Class

Layers Field

Point Taxi Parks ObjectID, Shape, TName, Easting, Northings,

Address, Contact No., Picture

Line Ikeja Road ObjectID, Shape, Type, RD_NAME, RD_EXT,

ROUTE, FW, SERVICE, RD_ID, SPEED, ID,

DISP_TYPE, RD_NAME_F, RD_NAME_U

Shape_Lenght,

Ifako-Ijaye Road

Polygon Local Government ObjectID, Shape, ID, Name, Capital, State,

Zone_, Area, Population, Shape_Lenght,

Shape_Area

Land Use ObjectID, Shape, ID, Landuse, Count, Shape-

_Lenght, Shape_Area

Table 3.0 Showing the contents of the geodatabase, feature dataset, classes, Layers and fields

created.

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Fig 3.0 Showing the created geodatabase, feature dataset, feature class and geometric network

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Fig 3.1 Showing Network dataset parameters

3.2 Georeferencing

After creating the necessary feature classes, the feature dataset were added to an ArcGIS ArcMap

file. The feature classes and geometric network feature was classified by their subtypes

automatically to facilitate easy vector classification.

The scanned images and satellite images were added to the map file and images georeferenced in

GCS_WGS_1984 coordinate system with the identified control points on each image.

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3.3 Vectorizing

This simply means conversion of raster image features into digital format using the Point, line and

polygonal feature classes. ArcGIS ArcMap provides the necessary tool to digitize features easily and

accurately. The subtype classifications help to fast-track this process. Before digitizing, selectable

layers were enabled so as to select only one feature at a time. The snapping tolerance had been

specified during the feature dataset creation, all that remained was to indicate which layer(s) is to be

snapped to enforce network topology on the system.

After setting and activating all the necessary parameters, digitizing commenced with point feature

positioning i.e taxi parks, street map and data entry. The road and polygonal features were also

digitized.

Fig 3.1 Showing digitizing snapping parameters of the feature classes in ArcMap

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Fig 3.2 Showing the processes involved in digitized feature classes and editing attributes on

georeferenced vector maps.

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3.4 Topology analysis.

Topology network was built for the network involving point, line and polygonal features. Some of

the rules enforced include:

i. No polygon must overlap another polygon

ii. Points must be covered by end point of road features

iii. Road feature must not overlap it self

Fig 3.3 Showing topological rule enforce on the taxi parks and road network

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Fig 3.4 Showing the network after topology analyses were carried out on the network.

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3.5 Network dataset analysis

As mention earlier, topological capability has been built into the network when creating the feature

classes by enforcing geometric network rules. To test this capability, one will need to build network

dataset on the system.

The coordinate system would have been projected in the UTM system if it were not in UTM, but this

important step was not done because the system was already in the UTM system. This is important

to facilitate easy calculation in meters and kilometers. The network dataset was created from the

point and road feature classes.

The network dataset was built and validated after setting appropriate parameters and evaluators such

as snap tolerance, complex edge, connectivity and direction.

The ArcMap environment was deployed for the last time to add the newly created network dataset

and to perform network queries on the system.

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Fig 3.5 Showing the network after topology analyses were carried out on the network.

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4.0 Results and Conclusion

4.1 Results

Many spatial and non-spatial queries were carried out on the system. Network-based queries were

also carried out. Some of the queries executed include attribute queries. Examples of these queries

are shown below.

The attribute table of the Taxi Parks were also populated and shown below:

Fig 4.0 Showing the attribute table of the Taxi Parks including its coordinates, Park name, address,

contact detail and picture of the parks.

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Fig 4.2 Showing a spatial query to search for and locating Ojota International 2 Taxi Park.

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4.2 Topological queries

Queries were carried out to test and validate the accuracy of the network. These queries include,

displaying the coordinates of individual vertices that makes up an edge in a route network. Query

showing relationships among participating junctions and its adjoining edges was also shown.

Fig 4.3 Showing participating egdes and nodes on a section of commercial Road.

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4.3 Network queries

As described earlier, network analyses were carried out on the system with emphasis on shortest

route between taxi parks and stops in the study area. Some of these queries were shown below:

Fig 4.4 Showing the shortest route between Oluwole Taxi Park through agidingbi rd to ojota retail

market taxi parks.

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Fig 4.5 Showing the shortest route from Daddy salvage Taxi Park to Oluwole Estate Junction close

to Oluwole taxi park.

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Fig 4.6 Showing the shortest route from Central Mosque Iju Ishaga Taxi Park to Oluwo Estate Taxi

Park.

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Taxi Park pictures hyperlinking were carried out to further describe and identify the parks

graphically. Some of these were shown below:

Fig 4.7 Showing picture of the motor way central taxi parks near the old toll gate area.

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Fig 4.8 Showing picture of Abule taxi parks in GRA area of Ikeja.

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Fig 4.9 Showing picture of Ojodu retail market taxi parks in ojodu berger area.

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Fig 4.10 Showing picture of Ogba oluwole taxi parks near the ogba area.

Fig 4.11 Showing picture of another taxi park in Ogba area.

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4.4 Conclusion

In this work, GIS capabilities have been used to create a spatial data structure of taxi parks located in

Ikeja and Ifako-Ijaye local government areas of Lagos state, Nigeria. The study attempts to resolve

some of the challenges faced in locating existing taxi parks and creating new taxi parks in the stated

study areas and describes in detail the implementation and capabilities of GIS for managing routing

to and from the taxi parks.

The existing taxi parks in the local government areas have been shown and managed with ArcGIS

9.3 (by: ESRI). The study has successfully demonstrated the effectiveness of Geographic

Information System for creating and managing taxi parks spatial data structure. The technology

adopted will enable easy data capturing, processing, updating and visualization and expansion of the

data structure to other local government area so long the WGS_UTM 84 coordinate system was

used.

Graphic data was also captured and attached to the appropriate taxi parks for clear description and

identification.

The built in topology had made it possible to build network analysis query on the system and

described interactions between these connecting nodes and edges. Stops were created to mimic route

navigation from one location to the other within the study area.

Some of the challenges encountered include inaccurate Taxi Park-local government classification as

some of these parks do not fall in the stated local government areas, software and hardware

configuration, difficulty experienced during data capture, groundtruthing, technical nature of

coordinate transformation process and time expended in creating and digitizing the spatial attributes.

5.0 References

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Spatial Data Structure of Taxi Parks in Lagos, Nigeria. Lab Reportnew

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