Project final version

i How good is OpenStreetMap? A Comparative Study of OpenStreetMap and Malawi

Survey Data Sets: Case Study of Lilongwe City

DECLARATION

I declare that this research project is the product of my own effort and has not been previously

submitted for a degree at this University or any other University.

Name: Lwitiko Mulwafu

Signature: …………………………………. Date: …………………………………

Supervised by:

Name: Mr. Edgar Malombe

Signature: …………………………………… Date: ……………...............................

ii How good is OpenStreetMap? A Comparative Study of OpenStreetMap and Malawi


DEDICATION

I dedicate this work to my Mum and Dad for their invaluable love, support and advice they gave

me during my studies. I love you.

iii How good is OpenStreetMap? A Comparative Study of OpenStreetMap and Malawi


ACKNOWLEDGEMENTS

Firstly, I would like to thank my supervisor, Mr. Edgar Malombe for giving me the opportunity to

undertake this project and for all his help throughout the project and for always being willing to

assist with all my queries.

I also wish to send my enormous gratitude to the current Northern Regional Surveyor General,

Masida Mbano for explaining to me significant details of the buffer analysis program that

facilitated the analysis procedure of the entire study.

To the entire Land Management staff, I really express my gratitude for the direction in my

academic life since my first level to the final level.

I also acknowledge my colleagues, most notably Gift Banda, Simon Tembo, Joseph Mikuwa,

Fredrick Nazombe, Precious Mzumara, Mark Chin’gombe, Grifton Gawani, Innocent

Kadammanja, Maggie Gondwe, Felister Geya, Chack Banda, Mwawi Kamanga, Collings

Kafunda, Beston Chisamile, SCOM members, and all friends for they were superb in academic

life, heartfelt advice and good humour. I will always remember you.

My final thanks goes to my family for boundless love and encouragement. It was indeed a long

journey. Remain blessed.

iv How good is OpenStreetMap? A Comparative Study of OpenStreetMap and Malawi


LIST OF FIGURES

Figure 2.1: ITN data used for the comparison across London ………….4

Figure: 3.1 Study Area, Lilongwe City………………………………….8

Figure 3.2: Lilongwe Streets dataset……………………………………11

Figure: 3.3 Georeferenced OSM data of Lilongwe city ………….……13

Figure: 3.4 Georeferenced Street Guide ..........................................…...14

Figure: 3.5 Digitized OpenStreetMap…………………………………..15

Figure 3:6 Digitized Lilongwe Street Guide……………………………16

Figure 3.7: Overlapped OSM dataset on the reference dataset ………...17

Figure 4:1 Digitized Lilongwe Map showing roads with names……….1

Figure 4.2: Buffer Analysis…………………………………………….19

Figure 4.3: Intersection of the buffered dataset…………………………20

Figure 4.4: Digitized Lilongwe Map showing roads with names………22

Figure 4.5: Digitized Lilongwe Map showing Length Completes………24

v How good is OpenStreetMap? A Comparative Study of OpenStreetMap and Malawi


LIST OF TABLES

Table 3.1: List of coordinate……………………………………….12

Table 4.1: Results of intersection after buffer analysis ……………21

vi How good is OpenStreetMap? A Comparative Study of OpenStreetMap and Malawi


LIST OF ABBREVIATIONS

OSM= OpenStreetMap

GPS = Global Positioning System

VGI = Volunteered Geographic Information

vii How good is OpenStreetMap? A Comparative Study of OpenStreetMap and Malawi


ABSTRACT

The past few years have seen an influx of open source websites due to advances in technology an

example of one such project in the is OpenStreetMap, a free editable map of the world created by

volunteers.

However, as with all open source projects questions are asked about the quality and validity of the

data provided by OpenStreetMap as the volunteers that contribute the data lack the sufficient

cartographic training and the quality cannot be guaranteed. This study was the carried out by

looking the positional accuracy of OpenStreetMap data by comparing with the Malawi Survey

dataset which is the official data provider.

This study carried out the positional accuracy through a buffer analysis, name Completeness was

assessed by comparing the names on the reference dataset and that of the tested dataset through

the visual comparison and ground verification. Lastly length completeness was calculated as the

percentage of the length of the tested dataset to the length of the reference dataset.

The results of this study found that positional accuracy of OpenStreetMap data is very good in

comparison to the Malawi Survey dataset more than 90% of the dataset was found within the 10

meters of the buffer zone. The completeness was found to be very good with a percentage above

85% in some areas it was found that the reference dataset has more data than the reference dataset.

It was further found out that 19 % of the total length covered in the OSM within the study area are

labelled which means that the total length of 81% are not labelled.

viii How good is OpenStreetMap? A Comparative Study of OpenStreetMap and Malawi


CONTENTS

DECLARATION .......................................................................................................................................................... i

DEDICATION ............................................................................................................................................................. ii

ACKNOWLEDGEMENTS ....................................................................................................................................... iii

LIST OF FIGURES .................................................................................................................................................... iv

LIST OF TABLES ....................................................................................................................................................... v

LIST OF ABBREVIATIONS .................................................................................................................................... vi

ABSTRACT ............................................................................................................................................................... vii

CHAPTER 1: INTRODUCTION............................................................................................................................... 1

1.1 Background ........................................................................................................................................................ 1

1.2 Problem Statement ............................................................................................................................................ 2

1.3: Objective of Study ............................................................................................................................................ 3

1.3.1General Objective ........................................................................................................................................ 3

1.3.2 Specific Objective........................................................................................................................................ 3

1.4 Significance of the Study ................................................................................................................................... 3

CHAPTER 2: LITERATURE REVIEW .................................................................................................................. 4

2.1 Positional Accuracy ........................................................................................................................................... 4

2.2 Name Completeness ........................................................................................................................................... 6

2.3 Length Completeness ......................................................................................................................................... 6

CHAPTER 3: THE RESEARCH METHODOLOGY ............................................................................................. 8

3.1 The Study Site .................................................................................................................................................... 8

3.2 Preparation of the datasets ............................................................................................................................... 9

3.2.1 OSM dataset ................................................................................................................................................ 9

3.2.2. Malawi Survey Datasets .......................................................................................................................... 10

3.3 Data Collection Methods ................................................................................................................................. 12

CHAPTER 4: RESULTS ANALYSIS ..................................................................................................................... 18

4.1 Positional Accuracy ......................................................................................................................................... 18

4.2 Name Completeness ......................................................................................................................................... 22

4.3 Length Completeness ....................................................................................................................................... 23

CHAPTER 5: CONCLUSION AND RECOMMENDATIONS ............................................................................ 25

5.1 Conclusion ........................................................................................................................................................ 25

5.2 Recommendations ............................................................................................................................................ 25

ix How good is OpenStreetMap? A Comparative Study of OpenStreetMap and Malawi


5.3 Future work ...................................................................................................................................................... 25

REFERENCES .......................................................................................................................................................... 26

1 How good is OpenStreetMap? A Comparative Study of OpenStreetMap and Malawi


CHAPTER 1: INTRODUCTION

1.1 BACKGROUND

OpenStreetMap (OSM) is the most popular Volunteered Geographic Information (VGI)

community. OSM was initiated by Stephen Coast in July 2004 at the University College

London. Since its establishment, OSM is expanding at a large scale to the extent that the number

of registered users grew rapidly so that by November 2010 there were more than 320,000 registered

users and more than 2.000 million tracked points in the database (OSM 2010a).

In the past, urban data management used to be performed by professional cartographers, public

authorities or commercial data providers. Nevertheless, in the last couple of years a new and

different trend for data collection has evolved, describing the collaborative and volunteered

collection of geographic data. (VGI) is a term used to describe the voluntary act of creating

geographic information, often by users who are largely un-trained with little or no formal

qualifications in the creation of such data. This is sometimes also referred to as “Neogeography”,

which can be defined as combining the complex techniques of GIS and cartography and placing

them within the reach of users and developers, and is essentially about people using and creating

their own maps, by combining elements of an existing toolset (Turner 2006).

This has also been described as the ‘wikification’ of GIS where the masses are quietly being

transformed from being passive consumers to active producers of geospatial information (Sui,

2008). Volunteered Geographic Information (VGI) comprises the effect that an ever-expanding

range of users creates, assembles and disseminates geographic and spatial data in a collaborative

and volunteered manner (Goodchild 2007a). This means an individual person or groups collects

and creates geographic data based on their personal measurements and their knowledge about their

surroundings and furthermore share that information with others through open web platforms.

Song & Sun (2010) alluded to that VGI can be considered as a new opportunity of systems and

sensors for monitoring urban and regional environments. Particularly in urban environment where

the coverage is very good, because many humans results in many potential sensors and therefore

the usage of VGI in urban management increases.

As an online map collaborative plan, provided voluntarily by individuals involving the capture,

processing and dissemination of geographic information, the project aims to create and distribute

vector data for the world because most maps thought of as free actually have legal or

technical restrictions on their use, holding back people from using them in creative, productive,

or unexpected ways.

OSM follows the peer production model (Haklay & Weber 2008) that created Wikipedia and aims

for the provision of free to use and editable map data. The data in OSM is created in different

ways, one of which being acquisition of original data, manually captured by users with GPS



devices. However, people can also contribute data based on aerial images (e.g. by Bing or Yahoo)

or by contributing their local knowledge about the region they live in.

1.2 PROBLEM STATEMENT

According to Benkler (2006) OpenStreetMap (OSM) project offers completely free data with an

open content license which eases data collection to user but still questions arises on the quality and

validity of the information delivered by the OSM. It is however unknown on whether the street

data provided by the volunteers to the OSM maintains its position in terms of the ground

coordinates and its names so as to serve its purpose in guidance. In august 2013 Malawi held the

SADC Summit which saw delegates from different countries attending the meeting and in these

cases OpenStreetMap could play a greater role as guidance tool in navigation so as to help in

finding hotels, filling stations, health facilities and many more important features. Hence a

requirement for an understanding on how good OSM data actually is, so as to identify areas of

improvement and determine the fitness of the map obtained.



1.3: OBJECTIVE OF STUDY

1.3.1GENERAL OBJECTIVE

The overall objective of this research will be to compare OpenStreetMap with Malawi survey data

sets. This objective will be achieved by specifically looking at the following objectives.

1.3.2 SPECIFIC OBJECTIVE

1. To establish data quality in terms of positional accuracy

2. To establish data quality in terms of name completeness

3. To establish data quality in terms of length completeness.

1.4 SIGNIFICANCE OF THE STUDY

The study is essential because it will discuss the major concern of the OSM data and measure

whether they are used in appropriate way considering their quality. Users’ needs maps to have

known accuracy and up-to-date so as to be reliable. If these aspects are identified on OSM then

the fitness of use and the quality of the user generated project shall be evaluated.

After gauging the level of positional accuracy of the OSM dataset, the next issue is the level of

completeness. While Steve Coast, the founder of OSM, stated it is important to let go of the concept

of completeness’ (GISPro, 2007, p.22), it is important to know which areas are well covered and

which are not otherwise, the data can be assumed to be unusable. Furthermore, the analysis of

completeness can reveal other characteristics that are relevant to other OSM projects.



CHAPTER 2: LITERATURE REVIEW

2.1 POSITIONAL ACCURACY

Initially, Haklay (2010), focused on the positional accuracy and length completeness of England

OSM data through comparison with the Ordinance Survey’s Meridian 2 dataset. The methodology

used to evaluate the positional accuracy was based on Goodchild and Hunter (1997) and

Hunter (1999). The comparison was carried out by using buffers to determine the percentage of

line from one dataset that is within a certain distance of the same feature in another dataset of

higher accuracy. The analysis showed that OSM information can be fairly accurate with average

within about 6 meters of the position recorded by the Ordnance Survey, and with approximately

80% overlap of motorway objects between the two datasets.

Following this work, Ather (2009) studied four areas of London in detail, using the highly accurate

Master Map Integrated Transport Network (ITN) from Ordnance Survey as shown in Figure 2.1

below.

Figure 2.1: ITN data used for the comparison across London (The Cartographic Journal Vol. 47

No. 4 pp. 315–322 November 2010)

Note that Meridian 2 is a generalized dataset, which its nodes are kept in their original position, as

was measured in high accuracy methods, the number of nodes in each road segment is reduced

through the application of a 20-metre buffer. ITN, on the other hand, records the centreline of

roads across the UK, based on information from field survey and through photogrammetry using



high-resolution imagery. The accuracy of ITN is expected to be less than 1 metre in urban areas,

where the study took place.

Overall, 109 different roads were examined covering over 328 km. The results of the buffer

analysis showed that on the whole the percentage overlap for all the roads were very high. Each

tile had a combined average percentage overlap above 80%, with the tile TQ37sw (South London)

having largest percentage overlap of 85.8%. Three of the roads examined had an exact 100%

overlap, the A4207, B412 and the B412. Between each tile, the average percentage overlap for the

A-roads ranged between 92.6% to 84.3% and for B-roads it ranged between 81.64% to 71.52%,

and the only motorway to measured, the M4, had a very high percentage overlap of 98.9%. A

pattern here is that the larger road type had better results, as for example the percentage overlap of

the motorway segment was higher than the average A-road results, which in turn was higher than

the average B-road results. This is most probably due to the nature of the buffer analysis where

larger buffer sizes were used for the wider road types, and hence there is a greater margin for error

for any data collected on wider roads. Regardless of this, the average percentage overlap for each

road type was still very high.

Looking closely at the distribution of the results, a large majority of the roads examined had

percentage overlaps above 90%, as illustrated by the histogram below (Figure 29). The histogram

clearly showed the results are skewed towards 100% overlap, with the cumulative percentage

overlap results increasing dramatically at two stages, once after 70% overlap and a second time

after 85% overlap. The last bin (95-100% overlap) contained the highest frequency of results, with

64 if the roads examined (57%) falling in the last three bins (85%-100% overlap).

According to Bhattacharya (2012.p.36) at least 59% of OSM road network was found to be within

2 meter buffer zone, and up to 122% of OSM road placed within 6 meter buffer zone of

corresponding TOP10NL road. This result indicates that there exist more than one roads of OSM

within the 6 meter buffer zone of corresponding TOP10NL road. As said 6 meter is a good

positional accuracy so OSM road network data in the Netherlands was considered to be as quite

compatible with TOP10NL.

Neis et al. (2010) compared the length of the mapped street network of commercial data and OSM

for the year 2010. They stated that there are nearly no differences in the overall street length

between both data sets, but found a difference of 40 percent of street segments capable for routing

applications.



2.2 NAME COMPLETENESS

Apart from the positional accuracy Ather (2009) conducted quality tests for the road name attribute

completeness and for the number of users per area. The results showed that the areas with higher

percentage overlap results also had greater levels of road name completeness. It was suggested

that analysis on other attribute information, such as road type would be useful since it was noticed

to be left blank in many cases. With regards to the user analysis the results showed a positive

correlation between road name attribute completeness and number of users per area. On the

other hand, the number of users and the positional accuracy did not appear to have any correlation

at all.

The results of the name completeness analysis showed that only 26% of roads within the study

area are labelled meaning that 84% of the roads are unlabeled roads. Comparing these results with

the results of the previous research made by Ather (2009) for the area of London, we can clearly

see that there is a significant, difference since the most incomplete area of London had only 31%

of roads without name.

Ather (2009 .p.62), results of the road name attribution analysis showed that out of the four tiles

in figure 2.3 above TQ28se (North/Central London) was the largest dataset with 98.53km of road

and had the greatest level of road name attribute completeness with only 5% of roads unlabeled.

Tq38se (East London), the second largest dataset with 94.3 km of road, was the most incomplete

with 31% of road name attributes missing.

2.3 LENGTH COMPLETENESS

Haklay (2008) commented that the completeness of OSM is the most significant aspect of VGI

quality. He calculated the difference of total length of all roads from the OSM and Meridian 2

dataset to check the completeness of the OSM dataset. It appeared that the total length of the OSM

dataset covers up to 69% of the Meridian 2 dataset.

The research further examined length completeness element on comparison between Meridian2

(reference dataset) and OSM (test dataset). The study area covered five OS tiles at 1:10,000

resolution, covering 113 square kilometres across England. England was considered the most

appropriated study area due to the fact that OSM started there. The evaluation of completeness

was undertaken for the whole of England. The results showed that OSM data cover 29% of

England and approximately 4% of these data lack complete set of attributes. Generally, major cities

are well covered but that is not the case as you move from the centers to the outskirts. Hence the

inconsistency of OSM data means that is not yet suitable for more sophisticated GIS analysis.

Bhattacharya (2012.p.30) found out that the road line data of OSM over all study areas covered

more than 72% of the TOP10NL road line data. It was further found out that in Delft the OSM



road line data was covered more than TOP10NL. Finally, it was concluded that though the OSM

data over the study areas was not yet 100% complete, they were well presented and the percentage

of completeness was different in different locations. From the high presence of some object classes

it may be presumed that in the near future the OSM data of these areas will be complete.

This brief literature review highlights, the clear need for understanding of OSM data quality in

order to provide an assessment of the level of accuracy of the current and provide

recommendations on OSM dataset.



CHAPTER 3: THE RESEARCH METHODOLOGY

3.1 THE STUDY SITE

A research was done in Lilongwe city, located in the central region of Malawi. Lilongwe city was

selected because it has received a lot of contributions on the OpenStreetMap hence being ideal for

the research site. Furthermore the study area includes the City Centre and some suburbs which

enablebled the researcher to compare the results between areas that have dense road network with

high mobility and other areas. The selected spatial extent of interest is shown in figure 3.1 below.

Figure 3.1: Study Area, Lilongwe City; Date of data acquisition: 28thJune, 2014. (Source:

www.openstreetmap.org)



3.2 PREPARATION OF THE DATASETS

3.2.1 OSM DATASET

As outlined in the literature review, all map data used to create OpenStreetMap has been collected

and uploaded by users who have registered on the OSM website. The collection is carried out

through the use of handheld GPS devices, or traced from Yahoo! Imagery or other free map

sources, and digitized using either the online or offline editing tools Potlatch and JOSM.

The issue of inconsistent results and differing levels of accuracy in the OSM dataset is also partially

due to the fact that different GPS devices are used by each user, and therefore the measurements

are of different levels of accuracy depending on the device used. The level of accuracy is also

highly dependent upon the nature of data collection. For a user collecting data whilst driving, the

number of points collected for any particular road would vary upon the speed of the car; driving

slowly allows more GPS points to be measured and would therefore lead to greater accuracy.

Therefore, users who collect data by cycling or by walking are more likely to obtain more accurate

results as they are travelling slower.

Numerous human errors can also occur during the digitization process. Users may accidently make

changes to the map whilst digitizing their own points, for example by moving road points created

by other users. This may certainly be the case for first time users using Potlatch, who may be

experimenting at first, although a practice option is provided where no edits to the map are saved.

Another problem is the ease of sabotage, as once some registers to OSM they are free to make any

changes they like to the OSM map.

Although there are specific guidelines about how the data must be edited, it does not exempt the

user from human errors which occurs while the map is being created which results in inconsistent

of OSM datasets. Another factor that makes the OSM inconsistent is that users may collect the

data from different sources, hence obtaining the measurements of different levels of accuracy.

Since the accuracy of OSM data cannot be assessed precisely due to the nature of the mapping

methods the only way to evaluate the quality of these OSM data will be by comparing the obtained

OSM with a higher level datasets which was obtained at digital and mapping section in the

Department of Surveys (DoS).



3.2.2. MALAWI SURVEY DATASETS

The Lilongwe street guide road was produced by the by the Department of Surveys (DoS) at the

Malawi Surveys. The Malawi Survey is the most official map provider that produces the

cartographic products which are widely used in the country, this is so because there are specialist

who are assigned to collect data and hence fourth produce the map at specified accuracy depending

on the use of the map. During preparation of the data it was found that the Lilongwe Street Guide

dataset was comprehensively updated in 2003 and partially updated in 2012 as Malawi was

preparing to hold SADC summit in 2013.

During the partial update, major roads which were connected to major facilities such as hotels,

hospitals were carefully retraced based on the comprehensive map which was produced in 2003.

Centered on its accuracy the researcher saw it a need to use the dataset as a reference dataset.

Figure 3.2 below shows the map produced by the Malawi survey.



Figure 3.2: Lilongwe Streets dataset, (Source: Department of Surveys (DoS), 2012).

Study site



3.3 DATA COLLECTION METHODS

The following methodology aims to assess the quality elements of OSM road network (tested

dataset) against the Lilongwe Street Guide dataset (reference dataset). The methodology focuses

on the analysis of each element and on the comparison between the results of those. The

length completeness analysis was conducted for road network of the dataset but the rest of the

analysis was carried out only for the primary, secondary and tertiary road class due to time

restrictions.

Primary data was collected through field work using the Trimble Nomad GPS. Coordinates in form

of Universal Transverse Mercator (UTM) was collected from the selected crucial points of the

roads so as to act as references. The Trimble Nomad GPS recorded two kinds of information

namely the waypoints and tracks that was useful for georeferencing maps both the OpenStreetMap

and the street guide obtained from Surveys Department in Lilongwe.

The researcher used kinematic positioning to obtain reference points which were then used in

georeferencing the OpenStreetMap and the street map guide which was obtained from digital and

mapping section in the Surveyor General’s office. The following coordinate were obtained in nine

stations, (A to I).

Station ID Easting Northing

A 579898.742 8454929.903

B 581753.265 8455023.972

C 583186.404 8453253.726

D 584966.892 8450889.034

E 587317.496 8454109.726

F 584799.083 8455816.772

G 584998.305 8457070.281

H 584898.962 8457642.575

I 581004.716 8458673.678

Table 3.1: List of coordinates



Figure 3.3: Georeferenced OSM data of Lilongwe city; Date of data acquisition: 13th September,

2014. (Source: Georeferenced by the researcher)



Figure 3.4: Georeferenced Street Guide; (Source: Department of Surveys (DoS++66) 2013)

The two georeferenced datasets were then digitized using the editor tool in ArcGIS10 software.

Figure 3.5 and 3.6 below shows the digitized data obtained from the two datasets.



Figure 3.5: Digitized OpenStreetMap (source; Digitized by the researcher)



Figure 3.6: Digitized Lilongwe Street Guide (source; Digitized by the researcher)

After digitizing the OSM and Lilongwe Street map Guide which in this case is a reference dataset,

the OSM dataset was overlaid on the referenced data such that impression of the similarities and

dissimilarities was found. Figure 3.7 bellow depicts the visualization of all object classes together

from OSM and Lilongwe datasets from the same area.

Study

Area



Figure 3.7: Overlapped OSM dataset on the reference dataset (source; Digitized by the researcher)



CHAPTER 4: RESULTS ANALYSIS

4.1 POSITIONAL ACCURACY

Positional accuracy was carried out through a buffer analysis as recommended by Goodchild and

Hunter (2004). The buffer method was used to know how far or close was the displacement

between two datasets namely the reference (Street Guide Road) and tested dataset (OSM) as shown

in figure 10 below. The results of this method were presented as percentage of the data

within the buffer zone of 10 meters as indicated by Ather, (2009 p; 18). A 100% overlap means

that the tested dataset are perfectly overlapped to the reference dataset within a buffer zone.

Figure 4.1: Buffer Method (source; Digitized by the researcher)

The buffer method was applied in the Main roads and Secondary roads within the study area as

indicated in figure 4.2 below:



Figure 4.2: Buffer Analysis (Source; Prepared by the researcher)

The intersection of two dataset was then performed using ArcGIS software such that the roads

which was not within 10 meters was clearly shown as indicated in figure 4.3 below where the red

colour indicates roads within the buffer zone while blue colour indicates roads which are not within

the 10 meters buffer zone.

The figure 4.3 below shows the results of intersection after the buffer analysis was carried out.



Figure 4.3: Intersection of the buffered dataset (Source; Prepared by the researcher)

The results was the of the roads within 10 meters buffer zone was then calculated as indicated by

table 4.l below



The details of road length within and out of the buffer zone is shown in the table 4.1 below

Names of ROADS Total length (Meters) OSM within 10m buffer zone (Meters) Percentage

Mzimba Street 5,281.85 5,281.85 100

Chidzanja Road 4,053.81 3,369.21 83

Youth Drive 3,142.10 2,896.50 92

Chayamba Road 3,700.17 3,674.55 99

Presidential Way 7,938.38 6,842.88 86

Kenyatta Road 4,155.71 4,155.71 100

Paul Kagame Road 3,547.60 3,547.60 100

Kamuzu Procession Road 10,642.91 9,314.73 88

Kaunda Road 4,231.63 3,062.66 72

Lilongwe Bypass ---------- ---------- -----

Queens Road 2,398.08 2,398.08 100

Sir Glyn jones Avenue 4,473.96 4,442.54 99

Table 4.1: Results of intersection after buffer analysis.

The overall results shows that 92% of roads in the OSM dataset are found within the 10 meters of

the buffer zone which indicates that there is high positional accuracy and this is in line with what

Kounadi, (2009.p.58) found. Since high accuracy in position is achieved then the OSM dataset can

be fairly used by different organizations as for instance in tourism as a navigational tool guide for

tourist and disaster management. In disaster management OSM dataset can be used to control

further spread of diseases such Ebola by indicating the positions of areas which are affected on the

map as a no goes on areas.

The study further found out that Lilongwe bypass is only available on the OSM dataset and not on

the street guide dataset which was used as the reference dataset. This is so because the Lilongwe

bypass is a newly constructed road and it was not there when the reference dataset was being

prepared.



4.2 NAME COMPLETENESS

Name Completeness was assessed by comparing the road names on the reference dataset and that

of the tested dataset. This was done through the visual and ground verification in terms of roads

attributes after the two datasets were matched.

Figure 4.4 below shows the road names of the two datasets.

Figure 4.4: Digitized Lilongwe Map showing roads with names (source; Digitized by the

researcher)

The study found that all road names which appears on the reference dataset was also present on

the tested dataset. It was further established that the tested dataset had more road name percentage



than the reference dataset as indicated by figure above where roads in blue are the road which have

names labeled on OSM dataset while missing the labels on the Lilongwe street guide.

Although the OSM datasets registered some confidence in presence of road names but it was

however found that the length completeness was very high as compared to name completeness.

This is so because many roads residential roads which appear in both dataset are not labelled and

it was calculated that only 19 % of the total length covered in the OSM within the study area are

labelled which means that the total length of 81% is not labelled.

It was further established that the reference dataset maintains some old names which was changed

as for instance Paul Kagame road is indicated as Chilambula road on the reference dataset although

changed on 4th September, 2007 after being upgraded into a highway. This is so because the

Lilongwe Street Guide (reference dataset) has the names which are not up- to-date,

4.3 LENGTH COMPLETENESS

Length completeness calculated as the percentage of the length of the tested dataset to the length

of the reference dataset. The calculations were carried out for on a sample grid square.



Figure 4.5: Digitized Lilongwe Map showing Length Completes (source; Digitized by the

researcher)

Generally, the completeness is very good with a percentage above 85%. The road that do not

appear in the OSM map dataset are residential roads as it was found that all major roads on the

referenced datasets are available on the tested datasets. It was found that some grids have low

percentage of the road completeness as for instance in south east grids in figure 11 above.

It was however found in grid A the total OSM road network was 8000.349 meters in length and

Lilongwe Street Guide road network is 7853.366 meters such that the OSM road network is 2%

more than the Lilongwe Street Guide road network.



CHAPTER 5: CONCLUSION AND RECOMMENDATIONS

5.1 CONCLUSION

The study managed to achieve its main objective and specific objectives as stated in the preamble

of the dissertation. Therefore, it can be fairly concluded that the position accuracy of OSM is quiet

high in the primary and secondary roads which means that the datasets can be used with reliability

for several cartographic purposes.

However there are still issues with regards to the name completeness of the data. The analysis

showed that 81% of roads within the study area are unlabeled. Furthermore, the study disclosed

that as volunteers keep the OSM data up-to-date by continuously providing information of new

changes in the real world, users of OSM receive more frequently updated information. On the

contrary, Lilongwe Street Guide users need to wait till the next version is available, which happens

when there is a need without known duration.

5.2 RECOMMENDATIONS

In connection to the findings of this research, the following recommendations have been put

forward:

Ground verification needs to be intensified so that the data which is collected on the OSM

can be labelled. This is due to the fact the road names can only be established by users who

have conducted ground surveys, as identifying road names from other map sources would

have some errors and it can be accompanied by restrictive copyright laws.

The OSM dataset can be fairly used in the production of sketch plans by Malawi Surveys

as its positional accuracy has proved to be high.

Malawi Survey dataset needs to be update frequently if it is to continue standing out as an

official dataset.

5.3 FUTURE WORK

As per the findings of this study, it is suggested that the following areas should be further studied:

The same study can be carried out on a larger sample covering even the position

accuracy of residential roads so as to reduce the limitations of generalisation.

A deeper statistical analysis should be carried out on the data completeness with another

official dataset.



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