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  • Accessibility in the District of Hambantota,Southern Sri Lanka




    JANUARY 13, 2015


    1 Introduction

    Accessibility is a measure of what services in a society, e.g. trade, health care and public services, thatare available for the citizens. A common approach to accessibility studies is to study the relationshipbetween accessibility and the level of poverty. Different studies have shown that the level of povertyincreases with decreasing accessibility to essential services e.g. markets, hospitals and schools[1, 2].

    The Hambantota district is located along the shoreline in the Southern Sri Lanka (see mini map in Fig-ure 3). The district occupies about 4% of the countrys 65,610 km2 [4] and was the fifth poorest districtin Sri Lanka with 32% of its inhabitants being poor in 2002 [5]. The road network is built up of roughly1,000 km of roads that connects the districts 12 DS Divisions, i.e. administrative divisions. The infras-tructure constitutes of basic services within e.g. health and education sectors, and fishery along withagriculture and tourism are the main sources of income for the approximately 600,000 inhabitants[4].

    Buddhism is the most common religion (70%) followed by Hinduism (9.6%), Islam (8.5%) and Chris-tianity (8%). 99% of the population in Sri Lanka find religion as an important part of their life [3] andtherefore, accessibility to shrines is considered as essential in this study.

    1.1 Aim & Hypothesis

    The aim of this study is to examine the grade of accessibility to essential services (Table 3) in theHambantota District, Sri Lanka, based on population density and poverty level.

    Our hypothesis is twofold: (1) The highest accessibility is found in regions with high populationdensity. (2) Higher travel cost, i.e. longer travel time required to essential services are found amongpoor people.

    2 Methodology

    2.1 Data

    The GIS data used as input in this study is described in Table 1.

    Table 1: Input data for accessibility study in the Hambantota District of Sri Lanka.

    No Data Set GIS Data Type Description Source1 Roads Vector (Line) Digitized road data for primary and secondary roads

    and tracks in Hambantota from 1:50000 resolution to-pography map

    NATEKO, Lund University

    2 DEM Raster 30 m resolution GDEM data USGS3 Land Use Raster 30 m resolution land use data NATEKO, Lund University4 Population Density Raster 100 m resolution population density data for 2015 WorldPop5 Fuel Stations Vector (Point) Fuel Stations Google Earth6 Hospitals Vector (Point) Hospitals Google Earth7 Large Markets Vector (Point) Largest Markets NATEKO, Lund University8 Large Towns Vector (Point) Largest towns NATEKO, Lund University9 Schools Vector (Point) Schools Google Earth10 Shrines Vector (Point) Shrines; Temples, Mosques, Churches etc. Google Earth11 Administrative Divisions Vector (Poly) Administrative DS and DN Divisions NATEKO, Lund University



    Table 2: Average velocities in which Bus and Land Master can travel depending on ground cover i.e. road oroff-road travel, average walking pace in terrain and induced velocity due to slope.

    Type of friction Bus velocity (km/h) Land Master velocity (km/h) Walking pace (km/h) Slope induced velocity (km/h)Primary road 33.0 7.0 Secondary road 15.5 7.0 Track 3.5 6.5 Off-road 6 5 05% slope 2.0510% slope 1.51020% slope 1.020X% slope 0.25

    2.2 Step Processing

    In this study, ArcMap 10.2 were used to perform a GIS-based accessibility modelling using raster im-ages, which was needed in order to perform the aimed analyses. As initial inputs, two cost surface rasterswere used; One raster in which each cell held the maximum possible travel speed in that cell based onvalues from Table 2, and one raster containing cost values of the time needed to reach the nearest road.This operation produced a new cost surface raster with the required time to pass each cell (CellSpeed).Then, a cost distance surface raster (CDS) were computed in which each cell represented the friction,i.e. cost, to travel between a cell and an essential service (Table 3). A CDS for each essential servicewas computed for two different transportation methods, i.e. Bus and Land Master, resulting in 12 CDStotally.

    In this study, travel time were used as cost measure though any currency can be used as cost inaccessibility modelling as well. The process of how a CDS was produced is explained in section 2.2.1,and a graphic clip of the full model used to produce CDS rasters is found in A First Appendix. The twoanalyses performed are described in section 2.2.2 and 2.2.3 respectively.

    2.2.1 Creating Cost Distance Surface Rasters

    The Cost Distance Tool (CDT) was used to calculate all CDS needed to perform the analyses. The CDTdetermines the least costly path to reach a chosen destination, and then assigns an accumulative costto each cell based on the distance to the location of the destination. In order for the tool to work, acost surface raster is needed and a vector layer with final destinations to where the distance should beestimated. In this study, the CellSpeed raster noted above and a point vector layer with essential serviceswere fed into the CDT. An example operation of the CDT is illustrated by Figure 1.

    Table 3: Essentials services used in accessibility modelling in the Hambantota District, Sri Lanka.

    Service Reason ChosenFuel stations Fuel Stations are an essential service since fuel are used to power generators and vehiclesHospitals Hospitals are an essential service for healthLarge markets Markets are an essential service where people sell and buy groceriesLarge towns Towns are an essential service due to job opportunities, banks and etc.Schools Schools are an essential service since education can help improve other sectors in the societyShrines Shrines are an essential service since the amount of religious people in Sri Lanka are many



    Figure 1: Example of a CDT operation to produce a cost distance sufrace. Values in the example are not relatedto the actual analysis in this study.

    2.2.2 Accessibility based on Total Population

    The total amount of population living within a certain travel distance from an essential service werecalculated using the Zonal Statistics tool. The input data used in this operation were a populationdensity raster and the different CDS rasters reclassified into 12 groups based on travel distance in time(h). One table per CDS raster was created, which then were used to create the bar graphs in section 3.2.Figure 2 illustrates an example operation of this analysis.

    2.2.3 Accessibility on Poverty Level

    In order to analyse the accessibility to large towns of poor people, the large towns CDS raster wasreclassified as Boolean raster using a threshold cost value of 2 h. Total population in each Booleanclass was calculated for different DS Divisions based on transportation with bus. Then within eachDS Division, the total amount of poor people according to Census data [5] was correlated with theestimated population with high cost accessibility, i.e. long travel time. Finally, a regression analysiswere performed of the Household Population Below Poverty Line (HPBPL) and the estimated populationwith high accessibility cost to large towns. HPBPL is the abbreviation of a poverty measure index usedby the Census Department of Sri Lanka. The DS Division Tissamaharama was an outlier due to its greatdeviation from the mean and was therefore removed before the analysis.

    Figure 2: Example of a population accessibility analysis similar to the one performed in this study. Values are notthe same as in the real analysis performed.



    3 Results & Discussion

    This study contains several steps and therefore the results with discussion are presented in the followingsections (3.13.3) in accordance to the work flow.

    3.1 Accessibility Modelling

    Figure 3 illustrates the cost surface raster for bus as transportation method. Each cell in the raster imageis represented by the time required to pass that cell, shown as cost. Dark green cells seen as lines in thefigure represent roads, which allow higher travel speeds than cells representing off-road terrain, and hastherefore obtained low cost values. Light grey zones within the study area were given the value NoDatasince those cells represent e.g. water bodies, forests or banana plantations where bus travel is unlikely.

    A comparison between the two CDS rasters to nearest hospitals are shown in Figure 4a and 4b. Due tothe higher velocity by bus travel, a significantly better accessibility is seen in Figure 4a. In both figures,the accessibility increases with decreasing distance to roads as well as with road type. However, this isnot as clear for land master travel since land masters are off-road vehicles designed for low velocitieson most ground covers.

    Figure 3: A cost surface raster of cost in hours with bus as transportation. The cost is calculated from average bustravel speeds depending on road type and slope angle, and from average off-road walking pace.



    (a) Cost Distance Surface to Nearest Hospital by Bus.

    (b) Cost Distance Surface to Nearest Hospital by Land Master.

    Figure 4: Comparison of accessibility in hours to nearest hospital using bus (a) and land master (b) astransportation.

    3.2 Accessibility of