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RURAL INACCESSIBILITY SCORE (RIS):An Alternative Approach to Measure Accessibility

Ashoke Kumar Sarkar, Ph.D. and Harish PuppalaBirla Institute of Technology and Science, Pilani (India)

Rural Road Connectivity

Rural Road Connectivity is not only a keycomponent of Rural Development bypromoting access to economic and socialservices and thereby generating increasedagricultural incomes and productiveemployment opportunities in India, it is alsoas a result, a key ingredient in ensuringsustainable poverty reduction.

…………PMGSY Guidelines

MDG & SDG Goals and Rural Roads

• Rural Roads accessibility has not been mentioned directly neither in MDG nor in SDG

• However, for achieving most of the goals the relevance of rural roads can be directly correlated

• Most of Least and Less developed countries have been investingto provide road connectivity in rural areas.

• However, fund is always limited.

• The available fund should be judiciously used and distributedwith some logical approach among the regions/countries.

Measuring Accessibility

• A need was thus felt to develop an indicator that would measure the level of accessibility in a

region/ country

• Rural Access Index (RAI in percent) was developed in 2006 to be used by International

Development Association (IDA).

• It is expressed as:

TOP2km – Population within a buffer of 2 km of the road network; TOP – Total Population within a

region/ country

• In India, buffer distance is taken as 0.5 km for plain area and 1.5 km for desert & tribal areas and

hilly terrains.

𝑅𝐴𝐼 =𝑇𝑂𝑃2𝐾𝑚𝑇𝑂𝑃

× 100

PMGSY: An Initiative by Indian Government

Pradhan Mantri Gram Sadak Yojana” (PMGSY) was launched in the year 2000, as a centrally

sponsored Programme.

A well structured model adopted by the Central Government in India under PMGSY that has

further motivated some of the states to start their own rural connectivity schemes in order to

accelerate rural connectivity program.

Since 2000, about 380,000 km road length has been constructed under the programme

connecting about 154000 habitations till April 2019.

• A Study was conducted in 2007 by BITS Pilani to determine theRAI for a few selected Blocks of Rajasthan (primarily in desertregion) and Himachal Pradesh (in hilly region)

• RAI (IDA) and RAI (PMGSY) were determined and compared.

• The study was awarded by NRRDA (responsible for theimplementation of PMGSY programme) at the instance of theWorld Bank.

Comparison of RAI (PMGSY and IDA)values in Desert Region

Name of

District

Name of

Block

Total

population

of Block

Population

living

within

0.5km band

on both

sides of all-

weather

roads

RAI

(PMGSY) in

percent

Population

living within

2.0km band

on both

sides of all-

weather

roads

RAI (IDA)

in percent

Difference in

percent

between

RAI(PMGSY)

and RAI(IDA)

Jhunjhunu

Khetri 214474 189595 88.4 199708 93.11 4.71

Nawalgarh 217262 174483 80.31 194047 89.31 9.00

Surajgarh 172324 152076 88.25 166608 96.67 8.42

Chirawa 152844 133053 87.05 144638 94.63 7.58

Churu Taranagar 151006 121334 80.35 137821 91.26 10.91

Comparison of RAI (PMGSY and IDA) values in Hilly region

Name of

District

Name of

Block

Total

population of

Block

Population

living within

1.5km band on

both sides of

all-weather

roads

RAI

(PMGSY) in

percent

Population

living within

2.0km band

on both

sides of all-

weather

roads

RAI (IDA) in

percent

Difference in

percent

between

RAI(PMGSY)

and

RAI(IDA)

Shimla

Rohru 51063 51063 100 51063 100 0

Mashobra 75320 75320 100 75287 99.95 0.05

Narkanda 39575 39575 100 39520 99.86 0.14

Chopal 74426 74426 100 74399 99.96 0.04

Sirmour Shilli 53345 53345 100 53345 100 0

New Accessibility Index

RAI was modified in 2016 (RAInew). The new RAI is conceptually the same but advancedtechniques such as Geographic Information System (GIS) has been used at three levels,namely population distribution, road network and road conditions.

The first term in the above equation represents beneficiaries, the second term represents

the quality of road network and the last two terms represent road density and rural

population density respectively.

A study is conducted by Atsushi Limi et. al., (2016) in which RAI is reformulated as Equation

shown below to examine the correlation between RAI, Road density and Quality

𝑅𝐴𝐼𝑛𝑒𝑤 =𝐴𝑐𝑐𝑒𝑠𝑠 𝑃𝑜𝑝

𝐺𝑜𝑜𝑑 𝑅𝑜𝑎𝑑 𝐾𝑚

𝐺𝑜𝑜𝑑 𝑅𝑜𝑎𝑑 𝐾𝑚

𝑇𝑜𝑡𝑎𝑙 𝑅𝑜𝑎𝑑 𝐾𝑚

𝑇𝑜𝑡𝑎𝑙 𝑅𝑜𝑎𝑑 𝐾𝑚

𝑅𝑢𝑟𝑎𝑙 𝐴𝑟𝑒𝑎

𝑅𝑢𝑟𝑎𝑙 𝐴𝑟𝑒𝑎

𝑅𝑢𝑟𝑎𝑙 𝑃𝑜𝑝

Beneficiaries Quality Road Density Pop density-1

RAI and RAINEW : One criticism

• RAI and RAInew consider only the accessibility to road by the

habitations within 2km.

• Level of inaccessibility (may be represented by distance)

experienced by the residents of inaccessible habitations is not

considered.

• The dispersion of inaccessible habitations will vary.

RAI, RAINew and dispersion of villages

Figure (a) Figure (b)

Let us consider a case: Same road network and population of villages.

Only locations are different for inaccessible villages.

An additional insight for RAI and RAINew

• The only distinguishing parameter in (a) and (b) is the spatial distribution ofunconnected villages.

• RAI and RAINew values for both the areas are same.

• However, the difficulty levels of accessibility in case of (b) will be high for theinaccessible villages as the residents will have to travel longer distances incomparison to the unconnected habitations shown in (a).

• Logically the region shown in (b) should have a lower accessibility level.

• Thus, an attempt has been made to capture the level of inaccessibility of

inaccessible villages in the Index proposed in this presentation.

Proposed Indicator

• It measures both inaccessibility and the levels of hardship (inaccessibility in terms of distance) faced by the residents of unconnected (inaccessible) villages.

• Two indicators have been introduced, namely Rural Inaccessibility (RI) and Level of Inaccessibility (LoI).

RIS: Rural Inaccessibility Score

Components of Rural Inaccessibility Score

RI = Total population having no access to road in a region/ country (in terms of standard distance

used by IDA and PMGSY

Rural Inaccessibility Score (RIS)

Rural Inaccessibility (RI) Level of Inaccessibility (LoI)

𝑅𝑖 is weightage assigned based on the condition of the road network. If the condition of the road network

is unusable, a weightage of 0 will be adopted, where as if the condition is poor, weight of 0.5 can be adopted

𝑳𝒐𝑰 =(𝐿𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 ℎ𝑦𝑝𝑜𝑡ℎ𝑒𝑡𝑖𝑐𝑎𝑙 𝑟𝑜𝑎𝑑 𝑛𝑒𝑡𝑤𝑜𝑟𝑘)

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑢𝑛𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑒𝑑 𝑣𝑖𝑙𝑙𝑎𝑔𝑒𝑠+

𝑖=1

2(𝑅𝑖 ∗ 𝐿𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑟𝑜𝑎𝑑 𝑛𝑒𝑡𝑤𝑜𝑟𝑘 𝑢𝑛𝑢𝑠𝑎𝑏𝑙𝑒/𝑝𝑎𝑟𝑡𝑖𝑎𝑙𝑙𝑦 𝑢𝑠𝑎𝑏𝑙𝑒 𝑑𝑢𝑒 𝑡𝑜 𝑝𝑜𝑜𝑟 𝑟𝑜𝑎𝑑 𝑐𝑜𝑛𝑑. )

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑣𝑖𝑙𝑙𝑎𝑔𝑒𝑠 unconnected/𝑝𝑎𝑟𝑡𝑖𝑎𝑙𝑙𝑦 𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑒𝑑 𝑑𝑢𝑒 𝑡𝑜 𝑝𝑜𝑜𝑟 𝑟𝑜𝑎𝑑 𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛

(without considering population of unconnected villages)

Rural Inaccessibility Score

RI = Total population having no access to road in a region/ country (in terms of standard

distance used by IDA and PMGSY

𝑊i is the population of villages unconnected/partially connected due to poor road condition

𝑅𝑖 is weightage assigned based on the condition of the road network. If the condition of the road network

is unusable, a weightage of 0 will be adopted, where as if the condition is poor, weight of 0.5 can be adopted

𝑳𝒐𝑰 =𝑊 ∗ (𝐿𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 ℎ𝑦𝑝𝑜𝑡ℎ𝑒𝑡𝑖𝑐𝑎𝑙 𝑟𝑜𝑎𝑑 𝑛𝑒𝑡𝑤𝑜𝑟𝑘)


𝑖=1

2𝑊𝑖 ∗ (𝑅𝑖 ∗ 𝐿𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑟𝑜𝑎𝑑 𝑛𝑒𝑡𝑤𝑜𝑟𝑘 𝑢𝑛𝑢𝑠𝑎𝑏𝑙𝑒/𝑝𝑎𝑟𝑡𝑖𝑎𝑙𝑙𝑦 𝑢𝑠𝑎𝑏𝑙𝑒 𝑑𝑢𝑒 𝑡𝑜 𝑝𝑜𝑜𝑟 𝑟𝑜𝑎𝑑 𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛)


𝑊 is the inaccessible population within the region/block

(With considering the population of the unconnected habitations)

Developing Hypothetical Road Network for connecting unconnected habitations

Length of hypothetical network required to connect

inaccessible habitations is considered as a

component to measure level of inaccessibility

RIS = [w1(RIscaled) + w2(LoIscaled)]

• Where, RI is the rural inaccessibility, which is the total inaccessible population in a regionand RIscaled is the score given on a scale between10-100.

• Range of each scale will depend on the maximum, minimum and range of RI valuesobtained in the study.

• Similarly, LoI calculated is scaled between 10 and 100 (LoIscaled )

• w1 and w2 are the weights the decision maker would like to put on Rural Inaccessibility (RI)and Level of Inaccessibility (LoI) respectively and the summation of w1 and w2 will be 1.

Computation of RIS (Rural Inaccessibility Score)

Ex: If the decision maker would like to give equal emphasis on populationhaving no access and level of inaccessibility, (i.e. w1 = w2 = 0.5)

RIS = [0.5 x (RIscaled) + 0.5 x (LoIscaled)]

After computing RIS, decision maker may decide funding priorities based on

the relative score of each region.

Emphasis on the components in determining RIS

Framework to determine RIS using GIS

Digitization of habitations and existing road network

Creation of buffer along the existing road network

Classification of accessible and inaccessible population

Strength of inaccessible

population

Compute the length of road network required

to connect inaccessible habitations

Scaling of inaccessible

population

Evaluation of level of inaccessibility and scaling

of evaluated level of inaccessibility

Evaluation of Rural

inaccessibility Score

Total inaccessible population

and

Inaccessible no of habitations

Length of hypothetical

Road network

Case Study in Rajasthan (India)

Five districts are considered

BIKANER

CHURU

TONK

ALWAR

JHUNJHUNUN

0 100 20050 Kilometers

Not considered for study Desert areas Plain areas

B3

B4B6

B5

B1 B2


J8

J7

J5J4

J1

J6

J2J3


BIKANER

CHURU

TONK

ALWAR

JHUNJHUNUN


Not considered for study Desert areas Plain areas

C4

C5

C2C6

C3

C1

0 100 20050 KilometersC4

C5

C2C6

C3

C1

A14

A9

A1

A12 A6

A7

A3

A4

A13

A10

A8A2

A5

A11


C4

C5

C2C6

C3

C1

T5T2

T1

T4

T3

T6

A14

A9

A1

A12 A6

A7

A3

A4

A13

A10

A8A2

A5

A11


Population:2367745Area (Sq.km):30292No of habitations:850

Population: 2041172Area (Sq.km):13826No of habitations:852

Population:2139658Area (Sq.km): 5923No of habitations:854

Population:1421711Area (Sq.km):7184No of habitations:1003

Population: 3671999Area (Sq.km): 8385No of habitations:2217

Bikaner (B1-B6)

Churu (C1-C6)

Jhunjhunu (J1-J6)

Alwar (A1-A14)

Tonk (T1-T6)

Data drafted by Harish puppala and A.K.Sarkar

Jhunjhunu (J1-J8)

Tonk

Jhunjhunu

Alwar

Bikaner

Churu

Example: Block-wise Rural Habitations of Bikaner District

Demographic data of Alwar District (14 Blocks)

District Block Identity Block nameTotal

population

Total no. of

habitationsArea (Km2)

Alwar

1 A1 Bansur 172903 132 670.5

2 A2 Behror 125774 100 355.7

3 A3 Kathumbar 196467 170 554.1

4 A4 KishanGarh Bas 132632 170 527.7

5 A5 Kotkashim 106116 123 334.9

6 A6 Lachmangarh 188005 199 602.3

7 A7 Mundawar 164351 166 573.9

8 A8 Nimrana 134088 93 390.6

9 A9 Rajgarh 90854 165 699.2

10 A10 Ramgarh 169613 225 596.5

11 A11 Reni 102013 113 411.8

12 A12 Thanaganzi 128534 165 878.7

13 A13 Tijara 175798 216 671.3

14 A14 Umren 154030 180 1111.4

Demographic data of Bikaner and Churu Districts (6 Blocks in each district)


population

Total no. of


Bikaner

1 B1 Bikaner 232616 149 3766.4

2 B2 Dungargarh 211611 196 2985.5

3 B3 Kolayat 275524 137 7944.0

4 B4 Lunkaransar 198871 112 6347.8

5 B5 Nokha 183977 96 3792.7

6 B6 Khajuwala 215974 159 5454.9

Churu

1 C1 Churu 146765 105 1586.1

2 C2 Rajgarh 256405 212 2200.7

3 C3 Ratangarh 148718 98 1692.3

4 C4 Sardarshar 216429 169 3837.6

5 C5 Suiangarh 221608 154 2693.8

6 C6 Taranagar 125849 89 1814.8

Demographic data of Jhunjhunu District

(8 Blocks)


population

Total no. of


Jhunjhunu

1 J1 Alsisar 138731 124 809.821

2 J2 Buhana 190405 125 640.136

3 J3 Chirawa 133899 83 484.458

4 J4 Jhunjhunu 154475 145 811.481

5 J5 Khetri 121310 86 813.291

6 J6 Nawalgarh 185895 87 691.368

7 J7 Surajgarh 165239 122 822.610

8 J8 Udaipurwati 154495 82 850.859


population

Total no of


Tonk

1 T1 Deoli 118151 140 1218.772

2 T2 Malpura 135622 128 1486.765

3 T3 Niwai 120616 191 982.710

4 T4 Todaraisingh 85269 111 997.875

5 T5 Tonk 173376 238 1533.466

6 T6 Uniara 88652 195 964.496

Demographic data of Tonk Districts (6 Blocks)

Example: Road network required to connect inaccessible habitations in Bikaner

Sample calculation for estimating LoI

For a better understanding of the proposed methodology, a detailed calculation in evaluating RIS is shown below for one of the blocks

For demonstration, Nimrana in Alwar district is considered for determining LoI.

Required road length to connect (Length of hypothetical network required) = 22.34 km

Existing length of road network = 62.6 km

Number of unconnected/inaccessible habitations = 19

𝑳𝒐𝑰 =(22.34 ∗ 1000)

19= 1176

Note: This study assumed that the existing road network is in good condition and thus the second terms is

irrelevant (for this study only) and the load length is considered in meters in evaluating LoI

𝑳𝒐𝑰 =(𝐿𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 ℎ𝑦𝑝𝑜𝑡ℎ𝑒𝑡𝑖𝑐𝑎𝑙 𝑟𝑜𝑎𝑑 𝑛𝑒𝑡𝑤𝑜𝑟𝑘)


𝑖=1

2(𝑅𝑖 ∗ 𝐿𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑟𝑜𝑎𝑑 𝑛𝑒𝑡𝑤𝑜𝑟𝑘 𝑢𝑛𝑢𝑠𝑎𝑏𝑙𝑒/𝑝𝑎𝑟𝑡𝑖𝑎𝑙𝑙𝑦 𝑢𝑠𝑎𝑏𝑙𝑒 𝑑𝑢𝑒 𝑡𝑜 𝑝𝑜𝑜𝑟 𝑟𝑜𝑎𝑑 𝑐𝑜𝑛𝑑. )


(without considering population of unconnected villages)

Sample calculation for estimating LoI

For a better understanding of the proposed methodology, a detailed calculation in evaluating RIS is shown below for one of the blocks

For demonstration, Nimrana in Alwar district is considered. However, the equivalent results

for other blocks are shown in the subsequent slides

Inaccessible population in Nimrana = 10081

Required road length to connect (Length of hypothetical network required) = 22.34 km

Existing length of road network = 62.6 km

Number of unconnected/inaccessible habitations = 19

𝑳𝒐𝑰 =10081∗(22.34)

19= 11853

Note: This study assumed that the existing road network is in good condition and thus the second terms is

irrelevant (for this study only)

𝑳𝒐𝑰 =𝑊 ∗ (𝐿𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 ℎ𝑦𝑝𝑜𝑡ℎ𝑒𝑡𝑖𝑐𝑎𝑙 𝑟𝑜𝑎𝑑 𝑛𝑒𝑡𝑤𝑜𝑟𝑘)


𝑖=1

2𝑊𝑖 ∗ (𝑅𝑖 ∗ 𝐿𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑟𝑜𝑎𝑑 𝑛𝑒𝑡𝑤𝑜𝑟𝑘 𝑢𝑛𝑢𝑠𝑎𝑏𝑙𝑒/𝑝𝑎𝑟𝑡𝑖𝑎𝑙𝑙𝑦 𝑢𝑠𝑎𝑏𝑙𝑒 𝑑𝑢𝑒 𝑡𝑜 𝑝𝑜𝑜𝑟 𝑟𝑜𝑎𝑑 𝑐𝑜𝑛𝑑. )


(Considering population of unconnected habitations)

• After evaluating total population having no accessibility of all theadministrative blocks (39 in five Districts in this case), considering theminimum and maximum values, they were scaled between 10-100.(Blocks with higher population with no accessibility are given higher valuesin scale): RIscaled.

• Similarly, the computed LoI values in percent are also graded on a scale of10-100 (higher value means higher preference) and the scaled values arereferred as LoIscaled.

Grouping, Scaling of RI & LoI and Evaluation of RIS considering population of unconnected habitations

(scaling with Equal interval)

Score

RI LoI

Number of blocks Number of blocks

Equal intervals Equal intervals

10 29 28

20 5 9

30 2 0

40 1 0

50 1 0

60 0 1

70 0 0

80 0 0

90 0 0

100 1 1

RI- Grouping LoI-Grouping Score

Group-1 0 11275 0 16790 10

Group-2 11276 22434 16791 33396 20

Group-3 22435 33593 33397 50002 30

Group-4 33594 44753 50003 66607 40

Group-5 44754 55912 66608 83213 50

Group-6 55913 67071 83214 99819 60

Group-7 67072 78231 99820 116424 70

Group-8 78232 89390 116425 133030 80

Group-9 89391 100549 133031 149636 90

Group-10 100550 111709 149637 166242 100

Equal intervalId Block RI Score LoI Score RIS (Equal) Id Block RI Score LoI Score RIS (Equal)

B1 Bikaner 20 20 20 J3 Chirawa 10 10 10

B2 Dungargarh 30 20 25 J4 Jhunjhunu 10 10 10

B3 Kolayat 50 100 75 J5 Khetri 10 10 10

B4 Lunkaransar 10 20 15 J6 Nawalgarh 20 20 20

B5 Nokha 30 20 25 J7 Surajgarh 10 10 10

B6 Khajuwala 100 20 60 J8 Udaipurwati 10 10 10

T1 Deoli 10 10 10 A1 Bansur 20 20 20

T3 Niwai 10 10 10 A2 Behror 10 10 10

T4 Todaraisingh 10 10 10 A3 Kathumbar 10 10 10

T5 Tonk 10 10 10 A4 KishanGarh Bas 20 10 15

T6 Uniara 10 10 10 A5 Kotkashim 10 10 10

C1 Churu 10 10 10 A6 Lachmangarh 20 20 20

C2 Rajgarh 40 60 50 A7 Mundawar 10 10 10

C3 Ratangarh 10 10 10 A8 Nimrana 10 10 10

C4 Sardarshar 10 10 10 A9 Rajgarh 10 10 10

C5 Suiangarh 10 20 15 A10 Ramgarh 10 10 10

C6 Taranagar 10 10 10 A11 Reni 10 10 10

J1 Alsisar 10 10 10 A12 Thanaganzi 10 10 10

J2 Buhana 10 10 10 A13 Tijara 10 10 10

A14 Umren 10 10 10

Evaluated RIS (Equal weightage for RI and LoI)

0

10

20

30

40

50

60

70

80

RIS (Equal)

RIS

(E

qu

al)

Blocks

Most of them are falls in

the same groups,

• Results show that the scaling failed to classify the Blocks widely

• A large number of Blocks (27 out of 39) have a score of 10.

• K-means clustering, which is one of the popular clustering techniques is adopted to divide the range of evaluated values into 10 different groups and the weights are assigned accordingly, ranging between 10-100

Grouping, Scaling of RI & LoI and Evaluation of RIS (without considering population of unconnected habitations)

Score

RI LoI


Equal intervals Equal intervals

10 3 1

20 6 8

30 13 7

40 8 11

50 2 2

60 2 3

70 2 3

80 1 1

90 1 1

100 1 2


Group-1 116 234 289 289 10

Group-2 926 2313 653 790 20

Group-3 3867 7119 881 1006 30

Group-4 7902 12421 1027 1219 40

Group-5 13987 18275 1431 1445 50

Group-6 20668 21569 1526 1605 60

Group-7 27349 32312 1693 1850 70

Group-8 38796 38796 2317 2317 80

Group-9 51214 51214 2733 2733 90

Group-10 111709 111709 3246 3310 100

K-meansId Block

RI

Score LoI Score RIS Id Block RI Score LoI Score RIS







T1 Deoli 20 20 20 A1 Bansur 40 60 50

T3 Niwai 20 20 20 A2 Behror 30 30 30











J2 Buhana 10 20 15 A13 Tijara 40 40 40

A14 Umren 30 40 35


Grouping, Scaling of RI & LoI and Evaluation of RIS (with considering population of habitations)

Score

RI LoI


K-means K-means

10 4 9

20 6 9

30 13 6

40 8 4

50 1 3

60 2 3

70 2 1

80 1 2

90 1 1

100 1 1


Group-1 116 926 184 2205 10

Group-2 1285 3867 3428 6399 20

Group-3 4276 7902 7690 9702 30

Group-4 8524 13987 10591 13551 40

Group-5 18275 18275 16945 19461 50

Group-6 20668 21569 21461 24218 60

Group-7 27349 32312 28112 28112 70

Group-8 38796 38796 32084 32371 80

Group-9 51214 51214 89918 89918 90

Group-10 111709 111709 166242 166242 100

K-means grouping Id Block RI Score LoI Score RIS (K-means) Id Block RI Score LoI Score RIS (K-means)







T1 Deoli 20 10 15 A1 Bansur 40 50 45

T3 Niwai 20 10 15 A2 Behror 30 20 25











J2 Buhana 10 10 10 A13 Tijara 40 40 40

A14 Umren 30 30 30

0

10

20

30

40

50

60

70

80 RIS(K-means)

RIS

(K

-me

an

s)

Blocks

Dispersion can easily be seen


Comparison of results obtained by equal grouping and K-means

0

10

20

30

40

50

60

70

80 RIS(K-means)

RIS

(K

-me

an

s)

Blocks0

10

20

30

40

50

60

70

80

RIS (Equal)

RIS

(E

qu

al)

Blocks

Owing to the advantage of granularity which helps to distinguish regions, grouping analysis is

performed using K-means algorithm in this study.

• It is always simple if standard scales are used instead of using statistical techniques

• A limited study has been carried out in a few regions in Rajasthan and thus data pointsare limited,

• If similar studies are carried out in different regions and countries, large amount of datawill be available, and a standard scale may be developed for RI and LOI. (in such a caseit may not be necessary to use K-means algorithm).

Developing a standard scale

Maximum possible RI value

Least possible RI value

Group-1

Group-2

Group-5

Group-3

Group-4

Group-6

Group-7

Group-8

Group-9

Group-10

Groups with equal interval

Similar grouping i.e. division of

RI and LoI into groups with

equal interval will be adopted

ID BlockRIS (without

considering

population)

RIS(considering

population)

ID BlockRIS (without

considering

population)

RIS(considering

population )

B1 Bikaner 60 65 J3 Chirawa 40 25

B2 Dungargarh 45 65 J4 Jhunjhunu 30 20

B3 Kolayat 95 95 J5 Khetri 20 15

B4 Lunkaransar 60 40 J6 Nawalgarh 50 60

B5 Nokha 55 70 J7 Surajgarh 40 30

B6 Khajuwala 55 90 J8 Udaipurwati 55 10

T1 Deoli 20 15 A1 Bansur 50 45

T3 Niwai 20 15 A2 Behror 30 25

T4 Todaraisingh 40 10 A3 Kathumbar 40 30

T5 Tonk 20 15 A4 KishanGarh Bas 40 40

T6 Uniara 25 25 A5 Kotkashim 30 25

C1 Churu 45 30 A6 Lachmangarh 50 60

C2 Rajgarh 80 85 A7 Mundawar 35 25

C3 Ratangarh 30 10 A8 Nimrana 40 40

C4 Sardarshar 25 25 A9 Rajgarh 40 35

C5 Suiangarh 50 45 A10 Ramgarh 35 40

C6 Taranagar 45 15 A11 Reni 30 25

J1 Alsisar 35 25 A12 Thanaganzi 35 35

J2 Buhana 15 10 A13 Tijara 40 40

A14 Umren 35 30

Comparison of RIS without and with considering population of

unconnected villages

Impact of changing weights on RI and LOI on RIS

Id Block RIS Id Block RIS

B1 Bikaner 71 J4 Jhunjhunu 20

B2 Dungargarh 63 J5 Khetri 13

B3 Kolayat 97 J6 Nawalgarh 60

B4 Lunkaransar 44 J7 Surajgarh 30

B5 Nokha 70 J8 Udaipurwati 10

B6 Khajuwala 86 A1 Bansur 47

T1 Deoli 13 A2 Behror 23

T3 Niwai 13 A3 Kathumbar 30

T4 Todaraisingh 10 A4 KishanGarh Bas 40

T5 Tonk 13 A5 Kotkashim 23

T6 Uniara 23 A6 Lachmangarh 60

C1 Churu 30 A7 Mundawar 23

C2 Rajgarh 87 A8 Nimrana 40

C3 Ratangarh 10 A9 Rajgarh 33

C4 Sardarshar 23 A10 Ramgarh 40

C5 Suiangarh 47 A11 Reni 23

C6 Taranagar 13 A12 Thanaganzi 33

J1 Alsisar 23 A13 Tijara 40

J2 Buhana 10 A14 Umren 30

J3 Chirawa 23

RI (0.3), LoI (0.7) RI (0.2), LoI (0.8)





















J3 Chirawa 22

More emphasis on “LoI”

Impact of changing weights on RI and LOI on RIS

RI (0.7), LoI (0.3) RI (0.8), LoI (0.2)





















J3 Chirawa 27





















J3 Chirawa 28

More emphasis on “RI”

Variation of priority with RAInew and RIS (with equal weight on RIand LOI)

Block Id Block Name RIS RAI Block Id Block Name RIS RAI

B3 Kolayat 1 3 J7 Surajgarh 21 27

B6 Khajuwala 2 1 A5 Kotkashim 22 16

C2 Rajgarh 3 2 J1 Alsisar 23 21

B2 Dungargarh 4 4 C4 Sardarshar 24 28

B5 Nokha 5 5 A7 Mundawar 25 29

B1 Bikaner 6 10 J3 Chirawa 26 26

J6 Nawalgarh 7 6 T6 Uniara 27 14

A6 Lachmangarh 8 7 A11 Reni 28 18

A1 Bansur 9 13 A2 Behror 29 23

A4 KishanGarh Bas 10 8 J4 Jhunjhunu 30 30

C5 Suiangarh 11 19 T1 Deoli 31 31

B4 Lunkaransar 12 24 J5 Khetri 32 32

A10 Ramgarh 13 15 C6 Taranagar 33 34

A8 Nimrana 14 11 T5 Tonk 34 35

A13 Tijara 15 17 T3 Niwai 35 33

A9 Rajgarh 16 9 C3 Ratangarh 36 36

A12 Thanaganzi 17 12 J8 Udaipurwati 37 39

A3 Kathumbar 18 22 J2 Buhana 38 38

A14 Umren 19 20 T4 Todaraisingh 39 37

C1 Churu 20 25

It can be interpreted that for few of the regions, there is substantial deviation in the ranking for few of the

regions and other Blocks the variation the deviation in hierarchy is observed to be considerably negligible

and absent. This deviation is due to considering the hardships faced by inaccessible people.

Conclusion

• A new and logical approach has been suggested in which the difficulty levelsof access to unconnected habitations have been considered.

• It captures both population not having access to roads and level ofinaccessibility (difficulty level) would benefit in determining true ranking ofregions for fund allocation by international funding agencies.

• Scope for incorporating the road condition of the existing network has alsobeen incorporated for determining LOI.

• In some cases the population having no access to road may be high, but thedifficulty level associated in making them accessible may be low. In someother cases the contrary is true. The suggested method will be able toprioritize them based on the weights put by the decision makers on RI andLOI.

Scope for Further studies to refine the suggested method

• For calculating LOI values, only population has been considered. Thefacilities available in a village may also be given weight and a compositeweight may be considered.

• The study needs to be carried out in different areas having variation interrain and density of villages.

• Standard scales for determining RI and LOI are to be developed by carryingout a large number of studies.

• To help decision makers, a tool can be developed in ArcGIS which wouldhelp to determine RIS and to update the value periodically with the availableinformation of condition of road network.

THANK YOU

Ashoke. K. Sarkar

BITS Pilani

Rajasthan, India

Email: [email protected]

Bibliography• Bell, C., and S. van Dillen. How Does India’s Rural Roads Program Affect the Grassroots? Findings from a

Survey in Orissa. 2012.

• Dercon, S., J. Hoddinott, and T. Woldehanna. Growth and Chronic Poverty: Evidence from Rural Communities inEthiopia. Journal of Development Studies, Vol. 48, No. 2, 2012, pp. 238–253.https://doi.org/10.1080/00220388.2011.625410.

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• Luo, W., and Y. Qi. An Enhanced Two-Step Floating Catchment Area (E2SFCA) Method for Measuring SpatialAccessibility to Primary Care Physicians. Health & Place, Vol. 15, No. 4, 2009, pp. 1100–1107.https://doi.org/10.1016/j.healthplace.2009.06.002.

• Puppala, H., S. Kanuganti, S. Ashoke Kumar, and S. Ajit Pratap. Quantification of Ideal Spatial AccessibilityUsing Three Step Floating Catchment Area Method. Presented at the Method96th Annual meeting ofTransportation Research Board, Washington, D.C, 2017.

Bibliography (contd..)

• Guagliardo, M. F. Spatial Accessibility of Primary Care: Concepts, Methods andChallenges. International Journal of Health Geographics, 2004, p. 13.

• Kanuganti, S., A. K. Sarkar, and A. P. Singh. Evaluation of Access to Health Care in RuralAreas Using Enhanced Two-Step Floating Catchment Area (E2SFCA) Method. Journal ofTransport Geography, Vol. 56, 2016, pp. 45–52.https://doi.org/10.1016/j.jtrangeo.2016.08.011.

• Kanuganti, S., B. Dutta, A. K. Sarkar, and A. P. Singh. Development of a Need-BasedApproach for Rural Road Network Planning. Transportation in Developing Economies,Vol. 3, No. 2, 2017. https://doi.org/10.1007/s40890-017-0044-y.

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• Roberts, P., S. KC, and C. Rastogi. Rural Access Index: A Key Development Indicator.2006, p. 49.

• Limi, A., F. Ahmed, E. C. Anderson, A. S. Diehl, L. Maiyo, T. Peralta-Quirós, and K. S.Rao. New Rural Access Index: Main Determinants and Correlation to Poverty. 2016

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