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Available Online at Volume 5; Issue 4; July-August: 2017; Page No ISSN: 2348 - 2117 International Journal of Engineering Technology and Computer Research (IJETCR) Available Online at www.ijetcr.org Volume 5; Issue 4; July-August: 2017; Page No. 100-107 Journal Approved by UGC Rural Road Network Connectivity Analysis Based on GIS Kamlesh Sah1, Ch. Ramesh Naidu2 PG Scholar, Dept. of Civil Engg, GVPCOE(A), Visakhapatnam, India Professor, Dept. of Civil Engg., GVPCOE(A), Visakhapatnam, India [email protected] Received 29 July 2017; Accepted 17 August. 2017 Abstract Rural roads are considered to be one of the important roads within a district, which connects areas of production with markets and with other villages or with the state and National Highways. Rural road network connectivity is perceived as one of the prime components in increasing the agricultural output and earning capacity of the rural population. Rural roads stimulate overall development by providing access to social infrastructure and facilities. The study area is located in Visakhapatnam district, Andhra Pradesh, India. An attempt has been made through the present study to develop Geographic Information System (GIS) based rural road database and also network connectivity analysis (Beta Index, Alpha Index, Gamma Index, Cyclomatic Number and Aggregate Transportation Score) has been performed. The analysis result several that good level of connectivity of roadways in the selected study area. Keywords: Road network, Connectivity, Analysis, GIS route planning, transportation system modeling, INTRODUCTION healthcare accessibility planning, land cover The role of rural roads is very important in a country classification and even infrastructure management. like India where majority of the population resides in Road networks play an important role in many rural areas. Rural road connectivity is a key geospatial applications, such as cartographic, component of rural development by promoting infrastructure planning, etc. access to economic and social services. Rural roads OBJECTIVES provide the access to basic amenities and means of transporting agricultural products to nearest market • Collection of primary data, secondary data and centers. The rural roads can be classified as Other preparation of database inGIS. District Roads (ODR) and Village Roads (VR). • To develop spatial and attribute database for the A GIS database is quite suitable for planning, road network of the studyarea. constructing and monitoring of rural roads since all • To understand the existing rural road network of the relevant data in this case is geographically the municipality in terms of connectivity referenced. GIS makes it very easy to store, analyze anddevelopment. and present geographically referenced data. The THE STUDY AREA implementation of GIS can not only reduce the time needed for analyzing information but also can ensure Bheemunipatnam Municipality is considered for the a more efficient use of the resource with high study area which is located inVisakhapatnam district, flexibility in time and scale. It enables user to store Andhra Pradesh, India. Geographically it is situated and display large amount of data graphically to between 17.53N to 83.26E. It is the second oldest greatly enhance the interpretation and analysis. In municipality in India, established on 8 February 1861. the GIS platform, the database of transportation This Place is in the border of the Visakhapatnam network is normally extended by integrated with District and Vizianagaram District. It is 24 km far from spatial and attribute data. District headquarters Vishakhapatnam. Road data enables GIS applications to facilitate a The total geographical area is 18.88 Km2 and the variety of services which include satellite navigation, total population of the municipality is 51,984 (Census Report, 2011) with the population density of 2757 Corresponding author: Kamlesh Sah 100 Kamlesh Sah, International Journal of Engineering Technology and Computer Research (IJETCR) persons per km2.There are about 27 thousand (52%) the most populous ward with population of 3486. It are female and about 25 thousand (48%) are male. has an average elevation of 51meters. 90% of the whole population are from general caste, Adorable Bay of Bengal on one side, riveting river 10% are from schedule caste and 0% are schedule Gosthani on the other and captivating Red Sand tribes. Child (aged under 6 years) population of dunes on the third gracing this magnificent piece of Bheemunipatnam Municipality is 10%, among them land Bhimili. It is surrounded by Padmanabham 51% are boys and 49% are girls. There are about 13 Mandal towards North, Anandapuram Mandal thousand households in the city and an average 4 towards west, Chinagadila Mandal towards South, persons live in every family. There are 28 wards in the Bhoghapuram Mandal towards East. city, among them Bheemunipatnam Ward No 24 is Fig. 1: Location map of Bheemunipatnam The Bheemunipatnam municipality consists of one town and five villages. They are: Town – Bheemili. Villages – Chittivalasa, K.V. Bhumulu, Kummaripalem, Sangivalasa and Thagarapuvalasa. There are total 73km roads in Bheemunipatnam municipality. Maximum temperature here reaches up to 32°C and minimum temperature goes down to 20°C. It is hot in summer. Average temperatures of January is 26 °C, February is 26 °C, March is 28 °C, April is 29 °C & May is 31 °C .Yearly average rainfall is 879.87 mm. Methodology Fig. 2: Research Methodology © 2017 IJETCR. All Rights Reserved. 101 Kamlesh Sah, International Journal of Engineering Technology and Computer Research (IJETCR) Literature Review hospitals using GIS in that city. Bharati Gogoi (2013) worked on structural analysis of existing road Vinod R V et al. (2003) has calculated the network networks of Assam. The author has calculated the connectivity indices using GIS in Kasaragod taluk, network connectivity indices such as road density, Kerala. They identified poor connectivity of road alpha index, beta index, gamma index, cyclomatic network in that study area and worked for its number and aggregate transportation score of 27 improvement. R.Nijagunappaetal. (2007) worked districts of Assam and identified that study area has on various modules of Network analyst tool in ARC minimum road density and most of the districts are GIS software like network tracing, path analysis and characterized by minimum efficiency of road found solutions to different road network problems network in terms of connectivity. The efficiency of of Dehradun city. Arora.A et al. (2011) studied on road network is very low and higher spatial the analysis of road network for services like ATMS imbalance. of different banks and hospital to find solutions for routing problems related to accessibility, rate of Road Network Connectivity Analysis flow and network connectivity for south west part The connectivity of a network may be defined as of Delhi. Adesola.A et al. (2011) studied on road the degree of completeness of the links between network density and connectivity for Nigeria. They nodes of a network which are directly connected to identified that the study area was good in each other. Also, it means that the connectivity of a transportation facilities because of good network may be defined as the degree of connectivity index values and high road network connection between all vertices by arcs 'links' density. Sarkar, D. (2013) carried out structural (Robinson and Bamford 1978). analysis of existing road networks of Cooch Behar District, west Bengal. The author has calculated Kansky (1963) has studied the structure of the network connectivity indices such as alpha transportation networks and provided a number of index, beta index, gamma index, cyclomatic number indices which can be used for this purpose. There and aggregate transportation score of 12 blocks of are several methods that can be used to measure Cooch Behar District and identified that nine blocks the degree of connectivity; including Beta, Gamma, namely Mathabhanga II, Haldibari, Tufangang II, Alpha indices &Cyclomatic number (Taaffe and Sitai, Sitalkuchi, Mathabhanga I, Cooch Behar I and Gauthier, 1973 and Davis, 1974).There are many Cooch Behar II have lower development of Indices and every Index has its own equation for transportation in terms of connectivity and have different purposes. For extraction of connectivity least efficient road network in thedistrict. index,it requires a road network (line), junctions (nodes). These indices are useful to identify the Kumar et al. (2013) worked on road connectivity growth within network structure and for change and network analysis for Varanasi city. They found detection system. These may be calculated the optimal paths for providing services like asfollows. Table 1: Formula for road network connectivity Indices S. No. Indices Formula Correlation with Connectivity 1. Road Density Higher the density higher the . development ℎ 2. Beta Index β = Higher value indicates more connectivity 3. Alpha Index +1 Higher value indicates more connectivity α= 2 5 − 4. Gamma Index − Higher value indicates more connectivity γ = 3( 2) 5. Cyclomatic Number µ = −+ 1 Higher value indicates more connectivity 6. Aggregate Transportation ATS = β −+ α + γ + µ Higher value indicates more connectivity Score (ATS) and efficiency © 2017 IJETCR. All Rights Reserved. 102 Kamlesh Sah, International Journal of Engineering Technology and Computer Research (IJETCR) RESULT AND DISCUSSION Road Density Density of road network indicates the length of road per unit of geographical area, which discerns the
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