A Study of the Urbanizing Pattern in Kegalle District, Sri Lanka with Connectivity Analysis

A.B.Jayasinghe* & Dr. Jagath Munasinghe** *Lecturer, Department of Town & Country Planning, University of Moratuwa, Sri Lanka & **Head, Department of Town & Country Planning, University of Moratuwa

ABSTRACT

In any geography, it is observable that locations better connected to many other locations attract urban activities and therefore, are in relatively more advantageous positions than the less connected locations in the competition for growth. In that sense, a thorough analysis of the connectivity of different locations in a region will enable regional planners to evolve workable spatial strategies for the development of that region. The study presented in this paper is a test of this relationship between the trends of urban agglomerations and the patterns of connectivity of the urban centres in Kegalle district of Sri Lanka. The study was based on the node-axial diagrams derived from road maps of the region, in which the axial nodes represent urban centres. The relative connectivity of the axial nodes was computed at local and global levels in terms of their accessibility from all other nodes in the area selected for the purpose. The prevailing states of urban agglomerations of the centres were evaluated in terms of the availability of urban facilities, commercial, trade and informal activities in those centres. The relative connectivity values are correlated with the urban agglomeration values to test the relationship between the two. The results indicate a high possibility of connectivity analysis to predict the urban agglomeration trends in a region.

Keywords: Urban Agglomeration, Accessibility, Connectivity, Regional Planning

BACKGROUND

Urbanization changes the state of affairs in a region applications such as ‘rank-size rule’, ‘centrality more than any other force. Hence, when planning a matrix’ and other techniques. What these techniques region, the prevalent patterns of urbanization in that deal more with is the relative positioning of urban region and its surroundings receive higher attention locations at a given time, rather than their potentials of the planners. Urbanization generally implies a for long-term growth. At the same time, their continuous flow of populations into urban areas and applications do not lead to understand the evolving thus, a continuous growth of urban activities in those patterns in the spatial structure of a region (Glasson, areas. Yet, the urbanization is not a uniform process 1978). Further, they integrate some subjectivity, for that results in an even distribution of activities and which differences in preferred criteria for ranking populations all over the region. Some locations could deliver different results (McEvay, cited in agglomerate more urban activities than the others and Glasson, 1978). These limitations often leave thus grow faster, resulting in an inequality among planning practices in difficult situations in selecting urban areas in the region. Although this is a known strategic locations for urban investments. For phenomenon, this inequality of agglomeration among example, many of the urban areas identified as urban areas is something that has not been adequately district capitals and higher order towns in regional investigated so far in regional planning literature. plans in Sri Lanka, are being outranked by the Instead, urban location decisions in contemporary agglomeration and the growth of other towns in their regional planning practices mostly rely upon vicinities, which have been noted as lower order

February 2013. Vol. 2, No.1 ISSN 2305-1493 International Journal of Scientific Knowledge Computing and Information Technology © 2012 IJSK & K.A.J. All rights reserved www.ijsk.org urban locations. Parallel to these an emergence of urban corridors along major arteries centering In order to travel for any purpose, people usually unexpected locations, were unprecedented in regional tend to take the most economic path towards their destination, which is generally termed as movement development plans prepared within last thirty years. economy. The most economic means of accessibility The situation reveals that there are many critical of a location depends upon the extent to which that aspects that the regional planning attempts have so location is connected to its surroundings through far not adequately taken into consideration. In this some reasonable means of transportation. Hence, out paper we argue that ‘connectivity’ is crucial for of many factors associated with urbanization, this locations to gain higher urban agglomeration study investigates the potential of the ‘connectivity’ potentials in a long-term growth in an open of a given location to all other locations in the region through road transportation. competition among the locations in a region. To substantiate this argument, it presents the preliminary ‘Connectivity’ is a subject matter of interest for findings of a long-term study that explored a possible many fields of studies, and Connectivity Analysis is relationship between the urban agglomeration trend popular in information technology, computer and the connectivity pattern of locations in Kegalle engineering, etc. Its recent applications can be seen in district of Sri Lanka. spatial planning to model, forecast, and explain matters related to accessibility. Connectivity Analysis could be performed in many different forms (such as INTRODUCTION simple connectivity analysis, weighted network The research presented in this paper is based on a analysis...etc.), and highly advanced sophisticated few commonly adopted postulations in urban and mathematical operations could be used to compute regional planning, which can be briefly stated in the and explain the results related to connectivity. But, in following manner. this study, only the simple connectivity analysis is applied to measure relative connectivity of selected Any urban activity sustains only when there is a locations. The locations selected for the analysis are threshold population to support that activity, for the nodes established by the intersection of main which firms tend to locate their businesses in places roads within the region. central to a substantial catchment of population. Hence, the size of the population that can be served is ‘Urbanization’ is a term that has multiple a determinant of a location’s potential to attract urban connotations, and in urban and regional planning activities into that. This is generally understood with context, urbanization is generally used to explain the the economies of scale in planning context. On the phenomenon of increasing concentration of other hand, an agglomeration of a variety of urban populations into areas for non-agriculture based activities determines to what extent that location activities. The level and the size of agglomeration of could attract people into that. Hence, the urban activities in the central locations of those areas agglomeration of urban activities and the population can be termed as the level of ‘urbanizing’, which is size of the area are mutually reinforcing each other. generally reflective of the trends of urbanization. However, for each type of goods and services, people Hence, apart from sizing the population, this study are not willing to travel more than a specific distance, considers the measurements on the agglomeration of which is generally termed as the market range of that urban activities as one of the means of identifying the activity. When there is a market range proportioned level of urbanization. by a sufficient size of population spread within a reasonably accessible area, urban activities becomes STUDIES ON CONNECTIVITY sustainable. Hence, the businesses prefer locations in the most accessible places, so that they can attract as Erdos and Renvi’s (1960) ‘Random Graph’ model many as populations to serve. This is usually known can be considered as the base on which the most of as location economies of doing business. A factor the subsequent analysis on connectivity was crucial with this regard is the level of accessibility, in developed. In simple terms, the method involved is line with the popular saying in planning that: ‘higher the accessibility-greater the potential for the computation of relative connectivity among development’. systematically linked points, lines and areas. The

February 2013. Vol. 2, No.1 ISSN 2305-1493 International Journal of Scientific Knowledge Computing and Information Technology © 2012 IJSK & K.A.J. All rights reserved www.ijsk.org relative connectivity is measured in terms of the Instead of the ‘nodes’, considered in general number, distance, travel time, optimal path, etc. This connectivity studies, these studies accounted for the method has developed into the status of a connectivity of ‘streets’. In deciding the potential for attracting people, activities, land values, traffic, comprehensive technique with a number of inhabitant’s memory and many other factors for applications in many fields such as geography, thriving urban areas, the degree of connectivity of demography and economics. Barabasi and Albert streets to other streets in the urban area, which is (1999) studied connectivity of physical networks in interpreted with the concept of ‘integration’, is the relation to properties such as robustness and most critical factor according to these studies. vulnerability. Batty and Shiode’s (2000 and 2001) study promoted the development of this field into In summary, literature indicates that connectivity has been used as an attribute to measure many quantitative analysis within a twofold perspective aspects such as the accumulation of traffic on with special reference to the World Wide Web. intersections and concentration of people at urban Claremont and Jiang (2004) attempted to describe centers. Further, they show that the analysis of transportation networks by conceptualizing streets connectivity of a given a given location can be a into nodes and intersections into edges, and naming method to ascertain and predict the capacities of that the method ‘Dual Graph’. A few non-spatial network location in many fronts. Its applications in regional studies include topology analysis of modern planning will benefit planners by providing better understanding of the spatial dynamics of the region infrastructure and communication networks (Watts and enabling them to make more viable decisions. and Strogatz, 1998), the World-wide Web (Albert, 1999), the Internet (Faloutsos, 1999), and Biological networks (Jeong, 2000). THE STUDY Although as not widespread as applications in computer and related fields a few studies on the As stated earlier, the objective of this study is to connectivity of spatial networks, which has a direct test the capacity of the level of connectivity to assign relevance to urban and regional planning, can also be locations with urban agglomeration potentials. noted. The study of topology of the Indian railway Hence, the study consisted of three main steps. The network (Sen, 2003), the study on US Interstate first was the assessment of the relative connectivity highway network and the airport network (Gastner of different locations of the selected region. The and Newman, 2004) and study on the Italian power second was the measurement of agglomeration of grid (Crucitti et al., 2004) are examples for such urban activities in those locations, and the third was studies. The Barrat’s (2004) studies on ‘weighted to investigate a possible relationship between the network’ furthered the development of the conceptual findings of the first and the second. base associated with the connectivity analysis technique. ‘Weighted graph representation’ provided 4.1 Assessment of Connectivity a commendable solution for many existed limitations of the technique, and answered to a series of In order to analyze the connectivity of a set of questions that were fundamental to the understanding locations the network of all motorable roads of the of a spatial network. Among them, the relationship selected region, indicated in a map, is reduced in to a between the dynamics and the structure and their ‘node-axial’ diagram. The ‘nodes’ are where two or mutual effects on each other, and the impact of traffic more roads intersect with each other, and ‘axial’ are flow on the basic properties of spreading and the segments of roads between those nodes, congestion are important to be noted (Montis et al, represented as straight lines. The diagram resulting in 2007). Study of worldwide airport network including may also be called ‘axial map’ (figure-1). traffic flow and their correlation with the topological structure (Barrat's, 2004) introduced weighted graph This axial map is used to compute the ‘relative representation for spatial analysis. connectivity’ of each node. The relative connectivity The studies on Space Syntax research stream, is considered as the sum of the normalized values of emerging from Hillier’s (1996) work, are also based the relative connectivity of nodes, on the concept of connectivity, but at urban scale.

weights to the axial connections. The weight factors may be decided upon the distance between centers, travel difficulties, natural environmental barriers etc. However, as stated earlier, in this study only simple connectivity analysis is adopted with no weights assigned on the connections. Size of Operation Weight Local Level 1 Urban or Sub Region Level 2

computed in terms of the number of axial lines Region Level 3 that one has to pass through to get into the particular Table1: Weighted Value According to Level of Centers node selected from all other nodes. The computation was based on an ‘interactive matrix’ of nodes. The 4.2 Measuring the ‘Level of Urban connectivity value of each node is presented by the Agglomeration’ following formula.

The ‘level of urban agglomeration’ was measured

in terms of prevailing state of empirically observable

urban elements in each selected location. The urban

elements generally include: public utilities,

commercial and trade activities, size of commuting Dj : relative connectivity of the node ‘j’ population, number of vehicles arriving at the

location, size of informal activities, etc. Out of all Aij: level of accessibility/adjacency between node ‘i’ these, this study selected only the availability and the and ‘j’ level of public facilities, commercial activities and

servicers, and their sum value is considered as the Here, the virtual connectivity is still ‘relative’ sole indicator of the level of urban agglomeration. because the accessibility or adjacency depends on the Accordingly, an extensive fieldwork was carried out selected area of influences. The area of influence is to record all of these items in the urban centres, decided by setting up a radial distance from each which are considered as nodes in this study. Out of node in consideration. When the radial distance is the data recorded in all selected nodes (nodes and ‘n’, only the nodes that falls within the area their immediate vicinity) a classification of seventy- demarcated by that circle are taken into account for two activity types, related to the urban economy were the computation of connectivity of the node at the centre. The demarcation of the areas of influence is identified (Refer annexure-1). done at two scales, based on the authors’ First, the each and every activity along with their observations in the region. The first is the local level, size of operations was also recorded and mapped. depending on the area of attraction of the selected According to the size, recorded activities have been urban centre for immediate needs. The second is the recognized in three levels in a hierarchy, which are average distance to the boundaries of the region in termed in this study as: local level, urban level and consideration from the selected centre. The values regional level as given in table 1. derived for radius n: at regional level is called ‘Global Connectivity’ and values for radius n: at local level is called ‘Local Connectivity’. In the part of the Recorded activities at each node have been study presented here, only the Global Connectivity is categorized according to their type and then their taken into account. numbers were measured in a likert scale to assess their availability in a generalized manner. The The relative connectivity at the regions level, measurement was multiplied by the weighted value analyzed in this manner, can be considered as an as given in table 2. Thus, each and every activity unit indication of topological centrality of a node. This was multiplied by the weighted value, which computation can be made more effective to achieve represented the scale of operations of that activity. results with higher level of accuracy by assigning The value obtained in this manner indicated the

intensity of each activity type in the selected node. 6. THE ANALYSIS AND RESULTS The sum of values of all activity types at a particular node, named here as ‘Composite Urban 6.1 Level of Urban Agglomeration of the Nodes Agglomeration Value’ (CUAV), is considered as the (CUAVs) indicator of the degree urbanizing of that node. The CUAVs, which this study considered as the sole Accordingly, indicator of the level of urban agglomeration of a CUAV = ∑ [(Units measured in likert scale) x (Size of Operation)] node were computed out of the observations recorded in the field survey. According to the computations, Mawanella (2381) recorded the highest CUAV and THE STUDY AREA Kegalle recorded the second (2076). There is a significant gap between first two values and the third Kegalle district of Sri Lanka has been selected (Warakapola - 960). Therefore, Kegalle and for the study (figure-2), which is composed of a well Mawanella were considered as regional centers in spread road network and a wide spread of urban study area (figure-3). There were three locations, centers. Hence, it is considered as a suitable region for a project study of this nature. Since this is a whose CUAVs ranged from 700 to 1000, for which regional level analysis, study area need to have a they were categorized as urban centers. Next nine reasonable extent. As mentioned in many regional locations in line, which recorded values from 300 to planning studies, extent of the region should be 700, were categorized as town centers. Locations, smaller than the national geographic unit and larger which had CUAVs ranging from 100 to 300, were than local geographic units (Glasson, 1978). categorized as local centers and the rest, which had Accordingly, the selected area was one of the twenty- four districts of the country. Furthermore, the study values less area has heterogeneous topographical profile that enables to test applicability of the technique even under the effects of a complex topographical setting.

than 100 were considered as neighborhood centers. Figure 3. Distribution of ‘Composite Urban Agglomeration Values’ of nodes

Figure 1. Study Area - Kegalle District, Sri Lanka

Figure 4: Distribution of Location According to the CUAV value thus, represents the number of axial lines that connected the respective node indicated in the row with the node indicated in the column 6.2 Analysis of Relative Connectivity (Figure-6). As the initial step of the method of connectivity analysis, the layout of real road network was reduced into a ‘node-axis’ diagram. For this purpose, updated road maps that indicated all motorable roads (Class A, B & C) were used. Axial diagram was prepared extracting all nodes (where, two or more roads intersect with each other) of the selected region, Figure 6. Interactive Matrix representing the Number of and axial connections between them onto a Axial Line suitable surface. This node-axis diagram consisted of 196 nodes and 462 axial lines. A Once the matrix was composed, cell values in each column were normalized by dividing it by the separate set of 16 node-axis diagrams was sum value (∑Aij) of the column. The sum of prepared for a few selected major nodes to normalized cell values in a row: Ti was the item of measure local connectivity in the same method. consideration. The inverse of this value (Ti) represents the relative connectivity (RCi) of the node These diagrams were used compute virtual represented in that row (Figure-7). connectivity values of the nodes. In the method discussed above, the Relative Figure 5. Preparation of Node-Axial Diagram Using Connectivity of the nodes was analyzed. Since the district is an administrative unit, its boundaries do not necessarily define the functional linkages that the inhabitants maintain with adjacent area. If the study was confined to the administrative boundaries it might lead to contain some obvious faults in observations. Hence, the boundary of the study area had to be adjusted; expanding 15Km outside from the administrative boundary. Hence, the boundary of the study area had to be adjusted; expanding towards Kandy-Kurunagala road (A10) in the North and North-east, Awissawella-Ehaliyagoda road (A4) Motorable Road Network towards South, Ruwanwella-Nitambuwa road (B19) towards South-west and Ambepussa-Kurunagala road Then, the ‘interactive matrix’ was prepared to (A6) in North-west directions. Boundaries at the East compute the ‘connectivity’ of each node in terms and South-east directions were not expanded because of accessibility. Interactive matrix consisted of a Kadugannawa and Dolosbage (Gampola) mountain ranges have physically constrained the accessibility row and a column dedicated for each node. Then to the area beyond them. Then 196 nodes, which the interactive cells of the matrix was filled up located within the study area, were selected for with the sum of the number of axial lines that analysis. someone had to pass through to get into each node in the row, from each of the other nodes, represented in corresponding columns, in the most economic route (adjancy-Aij). The cell

inequalities among centers, are more or less well connected to each other.

Figure 7. Interactive Matrix representing the Relative Connectivity

Figure 9: Probability Distribution of Relative Connectivity

Figure 8: Nodes Analyzed for Connectivity

According to the results, the values of the relative connectivity (RC) at global level, were in a range between 1.4971 (Mawanella) and 0.4093(Ambalankanda) that exhibited an average value (RC) = 1.0477. The relative connectivity can be considered as an indication of topological centrality of a node: the importance of the corresponding node Figure 10: Cumulative Probability Distribution as an attraction point for people in the network. of Relative Connectivity Further information on network connectivity is provided by the relative connectivity distribution ‘P According to figure-10, (where x and y axes (RC)’ defined as the probability that any given node has relative connectivity ‘RC’. In this case relative represent node connectivity and cumulative connectivity probability distribution is relatively probability respectively) most of the nodes have peaked at a mean value of 1.05 and is skewed by - a lower connectivity (e.g. more than 75% nodes 0.48745 (figure-9). The result implies that centers within the region, although there are some out of total 196 nodes are with connectivity less than 1.15), while a few nodes have higher

February 2013. Vol. 2, No.1 ISSN 2305-1493 International Journal of Scientific Knowledge Computing and Information Technology © 2012 IJSK & K.A.J. All rights reserved www.ijsk.org connectivity (e.g. 25% node with connectivity more than 1.15). In particular, only two nodes have connectivity more than 1.45. This result implies that the topology of the Kegalle district road network is close to the random graph with a quasi-absence of dominant hubs (Barrat, 2004).

According to the connectivity values, Mawanella becomes the highest connected location and Kegalle becomes the second. As show in following table 3 and figure 11, the connectivity values are gradually decreasing. Based on this observation they can be categorized into a hierarchy of a few levels. According to that Mawanella and Kegalle are at the highest level in the hierarchy, while Warakapola, Alawwa and Ruwanwella are in the second level. Other centers, which are less in connectivity, can be in the lower levels of the hierarchy Figure 11: Level of Connectivity of selected Location Global Connectivity Nodes Mawanella 1.4971 6.3 The Correlation Analysis Kegalle 1.4662 Warakapola 1.4143 The CUAVs, which represented the level of Alawwa 1.3988 urban agglomeration and the values of Ruwanwella 1.3765 connectivity, were correlated at the last stage of Rambukkana 1.3485 the process. The objective of this task was to Kotiyakumbura 1.3288 examine whether connectivity shows any Bulathkphupitiya 1.3062 significant bearing on the agglomeration of Nelumdeniya 1.2776 urban activities. In order to inspect more Ussapitiya 1.2625 detailed relationship, the results analyzed in two Arandara 1.2611 different types of methods are illustrated below Galigamuwa 1.2511 (figure-12). Hettimulla 1.2462 Table 3: Level of Connectivity of Nodes

Figure 12: Level of Correlation against the Figure 13: Node Relative Connectivity and Relative Connectivity CUAV along the Kandy Road

In the first stage of the analysis, a general correlation test was performed. Accordingly, relative connectivity values of all nodes were arranged in descending order and correlated with the corresponding CUAVs. The results of this correlation analysis are given in the figure-9. According to that more than 65% of nodes have show significant correlation coefficients Figure 14: Node Relative Connectivity and (>0.075), which was significant at 0.01 level. CUAV along This is an indication that connectivity has a remarkable impact on the level of urban agglomeration of a given location.

In the next stage, connectivity values and CUAVs of nodes located along three main roads were correlated. The shapes of two corresponding graphs depict a positive Avissawella-Galigomuwa Road relationship between the two variables (Figure- 13, 14 and 15). The correlation coefficients were Figure15: Node Relative Connectivity and 0.8222, 0.7920 and 0.7720 (more than 0.75) at a CUAV along the Polgahawela-Kegalle Road and significance level of 0.01. This further indicates Kegalle-Awissawella Road a relationship between the relative connectivity 7. DISCUSSION and level of urban agglomeration.

According to the study, connectivity values obtained out of the analysis show a great

February 2013. Vol. 2, No.1 ISSN 2305-1493 International Journal of Scientific Knowledge Computing and Information Technology © 2012 IJSK & K.A.J. All rights reserved www.ijsk.org relationship with the urban activities edge effects created a nearly 8% error in overall concentrated in them. Mawanella and Kegalle result. Fourth, there are two urban centers which nodes obtained the highest connectivity values attracted people due to special reasons, as well as the highest urbanizing values in the Dedigama because of a temple; Kotawehera region. The two centers are highly competitive (religious and historic significance) and and it is obvious that there are no close Kananwita because of a waterfall (Scenic substitutes for them in terms of urbanizing as beauty) have capable to reach higher level of well as connectivity. Mawanella obtained higher urban agglomeration than reflected by the level of global and local connectivity value than connectivity results. Kegalle. It’s reflective of the existing trend of urban agglomeration. In the field observation it could be noted that However, the results of the overall analysis Mawanella has more number and variety of shows that the level of connectivity values have urban activities than that of Kegalle. a significant (more than 75%) relationship with Ruwenwella, Warakapola and Alawwa are three CUAVs of nodes. emerging urban centers within the region that 8. CONCLUSION AND recorded fairly high level of connectivity RECOMMENDATIONS compared to other nodes. Connectivity values of more than two third of the nodes correlated with As stated at the beginning, what is presented in CUAVs and thereby reflected a strong this paper is only the preliminary findings of an relationship between connectivity and urban initial stage of a long term project, aimed at agglomeration. exploring the possibilities of explaining urbanization trends with the analysis of ‘Connectivity’. The main finding here is that the However, there were a few numbers of nodes, nodes with higher connectivity were the ones which obtained connectivity values that are not with higher agglomeration of urban activities in corresponding with the CUAV values some and lesser connected nodes had less urban instances. First, 33% of local and village level activities. In other words, it could find a non- centers (15% of total nodes) are not reflecting trivial correlation between the connectivity and the urban agglomeration as revealed by the urban agglomeration in nodes located in a road connectivity values. This is mainly because network of the selected region. The results though there are road connections; those are low indicate the competence of connectivity analysis in mobility due to poor condition and absence of as a technique to explain the capacity to urban public transportation facilities. Second, the local agglomeration of locations. Regional planners centers, located within 1 km distance (10% of may find a strategic importance in this finding the total nodes) to the regional centers (Kegalle for two directions of interfere with the region: & Mawanella) were recorded higher level of First is a more passive involvement, where CUAVs than the corresponding connectivity planners can identify and predict the urban values. Urban agglomeration occurred due to the agglomeration trends all over the region in view, spillover effect of large urban agglomerations and devise suitable policy strategies to enhance may cause to this sort of distortion. Third, the the ongoing trends. Second is the active

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Annexure 01: Classification of Urban Economic Activates Center

Local Level Urban or Sub Region Level Regional Center Weight 1 2 3 1. Retail Shop (S) 5. Retail Shop (L) 2. Veg. & Fruit Stall 6. Pola Convenient 3. Retail Shop (M) 7. Wholesale Center Goods 4. Stationery & Book 8. Book Shop (L) Shop (S) 9. Tea Boutique 12. Hotel 16. Tire Selling Center 19. Lottery Selling 10. Tea Shop 13. Hardware (M) 17. Lottery Selling Center (RO) Service 11. Hardware (S) 14. Pharmacy Center Goods 15. Gas Selling 18. Hardware (L) Center 20. Textile Shop (S) 22. Footwear (S) 24. Footwear (L) 26. Super Market Shopping 21. Mobile Phone 23. Textile Shop 25. Ornamental 27. Textile Shop (L) Goods Selling (M) 28. Electrical Eq. 30. Furniture (L) 34. Motor Spear Part 37. Car Selling Center Selling Center (S) 31. Gem & Jeweler Selling Center (L) 29. Furniture (S) Shop 35. Computer Selling Luxury 32. Motor Spear Part Center Goods Selling Center 36. Electrical. Eq. (S) Selling Center (L) 33. Motorcycle Selling Entertainme 38. CD/Video Shop 39. Bar & Liquor Shop 41. Cinema nt 40. Race Bookie 42. Gym 43. Baber Salon 52. Studio 60. Hotel & Reception 68. Bank (RO) 44. Call Center 53. Printing Press Hall 69. Insurance 45. Taylor Shop 54. Bank (B) 61. Tuition Class (L) 70. Pvt. Hospital 46. Beauty Parlor (S) 55. Dispensary 62. Internet Café 71. Pvt. Schools 47. Communication (More than one 63. Medical center 72. Telephone Billing Center doctor) 64. Beauty Parlor (L) Centers Services 48. Bank (Area) 56. Tuition Class (S) 65. Optician 49. Dispensary (One 57. Vehicle Service 66. Petrol Station doctor) Center 67. Florists 50. Motorcycle Garage 58. Liernes 51. Iron Work Center 59. Vehicle Garage