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  • 1. ISSN (Print): 2328-3777, ISSN (Online): 2328-3785, ISSN (CD-ROM): 2328-3793 American International Journal of Research in Formal, Applied & Natural Sciences AIJRFANS 14-288; © 2014, AIJRFANS All Rights Reserved Page 147 AIJRFANS is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research) Available online at http://www.iasir.net An Analytical and Practically Feasible improvisation over representation of Sky-View-Factor Rajesh Gopinath1 , Jagdeep Singh2 , Dharmender Singh2 , Ghanshyam Kumar2 and Navneet Singh2 1 Assistant Professor, 2 Project Scholars Department of Civil Engineering, Acharya Institute of Technology, Bangalore, INDIA I. Introduction Street canyon geometry is the most relevant urban parameter responsible for microclimatic changes occurring in a street canyon, due to its potential to influence solar access and airflow at street level [1]. A typical urban canyon is a basic urban surface unit comprised of the walls of adjacent buildings, the ground (street) between, and the air volume enclosed within. As observed from figure 1, it can be visualised as a relatively narrow street with buildings lined up continuously along both sides [2]. A commonly used indicator to describe this typical urban geometry is the ‘Sky View Factor’ (S.V.F.), a dimensionless measure between 0 and 1, representing totally obstructed and free spaces, respectively. Herewith owing to its role in radiation balance schemes, S.V.F. is widely studied by climatologists for variations in surface and air temperature. Hence in this context, it is utmost vital to represent canyon geometry most appropriately. Figure 1: Sky-View-Factor Profile [3]. Figure 2: A FC-E8 fish-eye lens equipped NIKON Coolpix 4500 camera, alongside a typical fish eye photograph [4]. Some common methods that have evolved for estimating or calculating S.V.F. are scale models, analytical method (angle measurements, H:W), estimation by graphics signals, evaluation of G.P.S. signals from G.P.S. receivers, surveying techniques, manual and computer evaluation of fish-eye photos snapped with fish-eye lens, thermal fish-eye imagery and computer evaluation of a 3d-database describing surface geometric elements (G.I.S.) environment [5]. During the 1980's, studies were mostly based around the geometrical modelling of Abstract: Sky-View-Factor (S.V.F.) as an urban canyon factor is of most significance to climatic studies. The available techniques to measure the same is sufficiently inadequate to provide representativeness and are either cumbersome or uneconomical. The present study proposes a newer, practically feasible and logical means of ascertaining and representing S.V.F. for a wide spread highly variant canyon. Twelve locations possessing variable degree of green cover, water body, open spaces, paved/unpaved surfaces and built-up spaces were selected for the study. The study is based on an author introduced modification of the Oke’s methodology of determination of angles of elevation about absolute ground level, using a theodolite. The novelty of the present technique to determine S.V.F.is the inclusion of two extra angles of elevation perpendicular to road and with the height of instrument considered as focal point. Eventually the comparative results from Oke’s method and the author modified method were subjected to review from practical point of view. Keywords: Sky View Factor; cumbersome; urban; canyon; climatic;
  • 2. Rajesh Gopinath et al., American International Journal of Research in Formal, Applied & Natural Sciences, 6(2), March-May, 2014, pp. 147-150 AIJRFANS 14-288; © 2014, AIJRFANS All Rights Reserved Page 148 canyons. Also referred to as Analytical method; this is suitable for simple and well represented structures and could be used for algorithm testing and parametric analysis [6]. As compared to the other methods, it is also time consuming but is easy to understand and economical, and doesn’t need too many skills. The photographic methods (Figure 2) use a fish-eye lens to take onsite photographs that project the hemispheric environment onto a circular plane. It is only since the 1980s that photographic methods have received enough attention in determining S.V.F. in urban climatology [7]. However, as this method requires image generation and processing, it is uneconomical and often time-consuming. In contrast to the above-mentioned methods, which are based on direct calculation, the G.P.S. method was developed with the aim of measuring S.V.F. in real-time using proxy data. The G.P.S. component was integrated with a fish-eye lens photo capturing and processing module on a mobile platform to give simultaneous calculation and approximation of S.V.F. in real-time [8]. This method has been proved to be ‘significantly faster’ than the vector-based method [9]. However here, prediction equation depends on the accuracy G.P.S. equipment used. Hence though there are several methods to ascertain S.V.F., each has its advantages and disadvantage, and their application would be specific to environment and skills. II. Scope of Study Reviewed literatures have unveiled that the differences of intra-urban surface air temperatures have been shown to be strongly dependent on the S.V.F, when especially taken from the ground than those taken at sensor level [10]. Nevertheless it may be affirmed that there is no conclusive findings that highlight the true nature of correlation between canyon geometry with ambient air temperature, while some have shown good relationship, other studies showed the opposite. This gap in research thereby necessitates auxiliary studies on framework for more accurate determination and precise representation of S.V.F. Keeping this in mind, and by explicating from previous section, Geometrical Method would be the most suited and economical technique for undertaking research at Graduate level so as to resolve the current concern for S.V.F . III. Study Area The present research objective primarily proposes a logical modification in the basic formulae of computation of S.V.F., as an analytical and practically feasible improvisation over the geometric determination by Oke’s method (Figure 3) [5]. Under the circumstances, upon choosing a location which has no dead-ends, the results computed by Oke and present methodology would remain the same. However in reality such conditions need not necessarily apply in townships or cities ill-planned or inorganically developed, and therefore do not apply to Oke’s methodology. Therefore great care was taken in identifying locations wherein the author suggested modification projected significance. Also most researches cited in literatures for assessing Urban Canyon Geometry, were limited to the city centre or only some urban canyons of cities. In the context of present study, to ascertain true and representative results, the present study involved measurement of 12 distinct and different urban canyons for Bangalore city, each within a Radius of 250m. Figure 3: Oke’s Logic [5] Figure 4: Typical narrow landscape. Figure 5: Typical Wide landscape S.V.F. Figure 6: S.V.F of narrow landscape Figure 7: Typical closed valley landscape
  • 3. Rajesh Gopinath et al., American International Journal of Research in Formal, Applied & Natural Sciences, 6(2), March-May, 2014, pp. 147-150 AIJRFANS 14-288; © 2014, AIJRFANS All Rights Reserved Page 149 Generally all the stations comprised of distinct features such as immediate lakes in eutrophicated state, outward looking Buildings, low density-low rise developments with colonial bungalows on large plots, tight constructions which cut deep canyons through the area (Figure 4), wide pavements (Figure 5), narrow streets projecting skewed S.V.F. (Figure 6), closed spaces (Figure 7), low built-up density with green spaces interspersed between them, low rise structures, with mainly single, double and triple storey structures, scattered residential spaces and coconut grooves, Single-family homes, interlaced several open spaces etc. IV. Experimental Methodology While twelve calibrated Theodolites were made uses of in the physical surveying for the measurement of ‘angles of elevation’ and ‘height of each building’; a measuring tape was made use of in determining the ‘width’ of each road. Standard practices for calibration and angle measurement was adopted [11]. As all the observations were taken acknowledging height of the instrument; this study hence postulates the point of measurement as the instrument height and not the ground as suggested by Oke [5]. The present case-study herewith hence revises the positional concept and considers representative measurement as applicable to instrument height (or 1.5m) only. This was evolved as a practical and effective change from the actual procedure, to support the W.M.O. (World Meteorological Organization) guidelines which consider all climatic (including ambient air temperature) measurements only between 1.5-2.0m [12]. Figure 7: Angles of Elevation as per Oke’s 1998 Method Figure 8: Angles of Elevation as per Current method. The basic formula for finding S.V.F. was adapted from the analytical method of Oke (1988). In this geometric method, only 2 elevation angles (Equations 1 & 2) to the top of buildings were measured normal to the axis of streets in both directions (Figure 7), using a 1.5m high theodolite. Eventually the S.V.F. was determined using Equation 3 [3]. X1 = (1-Cos α1)/2 …Eqn. 1 X2 = (1-Cos α2)/2 …Eqn. 2 S.V.F. Oke = [1- (X1 + X2)] …Eqn. 3 X3 = (1-Cos α3)/2 …Eqn. 4 X4 = (1-Cos α4)/2 …Eqn. 5 S.V.F. modified = [(1- (X1 + X2)) + (1- (X3 + X4))]/2 …Eqn. 6 However, the present study postulates the above equations with a slight author desired modification that not only two, but all the four directions shall contribute to the S.V.F.; hence 2 extra angles of elevation, perpendicular to the axis of streets (Figure 8) were measured (Equations 4 & 5), and finally the S.V.F. was arrived at by using Equation 6. To achieve the study objectives, all the 4 angles of elevations were measured for each point for observable changes in the topography, about all the streets within the radius of 250m at each monitoring station. If there were open spaces, parks, forests or water surface in a particular direction, then 0º was assigned as an angle value. In present study, the dataset so obtained shall represent an entire radius, thereby bringing clarity in true representativeness of S.V.F. for a vast area, achieved with an averaged S.V.F. across each station, by referring the Google maps (downloaded from a common altitude) with the field work data. The plan-view was divided into equal number of square cell. For every complete cell, which depicted an entire open space or water- bodies, the S.V.F. was assigned 1 whereas in case of built-up spaces, it was accorded ‘0’ for it occupied the entire cell. For each cell which represents fractions of both, proportionality was introduced in assigning final S.V.F. Hence, the computation also took into account even the contribution of 0’s and 1’s from the cells where practically surveying wasn’t carried out. This exercise is of top priority as no standard measure has been found in literature review to arrive at a single representative value for S.V.F.
  • 4. Rajesh Gopinath et al., American International Journal of Research in Formal, Applied & Natural Sciences, 6(2), March-May, 2014, pp. 147-150 AIJRFANS 14-288; © 2014, AIJRFANS All Rights Reserved Page 150 V. Results and Discussion Using the author modified methodology; a map of continuous S.V.F. (Figure 9) at instrument height for all the 12 stations within 250m radius was generated. Generally speaking, though the 2 extra ‘angles of elevation’ may be 0° for a wide open road junction, however it would definitely exhibit certain values when there is dead-end encountered in the form of a building or tree or other land-use uses. This concept is most applicable for a city like Bangalore which has grown without any proper town planning. Figure 7: Map of continuous S.V.F. within 250m radius Chart 1: Comparison of S.V.F values from both techniques. As observed from Chart 1, there is a distinct deviation among S.V.F values computed from ‘Oke’s Method’ and ‘Author Modified Method’. This is found to be higher in stations wherein the street alignment was highly irregular and non-uniform with several dead-ends marking the station, and practically this makes absolute sense. Statistically speaking this variation will have a leading invariable impact on inferential relationships established, than that using values referred from Oke’s method. Hence the study proposes the application of present methodology over Oke’s technique. VI. Conclusion Surface geometry has a complex influence on the urban atmosphere. In the present research a logically modified and simpler analytical approach has been described which allows the accurate estimation of S.V.F. for the present inorganic urban scenario. When compared to the Oke’s Method, a significant deviation was revealed in the final S.V.F. values, capable of strategically influencing studies on relationship with climatic parameters. Therefore the proposed methodology confirms the value added significance of the additional angles of elevation measured perpendicular to road, alongside the consideration of height of instrument as focal point. Eventually the current study clarified the significance of incorporating the contribution of every cell for true representativeness of S.V.F. VII. References [1] Y. Nakamura and T. Oke, “Wind, temperature and stability conditions in an east-west oriented urban canyon,” Atmospheric Environment, Vol. 22, 1988, pp. 2691-2700. [2] Nicholson Sharon E., “A Pollution Model for Street-Level Air”, Atmos. Environ., Vol. 9, 1975, pp.19-31. [3] http://www.atmosphere.mpg.de. [4] Watson I.D. and Johnson G.T., “Graphical Estimation of Sky View Factors in Urban Environments”, International Journal of Climatology, Vol. 7, 1987, pp. 193-197. [5] Oke T.R., “The urban energy balance”, Progress in Physical Geography, Vol. 2(4), 1988, pp. 471- 508. [6] Johnson, G.T. and I.D. Watson, ‘The determination of view factors in urban canyons”, Journal of Climatology and Appl. Meteor., Vol. 2, 1984, pp. 329-335. [7] Steyn D.G., “The calculation of view factors from fish-eye lens photographs”, Atmosphere-Ocean Vol. 18, 1980, pp. 254-258. [8] Chapman L., Thornes J.E. and Bradley A.V., “Sky-view factor approximation using G.P.S. receivers”, International Journal of Climatology, Vol. 22, 2002, pp. 615-621. [9] Gal T., Lindberg F. and Unger J., “Computing continuous sky view factors using 3D urban raster and vector databases: comparison and application to urban climate”, Theoretical and Applied Climatology, Vol. 95, 2009, pp. 111-123. [10] Svensson M.K., “Sky view factor analysis - implications for urban air temperature differences”, Meteorological Applications, Vol. 11, 2004, pp. 201-211. [11] B.C. Punmia, Ashok Kumar Jain and Arun Kumar Jain, “Surveying-II”, Laxmi Publications (P) Ltd., 12th Edition, 1994. [12] Tim R. Oke, “Initial Guidance to Obtain Representative Meteorological Observations At Urban Sites, (Canada), Instruments and Observing Methods”, Report 81, World Meteorological Organization, 2006.