Abstract
The main objectives of this research are 1) to develop a spatial and environmental database by collecting and generating several related digital data and set up an environment for data management and analysis in combination with GIS and numerical simulation software. 2) To investigate objective knowledge of relationship between urban structure and ambient microclimate in new housing area in tropic country. As a result, it became clear that usability of developed spatial database and analysis environmental for a process which contains data modeling, management and visualization through the qualitative and quantitative consideration of numerical simulation effectively, and several new fundamental findings of relationship between deference of building type, open-space, and block density and ambient physical environment objectively.
1. INTRODUCTION
Nowadays, as for global environmental problems become increasingly such as climate change or urban heat island. It is said that to exploit natural renewable resources actively is unavoidable concept in the architecture, urban design field as well. Malaysia, which is one of the Southeast Asian countries, is located in the tropical wet climate zone. However, building form, site plan and road layout in the most new housing development area have been unconsidered strongly a hot and humid climate for their master design, urban landscape strategies and methods (Givoni, 1998).
Generally, environmental-friendly design methods have been actively discussed and researched based on the microclimate analysis especially in western countries. Thus, it is quite difficult to apply these accumulated findings and skills to an urban development or landscape design in tropic countries directly, because of each country's climate and general culture are totally different obviously. Furthermore, to reflect the physical environmental and climatic information in micro-scale landscape design effectively, it is necessary to analyze the spatial and environmental information of the target area adequately. However, fully quantitative analysis and assessment of outdoors environment on micro-scale using information technology such as GIS or computer numerical simulation have not shown substantial progress in Southeast countries.
In the previous research (Saito, 2010), we have presented about the spatial data collection, making GIS environment and several fundamental features on two housing areas in Johor Bahru. Based on these cumulative data and knowledge, the aims of this research are (1) to develop a spatial and environmental database by collecting and generating several related digital data and set up an environment for data management and analysis in combination with GIS and numerical simulation software. (2) To investigate objective knowledge of relationship between urban structure and ambient microclimate in new housing area in tropical country.
2.Developing GIS and Simulation Environment
2.1. Determining Objective Areas
In this research, we focus on the two relatively new developed housing areas that are located in the district of Johor Bahru in Malaysia as main target areas (See Fig.1and Fig.2). The housing area (A) (Fig.2) and (B) (Fig.2) has developed in the beginning of 90's and 2000's respectively as general suburban housing area and which has consisted of low-rise buildings mainly one-story and two-story building (Fig.3(a) and (b)).
2.2. Developing Data management and Analysis Environment
Collected several spatial data so far have been stored in GIS. In the Fig.4(a) and Fig.4(b) generated from GIS database, the numbers on the each buildings represent the height (m), differences of gray color represent building types and the numbers in the circle on the each blocks represent block density (FAR: Floor Area Ratio,%)
Besides the stored GIS spatial data, the integrated work environment for spatial data management, analysis and visualization based on GIS and computational microclimate simulation application (ENVI-met1)) were developed (Fig.5). In this environment, the original data converter was also developed for converting different format data among several independent applications. Consequently, it allows us to manage spatial data integrating as a platform whenever figure data update is needed during preparing for simulation. And it also allows us to analyze and visualize result data with other existing GIS layer after microclimate simulation by returning only numerical data from simulation application on GIS application.
3. Microclimate Simulation and Discussion
Firstly, we choose four focusing areas (each 300m x 300m) from both housing area A and B based on categories of main building types and forms.
– Model1: Mainly Detached-house block (See Fig.4(a))
– Model2: Mainly Terraced-house block (See Fig.4(a))
– Model3: Mixed of Detached and Terraced-house block (See Fig.4(a))
– Model4: Terraced-house and Openspace (See Fig.4(b))
3.1 Fundamental of Microclimate Simulation
In the microclimate simulation, especially two particular indices such as “air temperature” and “wind velocity” will be focused in this research because these indices are considered a principle environmental index which can be connected to health and comfort value in outdoor space generally. Table 1 shows several main initial parameters for the numerical simulation.
The spatial size for doing simulation is defined 400 x 400m area (This size includes 50m perimeter zone as a margin with building information for surrounding 300x300m of the each target area.), and 50m for z-axis direction. The grid size of x, y, and z-axis directions are 2.5m x 2.5m x 2.5m respectively and the grid from the
ground level to 2.5m height is subdivided into 5 grids (0.5m grid) for more detailed simulation. The types of land cover are categorized into three types that include Soil2), Concrete, and Asphalt as shown by Table 2. The building3), Grass5) and Trees5) that include height information are located on these land covers. The trees location information is shown by dots in Fig.6.
3.2 Results of Numerical Simulation surrounding Building and Block
Fig.7 shows ambient air temperature surrounding building and block at 2pm on 21st March. Results on the several viewpoints are as follows.
In terms of detached-house, we can see the locally darker colored distribution in comparison with others near by surrounding building which have a large yard in their lot and a pitch of building 10-20m in the part of A of Fig.7(a). Same distribution feature can be seen in the part of B of Fig.7(a) and part of A which is surrounding of detached-housing in the Fig. 7(c).
In terms of terraced-house, in the surrounding buildings which have approximately 100m long-side wall as shown in Fig.7(b), totally higher value are distributed rather than other models regardless of block inside or on the road. In the surrounding the same type of buildings which have 50m long-side wall in the Fig.7(c), relatively lower value distribution are spread in comparison with Fig.7(b). Totally lower values are seen in the Fig.7(d). Especially, lower values are seen surrounding building in the part of A in the Fig.7(d).
In terms of open-space, relatively lower value are spread inside open-space as shown by the part of B in the Fig.7(c) and Fig.7(d) rather than other parts. Both of these parts show the lowest parts in each model. However, decline of temperature are not seen on the surrounding road in both models.
From these three features, in terms of distribution patterns of several indices, it becomes clear that the part of surrounding detached-house that have a large yard and wide pitch of buildings show lower temperature compared with other part. Also, especially surrounding terraced-house, a length of long-side wall of the building and differences of its layout influence an ambient air temperature. Furthermore, inside of an open-space is relatively lower temperature rather than other places caused by green spaces and trees, however it’s influenced area are limited within open-space and not reached to surrounding buildings.
Next, Fig.8 shows ambient wind velocity surrounding building and block at same 2pm on 21st March. Results on the several viewpoints are as follows.
In terms of detached-house, very calm wind velocity is spread in the surrounding buildings which have 3.5m pitch of buildings in the part of A of the Fig.8(a). This feature is seen in the other blocks which include same type of buildings. Surrounding buildings which have a large yard within its lot as shown in the part of B of the Fig.8(a) and on the road especially on the intersection in the part of C of the Fig.8(a), approximately 1m/s higher wind velocity rather than a part of A are seen.
In terms of terraced-house, relatively higher value wind velocity distribution are seen on the straight road in the Fig.8(b) and Fig.8(c). On the back-lane (pitch of the building is approximately 7.8m) in the same figures, wind velocity is very calm. Also, on the road in the Fig.8(d), regardless same building types, ambient wind velocity is lower (approximately 0.2-0.4m/s) than surrounding buildings which are layout parallels each other (approximately 0.8-1.0m/s).
In terms of opens-pace, relatively higher value distributions are seen inside the central opens-pace in the Fig.8(d). Also, wind velocity is higher than other part on the southern road which connected to open-space.
From these three features, it becomes clear that differences of building type and layout influenced to surrounding wind velocity objectively although under same building models. Also it is made clear that influences of wind ventilation in the district open-space may reach to the buildings surrounding open-space.
3.3 Results of Relationship between Building Type / Building Shape / Block Density and Ambient Microclimate
Continuously, we calculate average of each index on a 1m grid on buffer zone (as shown by gray zone in the Fig.9) which is generated surrounding each building and block in order to grasp more detailed and individual situation of building and block. In this procedure, the 5m of buffer zone are generated surrounding each building and block. This particular number of distance is derived from average pitch of building (5.76m) and a half of average road width (9.92m) in the both target areas.
3.3.1 Building Aspect and Ambient Average Air
Temperature
As shown in Fig.10(a), it becomes clear that the difference of average air temperature surrounding building from the total viewpoint in the four models is less than 2°C. By looking at more closely in terms of building types, surrounding detached-house type, the difference of average air temperature is within 1-1.5°C according to the presence or absence of a large yard in their lot. Also by comparing the building which have same aspect surrounding terraced-housing, it becomes clear that the difference of 1-1.5°C average air temperature occurred by differences of building layout and surrounding situation.
3.3.2 Building Aspect and Ambient Average Wind Velocity
As shown in Fig.10(b), it becomes clear that the difference of average wind velocity surrounding building from the total viewpoint in the four models is less than 1.0m/s. By looking at more closely in terms of building types, in surrounding a part of detached-house which have a large yard in their lot and parallel layout terraced-house, average wind velocity is more than 0.6m/s. However, it also becomes that most of surrounding buildings in Model4, average wind velocity is less than 0.4m/s. These values are lower than other Models. It means that wind ventilation inside the model4 may be disturbed by surrounding several terraced-house which have a large number of aspect.
3.3.3 Block Density and Ambient Average Temperature
As shown in Fig.11(a), in terms of inside blocks and surrounding blocks which FAR is between 80-100%, it becomes clear that the influence on average air temperature by the differences of building type or layout are small. Also, in the case of FAR is around 140%, if buildings have comparable value of FAR and same terraced-house type, average air temperature inside and surrounding block in the Model4 is approximately 1°C lower than the Model2 (parallel layout). Furthermore, in the block that composed by mainly detached-house, the blocks that have a large yard in its lot show lower value of air temperature.
3.3.4 Building Aspect and Ambient Average Wind Velocity
As shown in Fig.11(b), in terms of inside blocks and surrounding blocks which FAR is between 80-100%, it becomes clear that the influence on average wind velocity by the differences of building type or layout are small. As with the features of wind velocity surrounding each building in the section 3.2, it becomes clear that the average wind velocity is 0.7m/s inside and surrounding blocks in the Model1, Model2 and Model3. Furthermore, it becomes clear that average wind velocity is more than 0.6m/s in relatively low-density blocks that FAR is less than 100%.
3.4 DISCUSSION
Through the qualitative and quantitative comparison, the differences in ambient air temperature and wind velocity are relatively small in the four models in this housing area from a macro-scale viewpoint. However, from a micro-scale viewpoint, it becomes clear that the influences from the differences of component elements of housing area to the surrounding environment by comparing the particular physical environment such as building type, form, layout and layout, and block density.
Firstly, these features are obtained in terms of “building type and form”.
– In surrounding detached-house type, the difference of average air temperature is within 1-1.5°C according to the presence or absence of a large yard in their lot.
– In the blocks that have same value of aspect, average air temperature surrounding buildings are affected by differences of building layout. The average air temperature surrounding non-parallel layout terraced-house is approximately 1-1.5°C lower than parallel layout one.
– In surrounding a part of detached-house that have a large yard in their lot and parallel layout terraced-house, average wind velocity is more than 0.6m/s. However, it also becomes that most of surrounding buildings in the Model4, average wind velocity is less than 0.4m/s.
Secondly, these features are obtained in terms of “open-space ratio”
– Inside of an open-space is relatively lower temperature rather than other places caused by green spaces and trees, however it’s influenced area are limited within open-space and not reached to surrounding buildings.
– The influences of wind ventilation in the district open-space may reach to building environment surrounding open-space.
Finally, these features are obtained in terms of “district floor area density”
– In terms of inside blocks and surrounding blocks which FAR is between 80-100%, it becomes clear that the influence on average air temperature by the differences of building type or layout are small.
– In the case of FAR is around 140%, if buildings have comparable value of FAR and same terraced-house type, average air temperature inside and surrounding block in the Model4 is approximately 1°C lower than the Model2.
– In surrounding a part of detached-house that have a large yard in their lot and parallel layout terraced-house, average wind velocity is more than 0.6m/s.
4. CONCLUSION
Through this research, firstly, geospatial database have been developed with GIS by collecting spatial map data and metrological data as an assessment and analysis tools for environmental information objectively in the housing area in tropic country Malaysia. Besides these database, the numerical simulation-working environment also have been developed in combination with this GIS and other particular application for microclimate simulation. Consequently, these environment allows us to handle and manage to make a base map including physical information such as buildings, blocks, land cover, trees within a scale of several blocks, and to run a simulation and to visualize and analyze using numerical result data as a sequential system. Actually, effectiveness of this working-environment is confirmed through the consideration by making urban model and analyzing, visualizing with real data. In the near future, we can expect that these environment and system will be more utilized as a common-platform in the same research activities after making functional modification and necessary adding spatial data.
Subsequently, we discussed a relationship between main physical composition elements and microclimate environment surrounding buildings and blocks in the housing area with developed working-environment for data management and simulation. Through this consideration, we grasped and compared the impacts and features that building aspect or block density influenced to ambient air temperature or wind velocity quantitatively, and achieved several fundamental knowledge of relationships between housing elements and surrounding environment.
As a future prospects, more deeply consideration about impact to surrounding environment in the housing area from differences of terraced-house layout and form based on the results and knowledge through this research project. Furthermore, adding consideration about effectiveness for temperature reduction by green part and trees that are not mentioned in this project can be also important topics.
ACKNOWLEDGEMENTS
This research was supported by the research grant from University Technology Malaysia and Shibaura Institute of Technology, 2009-2011.
NOTES
1) ENVI-met is a three-dimensional microclimate model designed to simulate the surface-plant-air interactions in urban environment with a typical resolution of 0.5 to 10 m in space and 10 sec in time. It has been developed by Prof. Dr. Michael Bruse and team, University of Mainz. Further detailed information: http://www.envi-met.com/
2) Albedo: Soil (0), Concrete (0.4), Asphalt (0.2)
3) Albedo: Wall (0.25), Roof (0.3)
4) Grass height: 0.05m
5) In the simulation, we use a default trees model that is originally stocked in the ENVI-met (height: 10m, Tree type: Deciduous). Actually this tree model is a not fully reflected feature of typical tropic tree.
REFERENCES
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3. Oke, T.R, Towards better scientific communication in urban climate, Theor. Appl. Climatol. 84, pp.179–190, 2006
4. Ritchie, A., Sustainable Urban Design – An Environmental Approach – , Taylor & Francis, 2003
5. Emmanuel, M.R. , An Urban Approach to Climate-Sensitive Design -Strategies for the Tropics, Spon Press, 2005
