MSc/PgDip/PgCert Urban Design 2009 / 2010 · (Studio 1a); 2. Urban Design Strategy. You will propose a Strategic Plan and a Concept Plan, together forming the Urban Design Strategy,

____________________________________________________________________________________ Analysis brief 5: Comparative analysis of urban fabrics, 2009/10.

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MSc/PgDip/PgCert Urban Design 2009 / 2010

ANALYSIS BRIEF Comparative analysis of urban fabrics Sergio Porta, Ombretta Romice, and Tutors

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Introduction The MSc in Urban Design has to some extent changed this year as a consequence of new contents and approaches that have been embedded in its structure. The course is now articulated into four phases:

1. Case analysis. You students will work in groups on Govan as part of a larger urban sector of Glasgow along the Clyde, getting to know intimately this area, its links potentials and pitfalls (Studio 1a);

2. Urban Design Strategy. You will propose a Strategic Plan and a Concept Plan, together forming the Urban Design Strategy, for the improvement of this area envisaging actions and projects that deal with services, mobility, housing, and public realm provision (Studio 1b);

3. Block analysis and coding. You will be requested to work out a complete morphological analysis of three urban blocks that are assigned by staff. The block analysis is carried out by drawing each urban block in two boards and by the quantitative analysis of morphological aspects as they appear on drawing. Once all sample blocks have been worked out and all data is available, students and staff derive from that a synthetic urban design code (Studio 1c).

4. Masterplanning and place design. You are led to the production of a masterplan for sub-areas of Govan district. You will learn how to take action for subdivision of large blocks, a correct management of density as related to transport and land use, how to design safe and livable streets and how to the existent urban fabric of public and private buildings in relation to streets, land uses, density and transport. Finally, you will be asked to deepen their masterplan and coding by experimentally developing the design of streets and buildings in a small part of it (Studio 2).

The main objectives of this reform are to strengthen the work on urban analysis by means of new analytical “packages”, each of which will be carried out by one single group of students in the first phase of work, corresponding to “AB 931 - Urban Design Studio 1a”. In addition, an entirely new phase, named “Block analysis and coding”, has been introduced that is aimed at understanding the structural characteristics of the urban fabrics, their spaces and measures. The resulting course is this year particularly dense of arguments and different methodologies will be taught in order to give you basic notions of what are the “tools” that an urban designer may apply to the interpretation and modification of urban spaces. All this results in a very challenging programme, which is still experimental this year, which will require highly committed students and staff to be successfully completed. On the other side, this programme is a very unique one, in that it blends operational tools with community involvement, theory with hands-on approaches. Perhaps the most challenging phase of the entire course is the first, the analytical phase. Because all analysis must be completed in about one month time, and because such analysis are all very demanding – especially for students who have never approached urban studies before and are requested to work with mostly new team mates – then we decided to write these notes, the Analysis Briefs. We have written 6 Analysis Briefs, one for each “package” of analysis, which means one for each group of students:

1. Drawing the existing city. 2. History and stories 3. Planning framework 4. Experiencing Govan 5. Urban fabric comparative analysis 6. Network analysis of streets

These briefs should be considered by all of you a constant reference during the work in phase 1. We have put into them all possible instructions – instructions, not just guidelines – for the correct completion of every task, with as much detail as we were able to manage. For the same reason these briefs are fully illustrated, so that at every step you will have an idea of the sort of thing the final result should look like. This is a way for us to speed up the process of learning by doing. This also witnesses the investment that we as staff have done on this rather ambitious course, a dramatic bet addressed on the ground of our highest expectations on you. We recommend all of you to react by mobilizing all your personal, intellectual and motivational resources, without which there is no one chance to get this course – and your learning experience with it – actually complete and significant at the end of the year.


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5.1 Scope and objectives General Scope of the brief Why do we propose to conduct comparative studies on different types of urban textures? What do these studies tell us about the quality of urban spaces? Comparative studies reveal themselves to be very relevant to get a first toolkit to evaluate specific characteristics related to the quality of space. We aim at identifying and quantifying a set of indicators that can help us in describing and comparing the key elements of the urban form. In this work in particular, students are invited to calculate measures of (1) connectivity and accessibility of the urban texture, and (2) solar admittance of a neighbourhood. A short description of the scopes and objectives of each of the 2 WPs here proposed follows. WP1. Connectivity and Accessibility of the Urban Texture Scope Students will investigate the role of the urban texture in assessing the proper functioning of the city. The urban texture is a combined expression of streets and block design. The design of the site layout and in particular of the urban grid is a powerful tool to control and manage the sustainability of a city. We assume here, that the urban texture intended as a structural property is sufficient alone to explain some characteristics of the city concerning connectivity and accessibility. The capacity and the arrangement of the street network are key elements in the determination of accessibility. In this work we refer to the arrangement of the network alone, the so named “topological accessibility”, since we are interested in analysing those variables that are directly related and controlled by the work of urban designers. For instance, both social and environmental aspects are intimately bounded to the design of the urban texture. A permeable urban texture is walkable and gives to people the chance to meet on the public realm. One of the basic urban design rules informs that the more physical connections we have on the street network, the more human connections can consequently be promoted.

Connectivity of the street network Street connectivity is a key component for a good urban design. High connected street networks perform better in terms of sustainable mobility, encouraging walking and bicycling in urban areas. In fact, grid-like urban structures offer more opportunities for activities and social interactions in general and reduce the travel demand, since everything is reachable in a shorter time. The apparent contradiction that having numerous connections on the street network would leads to congestion can be denied by correct transportation policies, whereby the car is not considered as the protagonist of travelling and pre-car age models are newly taken into account. Even if these principles are generally accepted in the urban design community, the question regarding how to establish the connectivity of a place is very open. Numerous indicators have been developed and imported in the urban design practice from very different fields, like biology, physics, geography and sociology. Network analysis and graph analysis collect all this knowledge and represent interdisciplinary research sectors and their applications are useful in very different domains. The proposed indicators and tools that follow try to delineate a possible practical answer to connectivity measurements for the purpose of increasing walking and cycling in the urban planning design process. These indicators are particularly useful in comparative studies, like for example in cases where we have to analyse a specific condition before and after intervention.

Accessibility of the urban network Accessibility is defined as the measure of the capacity of a location to be reached by, or to reach different locations. Therefore, the capacity and the arrangement of transport infrastructure are key elements in the determination of accessibility (Rodrigue et al., 2009). A set of structural indicators of connectivity and accessibility is presented in this section. Objectives

1. To understand the basic differences between urban fabrics as a result of different street layouts. 2. To reflect on the historical factors that stay behind the visible manifestations of street layouts.


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WP2. Raster cities (environmental analysis with D.E.M.) Scope Aim of this work is to introduce students to innovative analytical techniques based on the processing of simple urban raster images, known as Digital Elevation Models (DEMs). Several new algorithms regarding solar accessibility, heat transfer, the urban wind field, visibility analysis and urban morphology were implemented during these years. Together, they aim to constitute a tool for the environmental assessment of cities, thus providing valuable feedback to urban designers and planners. While the investigation of DEMs in non-urbanized areas has been a long-standing research focus in the geosciences and has led to an ample library of functions that are used in most GIS packages, the use of this technique in the urban context remains largely unexplored. Aim of this work it to address this gap.

The investigation of environmental indicators in architecture is not new and there are already several tools that calculate energy performance of buildings very accurately. Nevertheless, these tools are very useful at the micro-scale of architecture (environmental performance software) or at the macro-scale of landscape and regional geography (GIS tools), but the focus on urban texture is mostly lacking. Recently, the increasing attention to environmental policies in urban studies has opened up many questions about how planners should manage those indicators in the design process. In fact, numerous authors and architects are convinced that cities play a leading role in controlling sustainability: strategies for redefining more efficient cities in terms of energy performance and environmental quality were the centre of attention in seminal work by Richard Rogers in defining policies for UK cities (Rogers 1997; Urban Task Force 1999) and supported the debate around the promotion of more compact cities (Jenks et al. 1996, 2000). Anyway, a comprehensive and reliable toolkit for sustainable urban design is lacking among practicing professionals.

Objectives

3. To use simple tools (sets of scripts) to evaluate the solar accessibility of different urban textures. 4. To quantify some environmental indicators related to urban form at the scale of the

neighbourhood. 5. To conduct a comparative study among 4 different urban sites.


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5.2 Working instructions Timetable:

WP0. CONSTRUCTION OF URBAN MODELS This step is the prerequisite to fulfil the following sections WP1 and WP2. The base for the entire set of analysis consists in the reconstruction of 3 urban areas proposed by the students plus the area of Govan, where the students have to design their master-plan.

Which sites to choose? First, students have to carefully read the map of Glasgow, trying to understand its different components and evolutionary stages. This phase of reading is suggested, in order to better choose the areas of analysis. For instance, the sites should represent three different typologies of urban textures inside the city of Glasgow. They have to address three different historical construction periods as follows:

1) Site 1: the historical area, typically the city centre with traces of the ancient urban texture; 2) Site 2: the pre-modern city, i.e. an urban texture designed in the XIX or beginning of the XX

century before the age of car dependence. 3) Site 3: the modern city corresponding to a human settlement built after the II WW. 4) Site 4: A significant extraction from the Govan site that sufficiently represents the character of the

urban texture (streets and blocks layout). It is important that the centre of the selected sites corresponds to a significant urban knot, such as a transportation knot or a central place. This point has to be a central location, a point of reference for the neighbourhood.


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The selection of those urban areas has to be discussed together with the faculty and need to be approved before starting the analysis. This step should happen in the very first days of the kick-off. All the areas should be characterized by the same extension, i.e. a square that measures 800m by 800m. The indicated measures are not casual, because they precisely represent the reachable walking distance within the time of 5 minutes, if measured from the geometrical centre of the square.

Which information needs to be represented in maps/models?

After the 4 sites have been selected, different models have to be produced for each site: - Vehicular street network. The street network, built as a simple scheme is composed by arches

(linear street segments with no depth) and knots (indicate it with circles at the intersections of arches). The intersections can be distinguished depending on the number of arches converging on it; please, take into account the following 5 categories: more 4-way, 3-way, 2-way and cul-de-sac) and assign to each type a different colour (i.e. the colour of the circle that denotes the knot).

- Whole ped/veh paths network. Do the same as for vehicular network but completing the graph adding all links and nodes of the pedestrian and cyclist paths. This work must be coordinated with Group 6.

- Block structure. This is about the black and white representation of the block structure. Blocks are filled in black and the empty spaces (streets and other public spaces, non-urban land) are left in white (fig.1).

Fig. 1. A black and white representation of the block structure. This image also represents

the porosity of the urban fabric, and information that not always is essential in block analysis. Blocks can be in that case filled entirely in black without detailing internal voids.


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- The 3-D model of the built environment. This model is fundamental to start with the WP2. The model contains the volumes of the built fabric saved in dwg format (the volumes have to be extruded with Autocad). The information about the height of the buildings can be obtained from cartography or from the direct observation of places. Please save the model as an Autocad2000 file, and be sure that all the extruded volumes belong to the same layer.

- This work, as for the case in Govan, must be coordinated with Group 1. WP1. CONNECTIVITY AND ACCESSIBILITY A set of structural indicators of connectivity and accessibility is presented here. The parameters are organized in two main sections. The first section includes indicators related to the street network; the second one considers indicators derived from the analysis of the urban block. All the measures have to be computed on each case-study area (4 selected sites as explained above). At the end of the section 3 of this chapter, a table that summarize the presented indicators is provided. Please, refer to that scheme to organize your work. 1. Connectivity (week 2-3): In order to analyse the connectivity of urban areas we can investigate different strategies. We propose to classify connectivity measurements depending on the object of observation. In fact, to measure the connectivity we can simply analyse the street network but also its negative correspondent, i.e. the urban block interpreted as the space delimited by streets. In this sense, connectivity indicators are de facto morphological indicators as discussed in the section Morphology and Morphometrics. Cities can deploy urban design standards that regulate the size and the length of streets and/or they can suggest rules that control the size and the shape of urban blocks. A list of connectivity measurements follows.

-‐ Street network indicators. Numerous indicators can describe a network and these are mainly borrowed from graph theory. A network is defined as the interconnected system of elements: the elements can be interpreted as the nodes and the connection as the link of the network. From the investigation of these basic elements, nodes and links, we can derive several indicators that can take into consideration geometrical or topological spaces. - Intersection Density. A first measure is to count the number of nodes per unit area. The

higher is the number of intersections the greater is the connectivity. The area of investigation can encompass for example an urban structural unit (an area with similar morphological characteristics). This measure is also used by LEED ND (2008).

- Street Density. The number of linear extensions of streets per unit area is computed. This indicator can be obtained by counting the linear kilometres of street (linear extension of street segments to be summed together) per unit area of analysis (for example 1 square kilometre).

- Internal connectivity or Connected Node Ratio (CNR) can be measured as the number of street intersections divided by sum of the number of intersections and the number of cul-de-sacs. The higher is the ratio, the greater the internal connectivity. As suggested by the INDEX model (Criterion Planners Engineers, 2001), values should not be less than 0.5, and 0.7 and higher are recommended.

- Link-Node Ratio. The ratio of the number of links to the number of nodes. A perfect grid has a ratio of 2.5. Reaching 1.4 is a good target in new human settlements. The Link-Node Ratio is useful, when a comparative study between two conditions at different times on the same area is undertaken. Anyway, this index is unrelated to the sizing or spacing of the grid. This means that the same grid at different scales has the same value, suggesting that some additional considerations about the length of street intersections have to be taken into account.

- Connectivity of the object, typically the neighbourhood. This can be measured counting the number of existing intersections and street segments and computing their lengths, thus revealing the presence of cul-de-sac like urban layout or, on the contrary, a rich interconnected urban texture. Internal and external connectivity of a neighbourhood are introduced. Internal connectivity can be measured as the number of street intersections divided by sum of the number of intersections and the number of cul-de-sacs (the higher the ratio, the greater the internal connectivity); external connectivity is the median distance between ingress/egress points in meters (the shorter the distance, the greater the external connectivity).

- Grid pattern ratio. It defines the rate of the investigated area which is included in a grid pattern. We distinguish between a strong and a weak grid pattern ratio.

- The strong grid pattern ratio defines the rate of the investigated area which is included in a perfect grid pattern. A grid pattern is characterised by 4-way intersections and an urban block is included in the grid pattern if the nodes at all its corners are 4-way intersections (fig.2, left).

- The weak grid pattern ratio defines the rate of the investigated area which is included in a almost perfect grid pattern. A grid pattern is characterised by 4-way intersections and an urban block is included in the grid pattern if all the nodes except one at its corners are at


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least 4-way intersections (fig.2, right). This measure represents a less restrictive condition than the previous indicator.

Fig. 2. Strong grid pattern ratio (left) measures how much of the study area is included in

blocks that have all their corners constituted by 4 ways intersections. Weak grid pattern ratio (right) is the same, but also blocks with all but one corners on 4 ways crossings are included. The patched areas are included in the grid.

- Street network permeability. The permeability index tells how integrated the street network is. On every intersection of the street network we have to indicate the numerical value of the allowed possibilities of moving forward. This can be done by summing all the possibilities on each arch converging on the intersection (fig.3). Please notice, that if there is the chance to turn around and go back, this movement should also be summed to the value. Two different computations need to be conducted: one on the pedestrian network and the second on the vehicular one. Finally, the difference of the permeability of the pedestrian and the vehicular network can be computed. This latter value represents de facto also an accessibility index.

Fig. 3. Movements allowed on a 4-way intersection; on the left, the count on the vehicular network

(2+2+3+0=7 movements) and on the right, the count on the pedestrian network (4+4+4+4=16 movements).

-‐ Urban block indicators.

- Block Area. Defining the area of the block implies the definition of the street network that supports the urban texture. The smaller are the blocks the greater the connectivity. This indicator is very simple and immediate.

- Block Density. Similarly to the previous index, determining the number of blocks per unit area (typically 1/ha) informs about the granulometry of the urban texture. This index permits more flexibility than the block area index in the design of neighbourhoods, since it allows to provide more diversity in the process of sizing blocks.


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- Block Length. Together with the block area, controlling the maximum extension of the frontage of the block is a way to avoid elongated or fragmented shapes that could rather reach the opposite effect and decrease the connectivity of the urban texture.

2. Accessibility (week 2-3):

-‐ Accessibility indicators. A direct consequence of increasing connectivity of a street network is to provide greater access and communication. Citizens living in cul-de-sacs-like areas have simply less accessibility to places, because the connectivity of their houses is low and they have to travel more to access points of interests (transportation nodes, commercial uses, services, etc.). Therefore, it is really difficult to clearly separate measures of accessibility from measures of connectivity. In general, we can measure accessibility by determining those existing relationships between the point of observation and the point of interest. A list of indicators follows: -‐ Distance of the object to a particular target (centre of the district, transportation knot,

commercial uses, public park). This index can be computed after determining the centroid of the object itself and then measuring the Euclidean distance to the target. Pedestrian access is usually encouraged if targets are within a ¼ - mile distance (Duany and Plater-Zyberk, 1992). In order to calculate this indicator we refer to the PedShed Analysis (fig.4).

-‐ Rate of connectivity. Counting how many points of interest are included in a defined area, like for example inside the ¼ - mile radius walking area. Subcategories like for example retail, green areas, transportation can be the object of the analysis (refer to the figure below). This analysis produces maps that can describe the distribution of accessibility values if computed on a fine-grained grid. For example we propose the following procedure: - To count the number of existing activities per subcategory (we consider: residential,

shops, offices, health, entertainment, utilities); - To calculate the variety index (or diversity index) of the categories that are present

around the point of observation. Simpson Diversity Index = 1- Σ (n/N)2 where n = the total number of units in a single category N = the total number of units in all categories.

3. Pedshed analysis (week 2-3):

-‐ Pedshed analysis. PedShed analysis aims at identifying the permeability of the street network for pedestrian (fig.4). Students are invited to take the map with the pedestrian street network and trace a circle with radius of 400 meters. The analysis has to be performed inside the circle only. Starting from the centre of the circle we have to take all possible paths people can take for a linear distance of 400 meters. In order to do this, please measure a solid poly-line drawn in the centre of the street and stop when the poly-line reaches 400 meters. Once all possible ways have been drawn, build the perimeter that includes all the reachable places. These latter have to be filled with a uniform colour (for example in red, like in Figure 4 left, presented below). Please, refer to the lots inside the block as the minimum units to be considered to trace the reachable area: this allows highlighting also small portions of blocks in case of large blocks, that otherwise would have been totally included or discarded. After this step, the percentage of the reachable area can be computed by simply calculating the ratio of the reachable area (take the entire area of the polygon, streets included) divided by the area of the circle.


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Fig. 4. An example of a PedShed analysis. Source: ISTP Murdoch University and Western Australia Ministry for

Planning, 2001.


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The table below (tab.1) summarizes previous indicators and should be used as a reference by students.

Table 1. The list of indicators presented in WP1.

An example of application of connectivity and accessibility indicators inside an urban design code: the LEED ND pilot version NB: The following section is not part of the exercise, but it only describes an example of an application of urban connectivity and accessibility indicators inside an urban design certification procedure, namely LEED ND in its pilot version. This procedure can be taken into account by students once the general master plan has been developed. For instance, the following subsections refer to those parts of the procedure that explicitly apply connectivity indicators.


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-‐ LEED ND| Smart Location & Linkage, SLL Prerequisite 1: Smart Location OPTION 2 – ADJACENT SITE WITH CONNECTIVITY (fig.5). Locate the project on an adjacent site with pre-project connectivity of at least 150 intersections/sq. mile within a half circle using a radius cantered on the midpoint of the adjacent portion of the project perimeter. The radius of the half circle must be ¼ mile, or the length of the adjacent portion of the perimeter, whichever is longer; and if the project contains streets, its connectivity cannot be less than the connectivity of the surrounding area measured within the half circle; and design and build the project with at least one through-street and/or non-motorized right-of-way (non-motorized rights-of-way may count for no more than 10% of the total) intersecting the project boundary at least every 800 feet.

Fig. 5: LEED ND, Smart Location, adjacent site

with connectivity

-‐ LEED ND | Smart Location & Linkage, SLL Credit 1: Preferred Locations OPTION 2 – CONNECTIVITY (fig.6). Locate the project in an area that has the following connectivity within a 1 mile radius from the perimeter of the site boundary: a. 400 or more intersections/square mile or greater (5 points) b. 300-400 intersections/square mile (3 points) c. 200-300 intersections/square mile (1 points)

Fig. 6. LEED ND. Preferred Locations, connectivity


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-‐ LEED ND | Neighbourhood Pattern & Design, NPD Prerequisite 3: Connected and

Open Community Required OPTION 1 – PROJECTS WITH INTERNAL STREETS (fig.7) Design the project such that its internal connectivity is at least 150 intersections/square mile. Designate all streets and sidewalks that are counted toward the connectivity requirement as available for general public use and not gated. Gated areas are not considered available for public use, with the exception of education and health care campuses, and military bases, where gates are used for security purposes.

Fig. 7. LEED ND. Connected and Open

Community. Projects with internal streets

-‐ LEED ND | Neighbourhood Pattern & Design, NPD Credit 6: Street Network (fig.8).

Locate and/or design the project such that its internal connectivity, and/or the connectivity within a 1/4 mile radius from the geographic centre of the project, falls within one of the ranges listed in the following table: Connectivity (intersections/sq. mile) Points Earned > 300 and ≤400 1 > 400 2

Fig. 8. LEED ND. Connectivity schemes of street networks.


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WP2. RASTER CITIES - Image processing techniques for the analysis of 3-D urban models

Introduction to the technique In order to accomplish this exercise you need to carefully follow instructions presented below: Prerequisites: 0. Please install the software Matlab on your computer (version 7 or higher). 1. Please install the software 3D Studio Max on your computer. Materials provided to the students 1. A series of scripts (Matlab codes) 2. An excel table where students are invited to collect all the results emerging from their calculations NB: This analysis does not require particular skills in programming. Basic commands and instructions will be provided by staff in a step-by-step way. Only simple commands to input models and data, run the scripts and read the results are required. * This text is extracted from (Morello & Ratti, 2007)

Why was the technique created? Both aspects concerning the wellbeing of people in outdoor and indoor spaces are relevant, in order to achieve the environmental quality of urban spaces. In fact, the delicate relationship existing between the assessment of the urban fabric and the design of open spaces defines the urban environmental quality and assesses the success of a city. This wise balance inside the urban form is surprisingly tangible in numerous historical city centres and was generated through a long process of transformations over time. Today, cities evolve rapidly and the slow process of adaptation of urban shape to meet human needs and sustain ecological diversity is no longer feasible. As an alternative way aiming to manage the complex set of environmental variables in the frame of rapid urban changes, the proposed set of tools is presented. It allows to investigate simultaneously different environmental aspects, such as solar access, cross ventilation, energy consumption, etc., in relation to the arrangement of the urban fabric. Algorithms defined in the Matlab environment and derived from image processing, can work with very simple raster images of the urban texture stored in Bitmap format. Potential users might simply use the proposed set of tools or implement new algorithms to meet their needs and compare different design solutions from the environmental and morphological viewpoint. In fact, using this set of tools, a new paradigm for assessing the environmental consequences generated by the urban texture is investigated. This is centred on the relationship existing between environmental indicators and urban morphology: the question is if - and in what measure - the correct arrangement and the shape of the urban fabric alone might improve the environmental behaviour of the city. With the aim of making an effective environmental quality starting just from morphology, several design tools can be developed, assessing new potentialities related to the form of human settlements. For instance, the energy-based morphogenesis of the built environment could be intended as the first step towards the improvement of the sustainability of cities with no additional cost due to the application of complex technologies. The technique revealed itself to be useful for simulations on alternative design schemes over large-scale masterplans and for extensive and complex urban areas, helping to make decisions supported by measured quantification. In particular, the technique demonstrates the potential of digital urban models based on raster images for the analysis of the city, which brings with it many advantages such as fast computability, flexibility, precision and comparability of results obtained from several algorithms. The tools were initially created to compare the environmental behaviour of different urban configurations. In fact, the technique might be desirable in comparative studies, whereby environmental indicators can be mapped and visualised for different design projects, and consequently critical situations can easily emerge. Especially in the case of limited resources, the identification and quantification of environmental deficiencies on the urban texture could help in programming intervention phases more efficiently. In fact, the rapid measurability of several environmental indicators on each point of the urban space is simply based on the same digital support as the unique input, which is analysed and processed through a series of imposed algorithms. We focus on the city and its development scenarios for the future. Further work and applications of the proposed technique might promote a new concept of urban environmental architecture, based on new design strategies and generative rules for the prediction of innovative morpho-typological solutions derived by environmental indicators. For whom was it created? An optimal site or design solution is almost unachievable. Often, requirements for different environmental issues are in opposition. For instance, the exigencies of indoor spaces and outdoor spaces differ, since good exposure to the sun can reduce energy consumption inside buildings, but at the same time this action can limit the environmental quality of the resulting shaded open spaces, which suffer from the reduction of the sky view factor. Also, a higher level of compactness reduces heat losses but also reduces gratuitous gains from solar irradiation and does not encourage natural ventilation. In spite of the impossibility of achieving the optimal urban design scheme from the environmental viewpoint, urban designers should not give up looking for the best practice in relation to the aim of sustainability. The


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intention is to propose a technique, which enables the analysis of the many aspects involved in the assessment of urban environmental quality, in the belief that only a wide spectrum of environmental indicators can support conscious design choices. The technique described here was created in an effort to provide quick but reliable tools for academic and research purposes in the field of environmental design, with the aim of making them accessible to both public administrators and practitioners. On the one hand, decision makers and public administrators could use them in evaluating the impact of different design solutions on the urban fabric and in finding more sustainable design alternatives; on the other hand, urban designers could make use of the proposed tools at the initial design process, in order to take advantage of local environmental opportunities. At the least, the proposed analyses based on the comparison of alternative design solutions from the viewpoint of urban environmental quality in the heuristic phase, could mean a significant improvement in terms of energy efficiency and environmental comfort. Urban design students could also benefit from the diffusion of the low-cost library of functions, which might integrate traditional approaches to urban planning with a higher consciousness in the field of urban environmental sustainability. In fact, an open source initiative could diffuse, ameliorate and increase the now available set of tools, making the technique become more sophisticated.

How does it work? The methodology is based on the use of very simple raster models of cities, called Digital Elevation Models (DEMs). DEMs reproduce the geometry of the urban fabric and are produced by regularly spaced matrices of elevation values, which contain 3-D information on 2-D digital support, stored in Bitmap format. Implementing software algorithms derived from image processing, it is possible to develop efficient strategic tools for analysing and planning the sustainable urban form, measuring geometric parameters and assessing radiation exchange, energy consumption, wind porosity, visibility, spatial analyses, etc. Results are extremely fast and accurate. However, their application to architecture and urban studies has not yet been fully explored. The first application of DEMs in architecture originated at the Martin Centre, University of Cambridge (P. Richens, C. Ratti, K. Steemers) and explored the potentialities of this low-cost and powerful technique. Today, through the increased availability of Digital Elevation Models (DEMs) from lidar (Laser Imaging Detection and Ranging, i.e. a technology that determines distance to an object using laser pulses), the proposed technique could open the way to new low-cost raster-based urban models for planning and design. In the absence of satellite imagery, the DEM can be derived from the digital 3-D model produced with CAD and rendered with software such as 3-D Studio Max that enables a view from the top and from infinite distance to be generated and at the same time differentiates the elevations of buildings on a greyscale colour map. Once the plan with the heights of the objects is created in a bitmap format, the latter can be easily processed by the proposed algorithms that read the image as a square matrix. Environmental indicators are the subject of the algorithms defined in the Matlab environment.

An application of the technique The tools reveal themselves to be a feasible way to assess the environmental quality of urban spaces. Under the broad definition of environmental quality, both aspects related to energy efficiency and human comfort are taken into account: on the one hand, the aim is to quantify the potential energy efficiency derived from the capacity of the urban fabric to take advantage of passive gains at the city scale; on the other hand, aspects of perceived comfort in urban open spaces are investigated, among others, or through visual preference analyses, through the definition of thermal conditions. Environmental parameters include solar access (solar paths, mean shadow density, solar gain through solar envelopes, sky view factors), energy consumption (surface-to-volume ratio and passive/non-passive zones), cross ventilation, wind porosity, urban canyon height-to-width, pedestrian accessibility and visual perception of open spaces through isovist fields. For instance, algorithms explore rules based on natural rhythms that define the morphogenesis of buildable volumes in the city and encourage the solar access of the urban fabric (for temperate climates) through an energy-based reinterpretation of the 'Solar Envelope' concept ('Iso Solar Surfaces'), first introduced by R.L. Knowles (1974, 1981). Not just the sun, but other natural forces as well help in modelling the urban environment: the urban metabolism, in particular the thermal exchanges and the natural ventilation occurring over cities generate macro- and micro-climates, influencing the perceived comfort and the environmental quality in general. Moreover, algorithms based on the calculation of 'sky view factors' over extensive urban portions enable the urban form to be linked with the generation of the urban heat island. In fact, the phenomenon of the urban heat island is related to those environmental indicators which profoundly depend on design choices, such as urban materials on horizontal and vertical surfaces, the vegetation density on open spaces and the shape coefficient of street canyons. Maps containing the identification of critical situations are produced, in order to define strategies of intervention on large urban areas. Furthermore, the broader definition of environmental quality considers human wellbeing in open spaces, in particular the psycho-physiological aspects related to the perceived experience of the urban form. Useful tools for measuring pedestrian accessibility, visual perception and visibility of open and built spaces through isovist-fields and the reinterpretation of Lynch's visual elements are presented. Isovists describe the field of vision of the observer located at a specific point in space and represent for instance the base unit for the construction of the model. Starting from the analysis of the geometrical characteristics of these figures, and from the sequence along a visual path, it is possible to draw a conclusion on the visibility analysis of the built urban fabric.


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1. Build Digital Urban Model (week 1). NB. Map sources: digital maps, reconstruction of the 3-D model of the 3 case study areas (see WP0).

Construction of the Model. Exporting the 3-D model to 3D Studio Max. We have to purge all the non used layers in Autocad and remove all the non extruded objects. After, the model can be exploded so that the volumes become 2-D surfaces. We suggest to save the file before exploding it, so that we don’t lose the 3-D information. We can save the model as a dwg file.

-‐ Construction of the Digital Elevation Model in 3D Studio Max. The model is finally ready to be imported in 3D Studio Max. When we import the model, it is grouped as a single object. We only have to move it in the z direction, so that the highest point of the model is positioned at z=0. In other words, the entire model is below 0. If we know the height of the tallest building, we only have to impose z= max height/2 since the centre of the object corresponds to the centre of mass of the object itself (fig.9).

Fig 9. Importing the 3-D model from Autocad in 3D Studio Max

-‐ The model is ready to be rendered

and to create the DEM. This can be done by using a special render element, the so called “ZDepth”, which corresponds to a projection (the plan from the top) from infinite distance, whereby the gray of the pixels contains the information of the normalized height of the object. Please, refer to the instruction of figure 10 to set the correct parameters for the render. First, the “ZDepth” element has to be activated and then the rendering heights have to be set (Zmax = maximum height measured on the model in meters; Zmin = 0).

Fig. 10a. Settings on the render window of 3D Studio Max.


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-‐ How to correctly save the model. Once the render is ready you have to save the image. Please, it is very important that you save the image as a 8 bits file, greyscale (refer to the saving options window).

Fig. 11. The render output using the render element Z-Depth Refinement of the Model on 3-D Studio Max. After the model has been saved as an image, it needs to be exported to Photoshop to be cropped on the exact perimeter of the 800m x 800m perimeter we have established. This step is necessary because the output on 3-D Studio Max has a black background that goes beyond the boundaries of the selected area. In order to exactly crop the image on the established perimeter, you have 2 options:

-‐ You can directly work on the Autocad model, by adding a sort of frame, to be placed externally to the 800m x 800m area. The frame has to be treated like a building, a solid volume to be extruded (please, consider that you can extrude it for a height that does not have to exceed the tallest building on the model).

-‐ You can superimpose on Photoshop the map with the exact boundaries as a separate layer (for example the wireframe of the Autocad model in EPS format) and then cut the DEM on those limits. Please, use transparencies to superimpose both levels in a precise way.

-‐ After the image has been cropped you should verify that the model is a perfect square (the image size should have the same number of pixels per side) and save it as BMP or TIFF (8 bit, greyscale).

2. Use of Matlab codes (week 2).

Processing with MatLab. Once the image of the DEM has been created we can use it as the main input for the Matlab codes. We can start with the environmental analysis on the urban form. Before doing this some basic functions in Matlab are presented aiming at introducing the student in the technique. Again, no knowledge in programming is required and the following instructions are only presented in order to better understand the logic behind this work.


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STEP 0 – Image processing of 2-D arrays in MatLab

The technique uses 2-D arrays with intensity values that correspond to the height of the pixels. For example, the following array named sample (fig.12) is a 3 by 3 matrix.

Fig 12. Visualisation of the array sample in 2-D and 3-D. Sample = [1,3,4;0,0,2;5,3,1].

STEP 1 – Importing an image on Matlab When we import the model into the Matlab environment we translate the information enclosed in the image into a 2-D array [rows, columns], whereby the values of intensity correspond to the height of the pixels of the image as explained in the paragraph above. The commands listed here help to better understand the logic of the image processing technique, but are not necessary steps for the analysis, since these commands are already contained inside the scripts that are provided to the students. For instance, on the command window of Matlab you can digit the command imread (i.e. image read) followed by the path where the image is stored (use for example the DEM created above):

[a,c] = imread('C:\Users\...\models\DEM.tif'); a… a represents the name we assign to the array with the image. It is important to calibrate the image in order to assign the proper dimensions (length and width). This is possible by assigning the dimensions in meters. For example, the site measures 800 m by 800 m. widthx=800; widthy=800; Moreover, we can decide the resolution of the image by defining the extension of the pixel itself (pixelwidth). Here we put 2 meters as the width of the pixel. pixelwidth=2; The size of the image is obtained with the formula: sizex=widthx/pixelwidth; sizey=widthy/pixelwidth; We can finally adjust the initial array a by considering the real width in meters (as defined above) and imposing a filtering method on the pixels (in this case we use the filter nearest, which assigns the intensity value of the nearest pixel in the corresponding neighbourhood). This procedure is contained in the command imresize (image re-size) and we can derive the new adjusted array b: b=imresize(a,[sizex sizey],'nearest'); Once the image is calibrated on the xy plan, we have to adopt the same criteria to calibrate its vertical dimension z. The imported image is a raster image of 8 bits (i.e. 1 byte) in greyscale. This means that the scale of possible intensity values contains 28 = 256 possible values. In fact, the imported image is a matrix with pixels of value in a range from 0 to 255 colours. It is necessary to normalize these latter values by assigning the real height of the pixels in metres.


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b=b.*(maxheight/max(max(b)));

where: maxheight is the known height in metres of the tallest building (input) whereas the command max(max(b)) (or con max(b(:))) calculates the maximum value of the 256 colours array, in this case 255. If we summarize, the inputs required to calibrate the DEM are: maxheight, widthx, widthy, pixelwidth.

STEP 2 - Visualizing the results In order to visualize the maps In order to visualize the arrays (images, maps) we can use the command imagesc and define some details of the figure (title, colourmap, scale of the colours, proportions of the image). imagesc(f_total); title('shadow''s map’); colourbar; colourmap(1-grey); axis square; If the image requires a rapresentation in three dimensions, we can refer to the command meshz and other related commands to improve the quality of the final image. Matlab is not particularly suited for graphical purposes. meshz(b'); daspect([1, 1, 1]); colourmap(1-grey); view(135,30) shading interp camlight right

How to use the scripts The students are not required to have skills in programming and need only to proceed with some easy actions to run the scripts. Once Matlab is open, you have to open the editor window. The editor window is used to write and save complex functions and codes and works with the same language as the command window. From this latter we can open the scripts. To run the codes we have to press F5. Before we launch the scripts we have to enter the DEM and calibrate the image as explained above (the same procedure described above on the command window is valid on the editor window. Some minor details on the 2 here presented scripts follow later.

How to export the outputs (maps and numbers) Outputs of the model are maps and numerical results of the analyzed variables. Maps can be exported using the figure interface and by setting the export setup (you can change the fonts, background, etc). When the image is ready, you can copy the image using the command copy figure and export them to another program. To create an independent file with the image directly from Matlab, you can digit on the command window:

imwrite (A, namefile, format, resolution) where: A… is the name of the array/map namefile… is the name assigned to the image format… is the format of the image resolution… is the resolution of the file in pixels/unit For example, if we want to save the image with the mask of the built surfaces, we can digit: imwrite (built_mask, 'builtmask.tif', 'tif', 'Resolution', 300) Several saving options can be set. Please, refer to the Matlab help window (digit herlp imwrite on the command window) or the online guidebook


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On the other hand, numerical values are visualised on the command window at the end of the process. Results are also summarized in a single array (in these scripts they have the suffix Array, for example Array_solar_access) which can be exported to Excel: in fact, by clicking on the selected variable Array on the Workspace, the Array Editor pops up and from this values can be copied and pasted on Excel.

3. Environmental analysis (week 2-4): Here the list with the 3 scripts to be used for the analysis:

-‐ morphology_glasgow.m -‐ shadowsonadem_milano_mask.m -‐ skyviewfactor_milano_mask.m

Morphology indicators. Please, refer to the MatLab code: morphology_glasgow.m The script calculates some indicators related to the form of the built environment like the covered area, the built volume and an estimation of the total area of the vertical surfaces. In addition, the code derives some density indicators.

Fig 13. Morphology indicatprs: building density. Source: Porta and Morello, 2006. Shadowing. Please, refer to the MatLab code: shadowsonadem_glasgow.m The script computes the solar path and the shadowing maps at hourly intervals for a specific day of the year (we suggest to compute for example the solar paths during typical days of the year, such as the equinoxes and the solstices). Hourly maps are The script contains the function sunpositionfunction.m in order determine the exact position of the sun according to solar geometry formulae. Outputs of this code are maps with the shadowing conditions at the different hours of the day (fig.14). Moreover, a synthetic map named shadows density map, represents the superimposition of all hourly values on the same map. Numeric results are expressed as square metres of shadow generated on the urban space. The inputs needed to run the script are:

-‐ The calibration of the image (dimensions of the area in metres and the maximum height on the site);

-‐ The day of the year chosen; -‐ The latitude of the location.


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For example: Day=21; Month=12; latitudo=45;

Fig. 14. Morphology indicatprs: shadowing maps. Source: Porta and Morello, 2006. The subroutine presented below is derived from the simple shadow-casting script introduced by Ratti and Richens (2004) and represents the basis for further implementations of macros dealing with the translation of DEMs (fig.15). First, the three components of the vector pointing towards the sun are defined. Then, we compute the components of an opposite vector, scaled so that the larger of the x and y components is just 1 pixel, and the z component is adjusted to the image calibration (fig.15a). If we translate the DEM (fig.15b) by the x and y components, and simultaneously reduce its height by subtracting the z component, we get part of the shadow volume. If we continue translating and lowering by multiples of this vector, and take the maximum of this volume with that previously calculated, we build up the whole shadow volume (fig.15c). The process can be stopped when all levels are zero or the translation has shifted the volume right off the image. To reduce the shadow volume to an actual map of shadows on the roofs and ground level of the city, the original DEM is subtracted from the shadow volume (fig.15d). Pixels with negative or zero values are in light; positive values are in shade. The above described routine allows the rapid computation of the so-called ‘shadow volume’, i.e. the volume of air that is in shadow over a given urban DEM. The interest of this approach is that the raster analysis is impressively fast and can deal with great vectorial complexity (due to the fact that processes are reduced to simple operations on pixels). Hence, at every hour it is possible to produce the map and the content is saved in the matrix f. This array contains 3 dimensions corresponding to the x and y values (0 or 1) of the shadows map and the hour. f_tot(:,:,hour)=f


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Fig. 15. The computation of the shadow volume through the image-processing of DEMs; from left: (a) the translation

macro, (b) the DEM for a square of height z, (c) its shadow volume where gray levels indicate the heights of each pixel and (d) the map of shadows.

Sky-view factor. Please, refer to the MatLab code: skyviewfactor_glasgow.m This script calculates the sky view factor (SVF) of the urban texture. This computation does not depend on the solar path but only on the urban geometry. The routine is based on the same computation presented above for the shadow casting on the DEM (shadowingfunction.m). Instead of using the sun position to cast shadows, the script uses in this case a function that chooses random sun positions on the sky vault. For example, 100 random iterations are sufficient to produce a quite fine-grained map with SVFs. In this case, the outputs are the map of the SVFs and a number of numerical values of SVFs computed on the whole site, or separately on the roofs and the open spaces.

Fig. 16. Morphology indicatprs: shadowing maps. Source: Porta and Morello, 2006.


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4. Raster City Report (RCR) (week 4-5) Designing Raster City Report (RCR). Refer to the excel file provided by the instructors during the class, on which students can collect all the results obtained as outputs from the various scripts. RCR’s structure mirrors the subdivision of the work in 3 main WPs. All contents have to be collected in one single album, A3 format, divided into the 3 sections as listed below: Section 0 (Introduction) In this short report the following content needs to be addressed:

1. Introduction to the brief with a general description of the required tasks and objectives (please, refer to this document). The text should not exceed 3 pages (3000 characters).

2. Comment and experience about the conducted analysis: describe what you have learned from this analysis; eventual suggestions and critics to improve the analysis. The text should not exceed 3 pages.

Section 1 (ref. to WP1) The final report concerning WP1, which includes the following content:

1. The table with the indicators and maps (where required) for the 3 case-study areas as presented in the WP1 section;

2. The interpretation of results for each subsections; 3. Interpretation and conclusion (max 2 pages).

Section 2 (ref. to WP2) The final report concerning WP2, which includes the following content:

1. The table with the indicators and maps (where required) for the 3 case-study areas as presented in the WP2 section;

2. The interpretation of results for each subsections; 3. A general conclusion (max 2 pages).


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5.3 Deliverables The Raster City Report, as illustrated in the previous section, is considered the deliverable of this Analysis Package.


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5.4 References WP1. Connectivity and Accessibility of the Urban Texture Quoted references: Criterion Planners Engineers, 2001, INDEX PlanBuilder User Guide, Portland, OR

(http://www.crit.com/documents/planuserguide.pdf). Duany A.M., Plater-Zyberk E., 1992, “The Second Coming of the American Small Town,” The Wilson

Quarterly, 4, 19-50. ISTP Murdoch University and Western Australia Ministry for Planning, 2001, Sustainable Urban Design.

Practical fieldwork project, Text booklet at the Sustainable Design Course, Murdoch University, Perth, WA.

Kanski, K.J., 1963, Structure of Transportation Networks: Relationships Between Network Geometry and Regional Characteristics, University of Chicago Press, Chicago.

USGB, 2008, LEED for Neighborhood Development Rating System. (http://www.usgbc.org/ShowFile.aspx?DocumentID=6146)

Rodrigue, J. P. Comtois, C. Slack B. 2009, The geography of Trasport System, Routledge, New York. (http://people.hofstra.edu/geotrans/eng/ch2en/meth2en/ch2m1en.html). References for working instructions: Porta, S. Morello, E. (2006), Students’ work at Urban Design Course, Politecnico di Milano, Facolta’ di

Architettura Civile. WP2. Raster Cities Quoted references: Carneiro C., Morello E., Desthieux G., 2009, “Assessment of solar irradiance on the urban fabric for the

production of renewable energy using LIDAR data and image processing techniques”, in Sester M., Bernard L., Paelke V. (editors), Advances in GIScience, Lecture Notes in Geoinformation and Cartography, Springer.

Carneiro C., Morello E., Ratti C., Golay F., 2008, “Solar radiation over the urban texture: LIDAR data and image processing techniques for environmental analysis at city scale”, in Lee J., Zlatanova S. (editors), 3D Geo-information Sciences, Lecture Notes in Geoinformation and Cartography, Springer.

Mangiarotti A., Paoletti I., Morello E., 2008, “A model for programming design interventions aimed at reducing thermal discomfort in urban open spaces”, in Journal of Green Building, 3:4, College Publishing, Glen Allen VA, USA, pp. 119-129.

Morello E., Gori V., Balocco C., Ratti C., 2009, Sustainable urban block design through passive architecture: A tool that uses urban geometry optimization to compute energy savings, Proceedings of the 26nd International Conference on Passive and Low Energy Architecture, Quebec City, Canada.

Morello, E., & Ratti, C. (2009). SunScapes: ‘solar envelopes’ and the analysis of urban DEMs. Computers, Environment and Urban Systems, 33, 26-34

Morello, E., Ratti, C. 2009. A Digital Image of the City: 3-D isovists in Lynch’s Urban Analysis. Environment and Planning B: Planning and Design , 36, 837-853.

Morello E., Ratti C., 2007, “Raster Cities: image processing techniques for environmental urban analysis”, in Thwaites K., Porta S., Romice O. (editors), Urban Sustainability through Environmental Design: approaches to time, people and place responsive urban spaces, Spon Press, London, UK, pp. 119-122.

Ratti C., Baker N., Steemers K., 2005, “Energy consumption and urban texture”, Energy and Buildings, 37:7, 762-776.

Ratti C., Morello E., 2005, "SunScapes: extending the ‘solar envelopes’ concept through ‘iso-solar surfaces’", Proceedings of the 22nd International Conference on Passive and Low Energy Architecture, Beirut, Lebanon.

Ratti C., Richens P., 2004, “Raster analysis of urban form”, Environment and Planning B: Planning and Design, 31:2, 297–309.

References for working instructions: Porta, S. Morello, E. (2006), Students’ work at Urban Design Course, Politecnico di Milano, Facolta’ di

Architettura Civile.

Documents

MSc/PgDip/PgCert Urban Design 2009 / 2010 · (Studio 1a); 2. Urban Design Strategy. You will propose a Strategic Plan and a Concept Plan, together forming the Urban Design Strategy,