Geodesign and a Pattern Language Curtis White and Howard Ward ESRI 2011 International User Conference Paper

GeoDesign and A Pattern Language Curtis White and Howard Ward ESRI 2011 International User Conference Paper

Introduction This paper explores the practicality of applying GIS‐based “GeoDesign” tools to evaluate the “goodness” of a land use or building design solution. The authors use criteria defined in one of the classic texts in architectural design, “A Pattern Language”, as benchmarks of goodness and describe their concepts and work applying GIS to the measurement of existing land use patterns in Tucson, Arizona.

The authors wish to gratefully acknowledge the timely contributions of GIS staff in Pima County and the City of Tucson for providing data used in the analysis portion of this paper.

What is GeoDesign? At the 2009 ESRI User’s Conference, Jack Dangermond introduced the term “GeoDesign” during his keynote address and discussed it in some depth in “GIS:Designing Our Future” [ArcNews 13:2, Summer, 2009. Since then, many people have been trying to figure out exactly what “GeoDesign” is. • In “GIS, Design, and Evolving Technology” [ArcNews 31:3, Fall, 2009], Dangermond elaborated this idea, tying it to the Landscape Change Model first proposed by Dr. Carl Steinitz [1990]. • At the GeoDesign Summit 2010, Dr. Michael Goodchild in his presentation [Spatial by Design: Understanding the Special Role of GIS] boiled this down to two key phases: “Input, edit and record a sketch”, and “Evaluate, analyze, predict, modify, improve”.

We will use and elaborate on Goodchild’s two phases later in this paper.

What is A Pattern Language? In the late 1970’s, Christopher Alexander and a team of fellow architects and designers published “A Pattern Language: Towns / Buildings / Construction” [Alexander, et.al, 1977]. This book hereafter referred to as “APL”) described a formal list of design patterns that the authors believed to be cross‐ cultural and time‐invariant, patterns that have developed naturally over the centuries by people actually building homes, neighborhoods, towns and cities. The patterns are arranged in a loose hierarchical order, starting at the regional level and descending through cities, communities, neighborhoods, housing clusters, individual houses, rooms, and ending finally in details of ornamentation. The authors rate each pattern as to their confidence in its universality with some based on very solid research and evidence and others more tenuous and hypothetical. They also invite the reader to observe, think and extend the concepts presented in APL and even to develop their own pattern language.

APL is a complex and thought‐provoking document, and it has given us many ideas that might be applied to the GeoDesign concept, and some of these will be discussed in this paper.

The three ideas that we wish to focus on are: • The patterns described in APL can be used as a way of evaluating how “good” a design is from a viewpoint likely quite different than that normally taken by geo‐designers.

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• These same patterns can be used to identify places in the current built environment that need improvement. • Many of these patterns can be identified using GIS and thus can be directly used in analysis, or even in an automated design‐checking process, during the evaluation of a design.

For purposes of illustration, we discuss five of the 253 patterns given in Alexander’s book and give a detailed example using one of these.

GeoDesign Explored The two fundamental phases of GeoDesign, as defined by Dr. Goodchild, and referenced in the introduction are as follows: • Phase 1: Input, edit and record a sketch • Phase 2: Evaluate, analyze, predict, modify, improve

Phase 1 In Phase 1, the fundamental idea is that we need to be able to represent our design within GIS. (For the purposes of this paper, that means within the ArcGIS paradigm.) How that design is represented depends very much on what it is we are designing. For a building, we may represent the design as a 3‐D Sketch, but we also might be satisfied with a building footprint representation. For a roadway, we may choose a centerline representation or a “road‐as‐polygon” approach, where the polygon might represent the road right‐of‐way or edge‐of‐pavement. In any event, it is important to note that at this stage we are not making a detailed “engineering” representation of a design; it is a conceptual representation and must incorporate those features in which we are interested.

It is absolutely necessary that Phase 1 include a corresponding representation of the designed object’s environment. Any design can only be evaluated (Phase 2) in the context of what exists now. And by “corresponding” we mean that however the design is represented, “reality” must be represented in a congruent way. It does no good to look at a “3‐D Sketch” of a proposed building if its environment (context) is represented as building footprints, or vice versa

Phase 2 Phase 2 is where the proposed design is evaluated and judgment rendered on how “good” the proposed design is. As Dr. Goodchild mentions, this evaluation means analyzing the design (which must be done in relation to the design’s environment), predicting the impact of the change (perhaps using simulation models or expert opinion) and then modifying the design based on these activities (which starts a new cycle of analysis and prediction). At some point, we decide that the current design proposal is “good” or the “best of the alternatives” and can no longer be improved. The decision then is to whether or not implement the design. So the designer must begin with the question, “How do we determine which design alternative is ‘better’”? What standards will be used to render what is, frankly, a value judgment?

As a first step we may state our design objectives in quantitative terms. For a roadway design, we might couch this in terms like “minimizes travel time” or “capable of handling this peak load”. But such criteria GeoDesign and A Pattern Language ESRI UC 2011 Page 2 are insufficient because, while quantifying certain aspects of the design, they ignore the larger questions that involve the design’s environment. What are the visual and sonic impacts of the roadway on the neighborhoods through which it runs? Do we add landscape elements to address the first issue and sound walls the second? But even this misses impacts that can occur; are we “splitting” a viable neighborhood, rendering the pieces unviable? Are we cutting off resident’s access to local amenities such as parks or playgrounds? Are we making it more dangerous for children to travel to school?

Is there a way to actually know what questions we need to ask and how to evaluate the answers? We believe that APL offers one promising path for investigation.

A Pattern Language Explored Patterns are organized in a loose, hierarchical fashion with closely related patterns presented in a group. The hierarchical order applies not only to the groups themselves, but to the individual patterns, and the result is a web‐like network of design concepts. Navigating this network of ideas is challenging, but is assisted by the format that the patterns are presented in. It also helps that the patterns are numbered. To quote the authors:

The elements of this language are entities called patterns. Each pattern describes a problem which occurs over and over again in our environment, and then describes the core of the solution of that problem, in such a way that you can use this solution a million times over, without ever doing it the same way twice.

For convenience and clarity, each pattern has the same format. First, there is a picture, which shows an archetypal example of that pattern. Second, after the picture, each pattern has an introductory paragraph, which sets the context for the pattern, by explaining how it helps to complete certain larger patterns. Then … [in bold type] a headline gives the essence of the problem in one or two sentences. After the headline comes the body of the problem. This is the longest section. It describes the empirical background of the pattern, the evidence for its validity, the range of different ways the pattern can be manifested in a building, and so on. Then, again in bold type… is the solution – the heart of the pattern – which describes the field of physical and social relationships which are required to solve the stated problem, in the stated context. This solution is always stated in the form of an instruction – so that you know exactly what you need to do, to build the pattern. Then, after the solution, there is a diagram, with labels to indicate its main components. [Introduction, pp. x ‐ xi]

We will, of necessity, abbreviate our examples, giving the problem, solution and a brief discussion, emphasizing how we see GIS being used with this pattern.

Example Patterns Below we discuss, and illustrate with examples, five patterns from different hierarchical levels in the language’s network. With one of these patterns ( “T Junctions”), we document and discuss our application of GIS tools in evaluating how well the pattern, as described in APL, serves in evaluating the “goodness” of that type of roadway design.

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Pattern 3, City Country Fingers (CCF) CCF is part of a larger pattern which we can abbreviate as “marking the edge of cities”. CCF assists in a companion pattern (Distribution of Towns) required for a balanced region by controlling the balance of urban land and open countryside within towns and cities themselves.

Problem: Continuous sprawling urbanization destroys life, and makes cities unbearable. But the sheer size of cities is also valuable and potent.

Solution: Keep interlocking fingers of farmland and urban land, even at the center of the metropolis. The urban fingers should never be more than 1 mile wide, while the farmland fingers should never be less than 1 mile wide.

Whenever land is hilly, keep country fingers in the valley and the city fingers on the upper slopes of the hillsides—AGRICULTURAL VALLEYS (4). Break the city fingers into hundreds of distinct, self‐governing subcultures—MOSAIC OF SUBCULTURES (8), and run the major roads and railways down the middle of these city fingers—WEB OF PUBLIC TRANSPORTATION (16), RING ROADS (17)….

This pattern is listed as one in which the authors have a moderate degree of confidence. It is based in part on Gallup poll research showing 87% or those surveyed preferred to live in a non‐City environment and that the trend was away from city preference. They also point to the trend of diminishing acres of undeveloped land and the ascendency of expensive resorts, summer camps and retirement communities so people can escape the sterility of the city. The hypothesis presented is that human beings are genetically wired to desire contact with the natural environment on a regular basis. “If we wish to re‐establish and maintain the proper connections between city and country, and yet maintain the density of urban interactions, it will be necessary to stretch out the urbanized area into long sinuous fingers which extend into the farmland...” [APL, page 24]. We think that the “flight to the suburbs” that the US experienced during the 1950’s might well be an expression of this innate human tendency.

How might a GeoDesigner bring GIS resources to bear on the CCF pattern? First, there is the issue of its validity, especially in the designer’s locale. One starting point would be to confirm the “diminishing open space” hypothesis. Mature GIS operations are likely to have archived data sets that can help establish this issue. In Pima County, archived Assessor parcels go back into the late 1990’s. These parcels have a land use code which can identify vacant, agricultural and park lands. Land ownership attributes can help determine whether the vacant land is open space for public use or merely vacant awaiting development. Historical aerial imagery can be used to determine the quality of such lands, including some measure of vegetation density.

Second, the value of such urban open space to the community needs examination. APL cited polling research. With the advent of social media free web‐based survey sites and smart phones, it is conceivable that a similar poll could be run cheaply and easily, even on a regular basis over a sustained period. To the extent that smart phone users allow their location to be tracked and to the extent this information can be obtained from the ISP’s, a spatial analysis of users and their preferences could be

GeoDesign and A Pattern Language ESRI UC 2011 Page 4 conducted. Other means of testing the value of urban open space might be mapping of health or crime statistics and conducting a proximity analysis to open space.

In terms of the analysis of benefits of the CCF pattern itself, it would be necessary to first identify any existing CCF land use patterns. GIS offers many opportunities, from aerial imagery interpretation to urban shape analysis (perhaps using some type of “compactness index”) to sophisticated connectivity analysis using existing land use and various topographic and barrier layers. GeoDesigners in communities with mature GIS operations are limited only by their imaginations.

Pattern 63, Dancing in the Streets (DIS) DIS is part of a larger pattern which we can abbreviate as, “provide public open land” and, at a finer scale, “evening activity in public”.

Problem: Why is it that people don’t dance in the streets today?

Solution: Along promenades, in squares and evening centers, make a slightly raised platform to form a bandstand, where street musicians and local bands can play. Cover it, and perhaps build in at ground level tiny stalls for refreshment. Surround the bandstand with paved surface for dancing—no admission charge.

Place the bandstand in a pocket of activity, toward the edge of a square or a promenade— ACTIVITY POCKETS (124); make it a room, defined by trellises and columns—PUBLIC OUTDOOR ROOM (69); build FOOD STANDS (93) around the bandstand; and for dancing, maybe colored canvas canopies, which reach out over portions of the street, and make the street, or parts of it, into a great, half‐open tent—CANVAS ROOFS (224)….

This pattern is classified as quite theoretical by the authors. It is based on a re‐emergence of street activity in the United States after a historically brief period of decline. They cite the controversy in San Francisco in the 1970’s over street musicians as an example of such re‐emergence. They remind us of the long‐standing American tradition of bandstands and jubilees in the park, and they note the continuing existence of DIS in many other cultures around the world.

Our own experience in Tucson bears out the validity of the DIS pattern. For many years, a downtown redevelopment effort (Rio Nuevo) has progressed through fits and starts. Downtown Tucson is dominated by busy cross‐town streets, large office buildings and parking structures, and street fronts with little or no retail or other human‐scale activity. Pockets are slowly developing (revitalized older theaters , a restored train depot with a restaurant, and a regular schedule of off‐hour events such as Downtown Saturday Night , 2nd Saturday and “Meet Me at Maynard’s ), but these are the exception rather than the rule and such development has been driven more by personal initiative than any active design/redevelopment effort itself.

One means for testing the validity of this design hypothesis in your own community is to collect and map cultural activities that occur there. Using ads from local papers, the internet or other sources you can capture the addresses of these events and geocode them. Over time you may see the emergence of GeoDesign and A Pattern Language ESRI UC 2011 Page 5 night‐life activity centers which can then be analyzed for DIS design patterns. You may also discover patterns not described in APL (an expressed hope of Alexander and his colleagues).

Pattern 95, Building Complex (BC) BC is part of a larger pattern which we can abbreviate as “overall arrangement of a group of buildings”. It is also a key transition point in APL: “…this pattern, the first of the 130 patterns that deal specially with buildings, is the bottleneck through which all languages pass from the social layouts of the earlier patterns to the smaller ones which define individual spaces” [APL, page 469].

Problem: A building cannot be a human building unless it is a complex of still smaller buildings or smaller parts which manifest its own internal social facts.

Solution: Never build large, monolithic buildings. Whenever possible, translate your building program into a building complex, whose parts manifest the actual social facts of the situation. At low densities, a building complex may take the form of a collection of small buildings connected by arcades, paths, bridges, shared gardens and walls.

At higher densities, a single building can be treated as a building complex, if its important parts are picked out and made identifiable while still part of one three‐dimensional fabric.

Even a small building, a house for example, can be conceived as a “building complex”— perhaps part of it is higher than the rest with wings and an adjoining cottage.

This pattern is listed as one in which the authors have a high degree of confidence. It is based in part on the hypothesis that the BC pattern fosters a more human‐scale built environment, one that is organized along the lines that human beings organize themselves. They cite as evidence for BC a survey of visitors to two public service building complexes in Vancouver, British Columbia. One constituted a complex of three‐story historic buildings and the other a group of monolithic office towers. Visitors to the smaller, older complex most often mentioned the friendliness of staff and their competence. Visitors to the office towers most often cited good physical appearance and equipment. They theorize that the towers are too big, inducing a form of free‐floating anxiety because visitors and occupants can’t really grasp that size of building and the organization that houses it.

Our own experience in Tucson bears out the validity of the BC pattern. Fourth Avenue is a shopping district that lies about half way between the University of Arizona and Downtown. The street is lined with many small shops, some sharing a common, larger building, but often in separate (closely adjacent) buildings or converted small houses. Merchants along the Avenue formed an association many years ago, and through this organization have added many features, including numerous trees and other landscaping, bike racks, small inset “performing” courtyards and other amenities. The result has been an area that is almost continuous occupied by shoppers, students, tourists and locals; it has been remarkably resilient during the current recession. The size and variety of buildings along the street appear to play a significant role in this.

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One means of applying GIS to testing the BC pattern is to map and survey users and visitors to these places. Assessor records often store the number of stories as an attribute of parcels. Aerial imagery can be used to confirm or even map building footprints. As mentioned previously, the ubiquitous nature of smart phones and social media sites could make a public survey approach realistic.

It should also be possible to map such locations with GIS and compare their economic activity with areas that are represented by more monolithic structures. The responsiveness of citizens to various government services might also be measured based on the type of structure the service is housed in (along the lines of Alexander’s original investigation).

Pattern 106, Positive Outdoor Space (PODS) PODS is part of a larger pattern we can abbreviate as “fix the position of individual buildings on the site, within the complex”.

Problem: Outdoor spaces which are merely “left over” between buildings will, in general, not be used.

Solution: Make all the outdoor spaces which surround and lie between your buildings positive. Give each one some degree of enclosure; surround each space with wings of buildings, trees, hedges, fences, arcades and trellised walks, until it becomes an entity with a positive quality and does not spill out indefinitely around corners.

Place WINGS OF LIGHT (107) to form the spaces. Use open trellised walks, walls and trees to close off spaces which are too exposed—TREE PLACES (171), GARDEN WALL (173), TRELLISED WALK (174); but make sure that every space is always open to some larger space, so that it is not too enclosed—HIEARCHY OF OPEN SPACE (114). Use BUILDING FRONTS (122) to help create the shape of space. Complete the positive character of the outdoors by making place all around the edge of buildings, and so make the outdoors as much a focus of attention as the buildings—BUILDING EDGE (160). Apply this pattern to COURTYARDS WHICH LIVE (115), ROOF GARDENS (118), PATH SHAPE (121), OUTDOOR ROOM (163), [and] GARDEN GROWING WILD (172).

This pattern is also listed as one in which the authors have a high degree of confidence. It is based on the hypothesis that outdoor spaces which are not positively shaped will generally not be used. They cite as evidence for this pattern a 1965 study of European city squares classified as lively or not. The lively squares shared two properties: (1) they were partially enclosed and (2) they were connected so that one lively square led into another. They theorize that a primitive instinct for security causes humans to seek out partially enclosed spaces from which they can look out.

Again, our observations from Tucson tend to validate the PODS pattern. Some of the liveliest spots in Tucson are along 4th Avenue, where merchants define smaller spaces from wider areas in the sidewalks using tables, displays, artwork and even plants.

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A GIS approach to validating a PODS pattern might involve selecting areas in your own community known to be either lively or “dead zones.”. Using aerial imagery a Geo‐Designer can capture the outlines of structures. It may also be possible to devise a mathematical measure of enclosure and apply it to the outline of structures.

Pattern 50, T Junctions (TJ)

TJ is part of a larger pattern we can abbreviate as “local road and path network”.

Problem: Traffic accidents are far more frequent where two roads cross than at T Junctions.

Solution: Lay out the road system so that any two roads which meet at grade, meet in three‐ way T junctions as near 90 degrees as possible. Avoid four‐way intersections and crossing movements.

At busy junctions, where pedestrian paths converge, make a special raised crossing for pedestrians, something more than the usual crosswalk—ROAD CROSSING (54)….

This pattern is classified as quite theoretical by the authors. It is based on maps from an empirical study that compared traffic accidents for different street patterns over a five‐year period. The results showed that three‐way intersections had fewer accidents than four‐way intersections. They cite further evidence that the safest T Junctions are ones that approach 90 degrees.

TJ the pattern we chose for application, as well as discussion, of GIS. This decision was based in part on the availability of data and our familiarity with the spatial analyses involved. Following is an outline of our approach to a GIS evaluation of TJ in the City of Tucson with more technical notes placed in the Appendix.

• Acquiring the Data – the greater Tucson/Pima County area has a long history of GIS development, professional networking and GIS data sharing. This culture allowed us to make a single email inquiry to the GIS Department at Pima County which, in addition to supplying their own accident data, also used their contacts at the City of Tucson to provide us with data within the City limits as well. It points out the importance of a mature GIS in all its aspects, including interpersonal relationships and an active user network.

• Processing and Analyzing the Data (detailed steps in Appendix A) – we chose to focus on an urban area of Pima County to ensure a high density of accident data. We used the current City of Tucson boundaries as a proxy for this area. We converted the ends of street segments to points which resulted in overlapping points where segments met at intersections. We then applied a “Collect Events with Rendering” tool to produce a point file with a count of the number of overlapping points at each intersection. Finally, we used a near analysis to assign traffic accident counts to intersections and then summarized the results

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• Evaluating the Results – the results of the analysis show that T Junctions in the City of Tucson account for 64 percent of the intersections, but only 51 percent of the traffic accidents.

• Refining the Analysis – This analysis was not intended to be an exhaustive and robust analysis of the validity of APL’s T Junction pattern. It is merely a guide and an illustration of the way in which GIS tools may be applied to the evaluation of a development pattern. There are a number of additional data sources and analyses that can be applied to validate the T Junction hypothesis in the City of Tucson including the following:

o Normalize intersection accident counts by the number of cars that pass through the intersection each day. It is likely the four‐way intersections see a lot more traffic and that could be the reason they experience a higher proportion of the reported accidents.

o Evaluate intersections by number of lanes. The greater the number of lanes, the greater the number of collision opportunities that should be taken into account as well.

o Evaluate intersections by the angle of approach. Intersections less than 90‐degrees may pose a higher accident risk due to visibility factors.

Summary The purpose of this paper was to introduce GeoDesigners to the concept of a design framework, how it might guide their thinking, and how it might be applied using GIS tools. “A Pattern Language” was used as an example of a design framework, but there are other frameworks out there as well. The important point is that development designers become aware of the importance of using a comprehensive framework, so as to avoid a singular focus (e.g. efficiency of traffic flow) and to evaluate their designs within a broader contextual framework. The extension of GIS into GeoDesign provides a natural gateway to bring a broad contextual view down to specific design issues.

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APPENDIX Summary of T‐Junction Analysis Using ArcGIS 10, SP2

Data Processing • Appended all of the TPD‐provided traffic accident data into a single file.

• Downloaded latest street network file from Pima County.

• Used “Intersect” tool in ArcMap to create points at each intersection. o Note that there will be one point for each intersecting street, so there will be three overlapping points at a tee intersection

• Used City of Tucson boundary to select intersections o 13,696 of 35,355 County‐wide intersections in current COT boundary

• Ran “Collect Events with Rendering” to produce a point file with an ICOUNT field added to count number of overlapping points.

• Ran the “Generate Near Table” function to find all accidents within 1 foot of each intersection

• Summarize near table on IN_FID to get a count of accidents within 1 foot of each intersection

• Added a VALENCE field to the near table (Sum_TAwIn1FtofIntersections_INFID), joined the intersection points and transferred the value of ICOUNT to VALENCE. Valence Intersections in County (no. of connecting streets) 2 1500 ((11%) 3 8,791 (64%) 4 3,383 (25%) 5 19 6 2 7 1

• Broke join and summarized on VALENCE, using SUM option on COUNT_IN_FID field to get total number of accidents for that valence.

No. and % Valence of reported accidents (no. of connecting streets) (2010) 2 67 (2%) 3 1,868 (51%) 4 1,717 (47%) 5 12 6 2 7 0

• SO: T Junctions represent 64% of the intersection but only 51% of the accidents.

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