Parametric Urbanism and Environment Optimization: Toward a Quality Environmental Urban Morphology

International Journal of Environmental Research and Public Health

Article Parametric Urbanism and Environment Optimization: Toward a Quality Environmental Urban Morphology

Yingyi Zhang 1 and Chang Liu 2,*

1 School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture, Beijing 100044, China; [email protected] 2 Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China * Correspondence: [email protected]; Tel.: +86-159-2179-3823

Abstract: Parametric thinking has found wide acceptance in both the building industry and environmental sciences. In the context of environmental urban morphology however, parametric thinking has been neglected. This paper critically assesses environmental optimization with a focus on parametric urbanism. The analysis addresses two research questions: “Can parametric thinking and its associated approaches facilitate an environmental urban morphology?” and “If yes, can parametric urbanism support environment optimization in complex urban areas?” Methodologies include a case study in Beijing, qualitative and quantitative analysis, parametric modeling, and environmental simulation. Results indicate that parametric techniques can effectively simulate environmental urban morphology by generating parametric models. These models provide a rational foundation for schematic decision-making about optimizing environments in urban development. Findings include a critique of parametric thinking as applied to city environments and insights about the potential uses of parametric techniques to support quality environmental urban morphology. Citation: Zhang, Y.; Liu, C. Parametric Urbanism and Environment Keywords: parametric urbanism; environmental optimization; integrated approaches; urban mor- Optimization: Toward a Quality phology; quality environment Environmental Urban Morphology. Int. J. Environ. Res. Public Health 2021, 18, 3558. https://doi.org/10.3390/ ijerph18073558 1. Introduction

Academic Editors: Yu-Pin Lin and From the 1980s, parametric thinking has evolved the ﬁelds of architecture, construc- Marta Otero tion, and engineering. The computer-aided tools of parametric thinking have redeﬁned the relationship between architectural design and shaping urban morphology. It has also

Received: 20 February 2021 impacted human-environment generation and regeneration. To some extent, the built Accepted: 25 March 2021 environment has become a product of computable processes and integrated approaches. Published: 30 March 2021 Parametric thinking enables tangible and intangible systems to be incorporated into design proposals that are removed from computational tool specificity. It asks architects to start Publisher’s Note: MDPI stays neutral with design parameters, rather than preconceived or predetermined design solutions [1]. with regard to jurisdictional claims in According to Kolarevic (2005), parameterization has its own rules, methods, and features. published maps and institutional affil- It rejects static or rigid solutions, instead considering variables as lively, dynamic, and iations. mutable elements of a design system. A parametric approach accommodates the need for recurring adaptation across design project processes [2]. Parametric thinking has been implemented in urban design practices. Pioneer architects and urban designers such as Zaha Hadid and Patrick Schumacher have carried out Copyright: © 2021 by the authors. parametric design practices on multiple urban scales. Their design schemes for the Kartal Licensee MDPI, Basel, Switzerland. Masterplan and Singapore One-North, for example, revolutionized people’s cognition of This article is an open access article urban space. (Yes, the avant-garde forms did result in some controversy.) Consequently, distributed under the terms and recognition of the potential usage of parametric tools intensified technological investments conditions of the Creative Commons in this area over the last decade [3]. Parametric urbanism arises from these practices. As Attribution (CC BY) license (https:// per Gu (2018), parametric modeling originates from generative design and analysis. It creativecommons.org/licenses/by/ is an integrated approach based on rules or algorithms (e.g., in generative grammars or 4.0/).

Int. J. Environ. Res. Public Health 2021, 18, 3558. https://doi.org/10.3390/ijerph18073558 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2021, 18, 3558 2 of 13

evolutionary systems) [4]. Driven by the features of cities as hybrid territories, the methodologies of urbanism take advantage of parametric tools to optimize urban-scale design models and manipulate the dynamics of urban morphology. As Nagy (2009) mentioned, from the first experiments using parametric tools in architectural design processes, it was clear that these tools could similarly benefit urban design projects, including higher-scale urban cases [5]. Parametric urbanism is based on parametric design systems in which the parameters of a given object are declared but not that object’s performance. It offers an opportunity for architects and urban designers to optimize urban forms and spatial construction through the lens of parameters. Parameters work as variables within a system that can control design performance from the minutiae of architectural detail to large-scale developments. For example, the width and length of a street can delineate parameters for improving a street’s form and function. Urban designers can modulate data values with parametric modeling software, such as Grasshopper 3D (Robert McNeel and Associates. Seattle, Washington, DC, USA). They might make the street more pedestrian friendly or more convenient for automobiles according to their functional analysis of a specific street. In the case of multiple street networks on a large scale, parametric software can analyze complex parameters intuitionally. The results of this analysis can then help architects and urban designers reveal urban environment ideals. As Leach (2009) declares, design lead by classic geometric shapes can prove inflexible when adaptivity is essential [6]. Parametric urbanism, conversely, frees design solutions from directions led by axial forces or the position of urban objects. Design is instead guided by the distribution and density of the constructed urban fabric, by sensitivity to deformations within territories, and by the development of compositional gradients (such as the transformations across the heights of buildings) [3]. Parametric urbanism encompasses an inherent appreciation of the spatial individualities that define a city. Environmental optimization has become more and more the essential driver of urbanism. Optimization is a search process for a solution addressing the special conditions of a specific problem [7]. Environmental optimization in urbanism refers to creating, or recreating, livable and sustainable urban areas through optimal schema. Numerous environmental challenges, such as microclimates in high-density cities, affect urban growth directions. Masterplans tend to be oriented toward optimizing physical urban morphology to create environmentally friendly cities by achieving human comfort and reducing fossil fuel energy demands [8]. Therefore, urbanism processes require significant consideration of environmental influences. As Salat and Adhitya (2009) say, urban morphology is the specific texture of a city informed by such parameters as building forms, street patterns, and population density [9]. The tangible forms of urban morphology deal with the physical composition of an urban environment and the spatial structure and form of various activities. Urban morphology has potential to embrace not simply a two and three-dimensional methodology that studies and characterizes the physical evolution of cities but one that facilitates a fuller understanding of intangible forms, such as the functional and socioe- conomic evolution; it also has a contribution to make in the sustainable evolution and development of cities worldwide [10]. Form-based code has been working as an approach in a plenty of urban projects for morphology shaping. It creates or recreates a specific urban morphology primarily by controlling physical form, with a reduced focus on land use, through city or country regulation [11]. However, form-based code is still paper based. Its code generation can be inefficient, especially during the amendment process in large-scale projects [12,13]. Hence the parametric techniques are introduced into the morphological shaping processes to improve efficiency. To analyze the use of parametric techniques in environmental optimization, this paper limits the research range of urbanism to its physical representation and urban morphology, rather than population, economy, or society. The goal of the study is to use parametric techniques to facilitate environmental optimization in a complex urban context to achieve a quality environmental urban morphology. Research question is as follows: Can parametric thinking and approaches facilitate environmental Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 3 of 13

Int. J. Environ. Res. Public Health 2021 18 environmental ,optimization, 3558 in a complex urban context to achieve a quality environmen- 3 of 13 tal urban morphology. Research question is as follows: Can parametric thinking and approaches facilitate environmental urban morphology? If yes, can parametric urbanism support environmentalurban morphology? optimization If in yes, complex can parametric urban areas? urbanism Accordingly, support the environmental potential- optimization ity of parametricin techniques complex urban as applied areas? to Accordingly, environmental the potentiality urban morphology of parametric is analyzed, techniques as applied assessing the benefitsto environmental that parametric urban urbanism morphology may ishave analyzed, in creating assessing quality the living beneﬁts envi- that parametric ronments. urbanism may have in creating quality living environments.

2. Materials and2. Methods Materials and Methods The method frameworkThe method incorporates framework two incorporates phases (Figure two phases 1). The (Figure first phase1). The consists first phase consists of of data collectiondata and collection model generation. and model Data generation. collection Data works collection to understand works tothe understand physical the physical morphology ofmorphology the research ofareas the and research prepare areas the and geographic prepare thedata geographic to generate data models. to generate models. Both physical andBoth geographic physical and data geographic are imported data into are the imported parametric into themodeling parametric software. modeling software. Parametric modelsParametric perceive models the existing perceive ur theban existing morphology urban in morphology a three-dimensional in a three-dimensional man- manner. ner. Methods includeMethods field include study, field statistical study, statisticalcalculation, calculation, quantitative quantitative analysis, and analysis, para- and parametric metric modeling.modeling. The result The is resultan experimental is an experimental process of process parametric of parametric modeling modeling for ana- for analyzing lyzing environmentalenvironmental urban morphology. urban morphology. This phase This targets phase the targets first theresearch first researchquestion question about about parametricparametric thinking thinking and approaches and approaches can facilitate can facilitate environmental environmental urban urbanmorphol- morphology or not. ogy or not. TheThe second second phase phase encompasses encompasses environmental environmental simulation simulation and and examination. Environmental Environmental simulationsimulation presentspresents the tendency tendency of of people’s people’s routing routing behavior behavior according according to the three- to the three-dimensionaldimensional models models generated generated in inthe the last last phase. phase. The The result result indicates indicates which which areas attract areas attract peoplepeople to totake take part part in in outdoor outdoor activates activates or or enjoy enjoy the environmental comfort.comfort. It also provides It also providesa a foundationfoundation forfor environmentalenvironmental examination. In In the the examination examination part, part, the the unattractive outdoor areas can be optimized by adjusting the parameters of the three-dimensional unattractive outdoor areas can be optimized by adjusting the parameters of the three-di- models. Methods consist of parametric modeling, simulation, and qualitative analysis. The mensional models. Methods consist of parametric modeling, simulation, and qualitative result is parametric layouts with scripts. Urban designers and environmental analysts can analysis. The result is parametric layouts with scripts. Urban designers and environmen- use the parametric layouts to optimize the environmental urban morphology according to tal analysts can use the parametric layouts to optimize the environmental urban morphol- the realistic demands. This phase contributes to answering the second research question ogy according to the realistic demands. This phase contributes to answering the second about whether parametric urbanism can support environmental optimization or not in research question about whether parametric urbanism can support environmental opti- complex urban areas. mization or not in complex urban areas.

Figure 1. Method framework. Figure 1. Method framework.

2.1. Data Collection 2.1.1. Material Data Types Urban morphology comprises both tangible and intangible forms. The former is composed primarily of the urbanscape’s appearance: the geometric form of its land, the

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various patterns of its functions, the layout of its buildings, etc. The latter involves cultural, social, and other intangible elements. This paper focuses on the tangible form of the material environment. Based on the figure–ground theory, the elements of the material environment include two primary categories: The figure category includes examples such as building lots, infrastructure, and outdoor barriers. The ground category incorporates green areas, open space, and squares [14]. Data collected in figure and ground categories encompass three aspects: building complexes, street networks, and open space. Environmental forces affect human comfort in a built environment, especially in high-density cities. Urban morphology shapes or reshapes environments, and vice versa. Whether generating an entirely new urban area or inserting an urban section in an existing area, the environment is inescapably changed. Six variables related to environmental urban morphology work to analyze environmental optimization in parametric urbanism: floor area ratio (FAR), air path width, road hierarchy, infrastructure, street canyon, and building height (Table1). The six variables are chosen because they are related to environmental urban morphology. They are commonly used to define urban forms in urban environmental research and morphological shaping projects, such as the Miami 21 Code, SmartCode Montgomery, etc. Each variable has a specific measurement manner. For example, FAR is a ratio, and air path width is measured in meters. The normalization method works to achieve dimensionless values. It converts the original value to data of comparable magnitudes. The normalized data can be directly embedded into the parametric modeling system. The average value of each area indicates the artificial features of each area. If the value is high, it means the area has artificial features. People may cluster in this area. The outdoor space may accommodate various outdoor activities, traffic flow, etc. During the simulation phase, if the urban area with high average normalized data shows unattractive environmental urban morphology, it means urban designers and environmental analysts need to reshape the morphology to meet functional requirements.

Table 1. Data types and descriptions.

Urban Urban Morphology Data Type Morphology Data Type Urban Morphology Aspect Data Type Aspect Aspect FAR Air Path Width (m) Infrastructure Building Complex Street Networks Open Space Building Height (m) Road Hierarchy Street Canyon Road Hierarchy Grade Motorway Width Grade Speed (km/h) Lane Total Width (m) Median Strip (m) 1 <30 ≥2 3.50 16–30 N/A 2 30–40 ≥2 3.50 20–40 N/A √ 3 40–60 ≥4 3.50 30–60 √ 4 60–80 ≥4 3.75 40–70 The above grading manners are based on the provisions of urban planning quota indicators [15] Infrastructure Grade Grade Description 1 Fire hydrant, plumbing well, trash can 2 Fire hydrant, plumbing well, trash can, substation box, border tree 3 Fire hydrant, plumbing well, trash can, substation box, border tree, streetlamp, bench 4 Fire hydrant, plumbing well, trash can, substation box, border tree, streetlamp, bench, street art, etc. The above grading manners are based on the ample degrees of the current situation FAR, ﬂoor area ratio. Int. J. Environ. Res. Public Health 2021, 18, 3558 5 of 13

1. FAR FAR acts as a quantized variable to represent the physical environment. It is the measurement of a building’s floor area in relation to the size of the lot/parcel that the building is located on. FAR value reflects building density, urban land usage, and the health of the urban atmosphere. 2. Air path width Air path width reflects the wind flow corridors between buildings or constructions. As per Ng (2010), the width of the street must be wider than the height of buildings; otherwise, skimming flow will dominate [16]. 3. Road hierarchy Road hierarchy helps to estimate vehicle flow. A high-grade road suggests high levels of auto-transportation. This may result in high carbon emissions and pollution. 4. Infrastructure Infrastructure describes municipal service facilities. To a certain extent, it indicates the population gathering. Ample infrastructure reflects high-level artificial activities and clusters of communities. 5. Street canyon Street canyon is defined by the width of a specific street and the building height along that street. The ratio of depth-to-width impacts natural ventilation. The higher this ratio, the weaker the wind at the ground level [16]. 6. Building height Building height reflects the skylines from a morphology perspective, as well as city ventilation from an environmental one. Taller buildings catch the wind passing through the city and downwash it into the city.

2.1.2. Geographic Data Types An urban zone in Shichahai, Beijing, forms the case study for data collection. The chosen zone is located in the central city of Beijing, adjacent to the main axis of the old city region. It consists of both conventional residential areas and modern business areas with a relatively high-density morphology. Shichahai fits the research target of using parametric instruments to analyze environmental urban morphology in a dense urban zone. Simulation and examination results will indicate the environmental quality in later experimental phases. The geographical information is obtained through an OpenStreetMap (OSM) format file. OSM is an editable map of the world supported by the OpenStreetMap Foundation, a non-profit organization registered in England and Wales. It has been enabled by portable satellite navigation devices [17]. OSM files contain a multitude of points of interest, buildings, natural features, and land-use information, as well as coastlines and administrative boundaries [18]. This paper uses building clusters from OSM to generate the figure–ground base map. The constraint node data of buildings are grabbed from OSM Online. We used Grasshopper 3D with its plug-in, Elk, a parametric modeling software, which translates the OSM file into coordinate points (Figure2). Int. J. Environ. Res. Public Health 2021, 18, 3558 6 of 13 Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 6 of 13

Figure 2. Constraint node data on the parametric modeling platform. Figure 2. Constraint node data on the parametric modeling platform. 2.1.3. Data Normalization 2.1.3. DataBesides Normalization the constraint node data, the six material data types, including FAR, air path width,Besides road thehierarchy, constraint infrastructure, node data, the street six materialcanyon, dataand building types, including height, FAR,were aircollected path width,through road field hierarchy, study. The infrastructure, research area street consists canyon, of eight and parts building according height, to were local collectedneighbor- throughhood divisions. field study. Each The neighborhood research area has consists official of edges. eight parts These according neighborhoods to local are neighbor- divided hoodby streets divisions. and roads. Each neighborhood The research hasteam official conducted edges. the These physical neighborhoods data collection are divided for each bypart. streets Collected and roads. data Therequire research statistical team analys conductedis to achieve the physical dimensionless data collection indicators. for each The part.normalization Collected datamethods require convert statistical the original analysis data to to achieve data of dimensionless comparable magnitudes. indicators. TheAfter normalizationreducing dependency methods convertand redundancy, the original the data original to data data of comparable can be grouped magnitudes. and classified After reducingdirectly—despite dependency being and recorded redundancy, with differing the original measurements data can be and grouped ranges and [19]. classified directly—despiteData normalization being recorded can include with differingboth data measurements-oriented processing and ranges and [ 19dimensionless]. processing.Data normalization The former canaims include to change both data data-oriented properties; processingthe latter to and compare dimensionless variables processing.with different The natures. former aims This to paper change uses data dimensionless properties; the proce latterssing to compareto normalize variables data withwith- differentout changing natures. its properties. This paper Statistical uses dimensionless approaches processing include three to normalize representative data normaliz- without changinging methods: its properties. Z-score normalization, Statistical approaches decimal includescaling threenormalization, representative and min–max normalizing nor- methods:malization. Z-score Z-score normalization, is appropriate decimal for situations scaling normalization,in which maximum and min–maxand minimum normaliza- values tion.cannot Z-score be conformed is appropriate but formean situations value and in which standard maximum deviation and minimumare conformed. values Decimal cannot bescaling conformed suits cases but mean where value data andcontain standard many deviation decimals. areThey conformed. are both unsuitable Decimal scaling for this suitsresearch. cases Consequently, where data contain min–ma manyx normalization decimals. They was are used both to unsuitable normalize for the this original research. data Consequently,collected for this min–max research. normalization was used to normalize the original data collected for thisAs research. per Formulas (1) and (2), the normalization process transforms data and their av- erageAs values per Formulas into a closed (1) and interval (2), the [0,1]. normalization Each part of process the case transforms study urban data zone and theirhas a aver-series ageof normalized values into adata closed that interval reflects [0,1]. the parame Each partters of of the its caseenvironmental study urban urban zone morphology. has a series ofThe normalized normalized data data that can reflects be input the parametersinto the parametric of its environmental modeling process urban without morphology. being Theimpacted normalized by variances data can in bedata input collection into the me parametricasurement modelingand range. process Data normalization without being re- impactedsults are presented by variances in Table in data 2. collection measurement and range. Data normalization results are presented in Table2. ei Emin Normalized ei (1) Emaxei − Emin Emin Normalized (ei) = (1) − where ei = the original data. Emin = the minimumEmax valueEmin of E range. Emax = the maximum wherevalue eiof = E the range. original If Emax data. is Emin equal = to the Emin, minimum the normalization value of E range. result Emax is 0.5. = the maximum value of E range. If Emax is equal to Emin,∑ the X normalizationk result is 0.5. x (2) n ∑n Xk x = k=1 (2) where x = mean value of normalized data. Xkn = X1. n = the number of the variables.

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where x = mean value of normalized data. Xk = X1. n = the number of the variables.

Table 2. Data Tablenormalization 2. Data results. normalization results.

Air PathAir PathRoad Hi- Infrastruc-Road Street Building Building LocationLocation No.No. FAR FAR Infrastructure StreetAverage Canyon Average WidthWidth (m) (m)erarchy Hierarchyture Canyon Height (m) Height (m) Part 1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 PartPart 1 2 0.70 0.00 0.33 0.00 0.50 0.33 0.00 0.33 0.00 0.25 0.00 0.41 0.00 0.00 Part 3 0.90 0.33 1.00 0.67 0.33 0.25 0.58 Part 2 0.70 0.33 0.50 0.33 0.33 0.25 0.41 PartPart 3 4 0.43 0.90 0.67 0.33 1.00 0.67 1.00 0.33 0.67 0.50 0.33 0.60 0.25 0.58 PartPart 4 5 0.55 0.43 1.00 0.67 1.00 1.00 1.00 0.33 0.67 0.50 0.33 0.73 0.50 0.60 Part 5 0.55 1.00 1.00 1.00 0.33 0.50 0.73 PartPart 6 6 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 PartPart 7 7 0.85 0.85 1.00 1.00 1.00 1.00 1.00 0.67 1.00 0.75 0.67 0.88 0.75 0.88

PartPart 8 8 0.97 0.97 1.00 1.00 0.50 0.67 0.50 0.75 0.67 0.75 0.75 0.77 0.75 0.77

2.2. Model Generation 2.2. Model Generation 2.2.1. Algorithm Logic The core2.2.1. of Algorithmparametric modeling Logic is the algorithm logic. An algorithm is expressed within a finite amountThe core of space of parametric and time and modelingin a well-defined is the formal algorithm language logic.for calcu- An algorithm is expressed lating a functionwithin in a mathematics finite amount and comput of spaceer science and time[20]. The and algorithm in a well-defined proceeds by formal language for cal- successive subtractions in two loops: if the test B ≥ A yields “yes” (or true) (more accurately theculating number b ain functionlocation B is in greater mathematics than or equal and to computerthe number a science in location [20 A)]. The algorithm proceeds then, the byalgorithm successive specifies subtractions B ←B − A (meaning in two the loops: number if b the− a replaces test B ≥theA old yields b). “yes” (or true) (more Similarly,accurately if A > B, then the A ← number A − B. The b process in location terminates B is greaterwhen (the than contents or equalof) B is to0, the number a in location yielding theA) greatest then, the common algorithm divisor specifiesin A [21,22]. B The←B algorithm− A (meaning starts from the an number initial b − a replaces the old state and initialb). Similarly, input and describes if A > B,a comput thenation A ← that,A −whenB. executed, The process proceeds terminates through when (the contents of) a finite number of well-defined successive states, eventually producing “output” and terminating B[23]. is 0, yielding the greatest common divisor in A [21,22]. The algorithm starts from an initial state and initial input and describes a computation that, when executed, proceeds 2.2.2. Softwarethrough and Scripts a finite number of well-defined successive states, eventually producing “output” A seriesand of terminating software has been [23]. applied to generate parametric models. According to Schumacher (2009), parametric design works as a style rooted in digital animation techniques, and2.2.2. its latest Software refinements and are Scripts based on advanced parametric design systems and scripting methods [24]. This paper utilizes Grasshopper 3D and Rhinoceros 3D (Robert McNeel and Associates.A series Seattle, of software Washington has, beenUSA) to applied edit scripts togenerate and build parametricmodels. models. According Grasshopperto Schumacher3D and Rhinoceros (2009), 3D rely parametric on algorithmic design logic to works create asgeometry a style with rooted a in digital animation visual programmingtechniques, language and itsinterface latest [25]. refinements They can effectively are based present on rational advanced models parametric design systems on canvas by editing components and parameter relations. Altering parameters causes changes toand propagate scripting throughout methods all functions [24]. This and paper the geometry utilizes to Grasshopper be redrawn [26]. 3D and Rhinoceros 3D (Robert DuringMcNeel the data and collection Associates. phase, the base Seattle, map is Washington, imported into Grasshopper USA) to edit 3D. In scripts and build models. the script Grasshoppersection, the existing 3D built and envi Rhinocerosronment and 3D proposed relyon schemes algorithmic are deconstructed logic to create geometry with a into parameters,visual components, programming and mathematical language interface rules. Figure [25 3 ].presents They the can relationships effectively present rational models between scriptson canvas and parametric by editing models. components The scripts consist and parameterof a series of script relations. units. Each Altering parameters causes unit contains parameter data adjustment slides. According to the data normalization result, datachanges values can to be propagate directly set throughoutby these slides. all A functionsparametric model and the of the geometry existing to be redrawn [26]. built urban morphologyDuring can the then data be collectionpresented on phase, the canvas the of base Rhinoceros map is3D. imported A variety into Grasshopper 3D. In of schemesthe can script also be section, presented the in real existing time by built changing environment the data adjustment and proposed slides. schemes are deconstructed into parameters, components, and mathematical rules. Figure3 presents the relationships between scripts and parametric models. The scripts consist of a series of script units. Each unit contains parameter data adjustment slides. According to the data normalization result, data values can be directly set by these slides. A parametric model of the existing built urban morphology can then be presented on the canvas of Rhinoceros 3D. A variety of schemes can also be presented in real time by changing the data adjustment slides.

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Figure 3. Scripts on Grasshopper 3D. Figure 3. Scripts on Grasshopper 3D. 2.2.3. Parametric Modeling 2.2.3.The Parametric parametric Modeling modeling process relies on the capacity to alter geometrical shapes whenThe dimension parametric values modeling are changed. process Programmi relies on theng capacitycodes, such to alter as scripts, geometrical define shapesthe di- whenmensions dimension of the generated values are model changed.s. Parametric Programming models with codes, various such asparameter scripts, values define and the dimensionsmorphological of thecharacteristics generated models. are more Parametric flexible modelsthan those with produced various parameter by conventional values andmethods. morphological Conventional characteristics methods normally are more flexibleoffer designers than those two-dimensional produced by conventional drafts from methods.which to obtain Conventional non-parametric methods models. normally Changing offer designers schemes two-dimensional therefore requires drafts considera- from whichble time to obtainand human non-parametric resources. models.Parametric Changing methods, schemes however, therefore enable requires the modeling considerable pro- timecess to and be human synchronous resources. with Parametric parameter methods, manipulation. however, When enable multiple the modeling scenarios process are re- toquired, be synchronous parameter withmanipulation parameter provides manipulation. real-time, When updated multiple models scenarios by manipulating are required, parameterscripts in Grasshopper manipulation 3D provides [18]. Numerous real-time, urban updated morphology models by layouts manipulating can be automati- scripts in Grasshoppercally generated 3D by [18 computerized]. Numerous urbancalculatio morphologyn. Three-dimensional layouts can modeling be automatically provides gen- an eratedapproach by computerizedfor urban designers calculation. and environmen Three-dimensionaltal analysts modeling to shape provides or reshape an approach environ- formental urban urban designers morphology and environmental toward a quality analysts urban to environment. shape or reshape The experiment environmental is based urbanon the morphology current existing toward morphology, a quality urban but th environment.e generated models The experiment are dynamic. is basedWhen onchang- the currenting the parameters existing morphology, of the models, but thevarious generated scenarios models and arepublic dynamic. space evaluations When changing will dis- the parametersplay in real-time. of the After models, a few various rounds scenarios of modi andfication, public designers space evaluations can select willthe most display well- in real-time.suited of the After resulting a few roundsmodels of for modification, further research designers or practice. can select The theadjusted most well-suitedparameters ofand the models resulting indicate models the schema for further on the research basis of or parametric practice. The scripts adjusted of the parametersShichahai urban and modelszone of indicateBeijing. the schema on the basis of parametric scripts of the Shichahai urban zone of Beijing.

2.3. Environmental Simulation and Examination Varying modelsmodels andand parametersparameters ofof urbanurban morphologymorphology makemake itit possiblepossible toto determinedetermine the environmentalenvironmental comfort of different urban textures and their impacts on people’s be- haviors [[27].27]. In dense cities, environmental urban morphology remains anan openopen problemproblem inin termsterms ofof shapingshaping urbanurban spacespace andand improvingimproving outdooroutdoor activities.activities. People preferprefer outdooroutdoor spaces with plenty of sunshine, a pleasant spacespace scale, open sight, and moderate winds. However, thethe perfectperfect outdooroutdoor space is difﬁcultdifficult toto provideprovide inin high-densityhigh-density cities.cities. For example, dense high-rise buildings lead to poor solar illuminance in some streets, although

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they may reduce the force of air currents in windy seasons. Sparse building clusters contribute better lighting conditions but can create strong winds—especially in cold winters. Individualthey may elementsreduce the such force as of windair currents or light in alonewindy areseasons. not enough Sparse building to support clusters environmental con- optimization.tribute better Therefore, lighting conditions instead ofbut relying can create solely strong on thewinds—especially thermal or windy in cold effect, winters. this paper usesIndividual comprehensive elements parameters such as wind to or both light simulate alone areand not enough examine to environmentalsupport environmental quality. A set ofoptimization. scripts simulate Therefore, and measure instead environmentalof relying solely urbanon the thermal morphology or windy through effect, the this prediction pa- ofper human uses comprehensive behavior trends. parameters The simulating to both simulate process and is examine based onenvironmental outdoor morphological quality. shapingA set of rather scripts thansimulate observing and measure a hundred environmental people urban to record morphology how they through spend the theirpre- time, diction of human behavior trends. The simulating process is based on outdoor morpho- where they go, and how long they stay in outdoor areas. The simulation is based on the logical shaping rather than observing a hundred people to record how they spend their outdoortime, where morphological they go, and shaping. how long Parametric they stay in modeling outdoor areas. software The simulation calculate the is based moving on route tendencythe outdoor rather morphological than a specific shaping. individual’s Parametric real modeling moving route.software This calculate assists the urban moving designers androute environment tendency rather analysts than toa specific take measures individu toal’s improve real moving the qualityroute. This of unattractiveassists urban areas. Thedesigners simulation and andenvironment measurement analysts are to establishedtake measures upon to improve the models the quality of the of Shichahai unattrac- urban zonetive provided areas. The in simulation the former and phase. measurement are established upon the models of the ShichahaiThe scripts urban setzone for provi environmentalded in the former optimization phase. comprise four sections (Figure4). SectionThe A limits scripts the set simulationfor environmental range tooptimization coincide with comprise the field four study sections boundary (Figure 4). of Sec- Shichahai. Sectiontion AB limits sets the simulation coordinate range points to coincide of attraction with the and field interference study boundary areas. of AttractionShichahai. areas includeSection downwind B sets the coordinate direction points zones, of people’s attraction movement and interference and destinations,areas. Attraction and areas southern zonesinclude with downwind relatively direction sufficient zones, sunlight. people’s Interference movement areas and include destinations, the natural and southern water system zones with relatively sufficient sunlight. Interference areas include the natural water sys- that cannot be crossed and the northern zones with insufficient sunlight. Section C runs tem that cannot be crossed and the northern zones with insufficient sunlight. Section C scripts to generate field lines. Section D presents the degree of environmental comfort with runs scripts to generate field lines. Section D presents the degree of environmental comfort chromatography.with chromatography.

FigureFigure 4. 4.Script Script setset for enviro environmentalnmental optimization. optimization.

3. Results and Discussion 3. Results3.1. A Parametric and Discussion Modeling System for Environmental Urban Morphology 3.1. A Parametric Modeling System for Environmental Urban Morphology This paper analyzes parametric thinking and techniques for facilitating environmental morphologyThis paper in analyzesan urban context. parametric An experiment thinkingal and parametric techniques modeling for facilitating system is generated environmental morphologyto support quality in an environmental urban context. urban An morpho experimentallogy in terms parametric of environmental modeling comfort system andis gen- eratedurban to morphology support quality pleasantness. environmental Environmental urban comfort morphology has become in an terms essential of environmental driver in comforturbanism and processes. urban morphologyUrban morphology, pleasantness. a major physical Environmental representation comfort of urbanism, has become re- an essentialflects the driver quality in of urbanism the built environment. processes. Urban Existing morphology, urbanism schemes a major rely physical heavily representationon man- ofual urbanism, methods. reﬂects The experimental the quality parametric of the built modeling environment. system applies Existing parametric urbanism techniques schemes rely to environmental urban morphology simulation and evaluation. This system facilities mor- heavily on manual methods. The experimental parametric modeling system applies para- phological shaping processes by providing rational calculation results, visualizing three-di- metric techniques to environmental urban morphology simulation and evaluation. This mensional models, and presenting built-environment consequences. systemThe facilities computer morphological calculates the shaping urban environm processesent by situation providing automatically rational calculationand presents results, visualizingthe results three-dimensional on a Rhinoceros 3D models,canvas when and presentingthe scripts sets built-environment are running in Grasshopper consequences. 3D.The Take computer the model calculates of Shichahai’s the urban morp environmenthology as situationan example: automatically The model indicates and presents the results on a Rhinoceros 3D canvas when the scripts sets are running in Grasshopper 3D. Take the model of Shichahai’s urban morphology as an example: The model indicates the

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degree of environmental attractiveness (Figure5). It reflects whether people are willing to choose a specific place for outdoor activities. The more field lines closer to red, the more willingthe degree people of environmental are to exercise, attractiveness communicate, (Figure or stay.5). It reflects The more whether field linespeople closer are will- to blue, the moreing topeople choose rejecta specific that place outdoor for outdoor environment activities. (Figure The more5a). Contributingfield lines closer factors to red, may the include morphologymore willing people scale andare to environmental exercise, communicate, wind or or light stay. situations. The more field Another lines closer model to presents ablue, measurement the more people of environmental reject that outdoor optimization environment (Figure (Figure5b). 5a). Areas Contributing that tend factors toward red may include morphology scale and environmental wind or light situations. Another indicate relatively pleasant environmental conditions. These areas are concentrated on the model presents a measurement of environmental optimization (Figure 5b). Areas that southeast part, closed to water banks and greenery. Areas that tend toward blue indicate tend toward red indicate relatively pleasant environmental conditions. These areas are thatconcentrated environmental on the southeast conditions part, can closed be improved.to water banks The and environment greenery. Areas might that be tend optimized bytoward adjusting blue indicate building that heights, environmental building conditions enclosures, can be acquiring improved. small-scale The environment open spaces, etc.,might to be support optimized optimization by adjusting inbuilding the environmental heights, building urban enclosures, morphology acquiring context. small- In real urbanscale open projects, spaces, changing etc., to support the modeling optimiza rangetion in may the environmental lead to different urban simulation morphology results. The rangecontext. could In real be urban with projects, a radius changing of various the modeling numbers. range It depends may lead on to howdifferent urban simu- designers andlation environmental results. The range analysts could usebe with it or a whatradius the of various design numbers. or research It depends requirements on how are. The Shichahaiurban designers model and is anenvironmental example to analysts run the use scripts. it or what The experimentthe design or aimsresearch to offer require- a universal simulationments are. The system Shichahai rather model than is only an example be used to in run one the specific scripts. urban The experiment zone. aims to offer a universal simulation system rather than only be used in one specific urban zone.

FigureFigure 5. 5. EnvironmentalEnvironmental optimization optimization simulation simulation in Rhinoceros in 3D Rhinoceros and Grasshopper 3D and 3D. GrasshopperFigure 3D. (a) indicates the field line simulation results. The more field lines closer to blue, the more people Figurepossibly (a reject) indicates that outdoorthe field environment. line simulation Figure results. (b) indicates The more a measurement field lines of closer environmental to blue, the more peo- pleoptimization. possibly rejectAreas thatthat outdoortend to red environment. shows relatively Figure pleasant (b) indicatesenvironmental a measurement conditions. of environmental optimization. Areas that tend to red shows relatively pleasant environmental conditions. The parametric modeling system has its roots in algorithmic logic and relies on the establishmentThe parametric of human–computer modelingsystem interactio hasn. Human–computer its roots in algorithmic interaction logic tools and pro- relies on thevide establishment an opportunity ofto human–computerjoin algorithmic logic interaction.with parametric Human–computer modeling. Grasshopper interaction 3D, tools providefor example, an opportunity translates algorithmic to join algorithmic logic to visible logic command with parametric components. modeling. People—in- Grasshopper 3D,cluding for example,those with translates limited coding algorithmic knowledg logice—can to visiblemanipulate command components components. to achieve People— includingcorresponding those models. with limited coding knowledge—can manipulate components to achieve Human–computer interaction modes work across the whole process of parametric corresponding models. modeling for environmental urban morphology. During the pre-modeling stage, general Human–computer interaction modes work across the whole process of parametric urban morphology characteristics and development trends are determined through pre- modelingliminary field for study environmental and data integration. urban morphology. Manual methods During constitute the pre-modeling the foundation stage, of general urbanparametric morphology modeling. characteristics After that, geographic and development information and trends data areintegration determined results through are pre- liminaryboth input field into parametric study and modeling data integration. software. According Manual to methods the manual constitute edition of theparam- foundation ofeter parametric types and modeling.their correlations, After that,a computer geographic automatically information calculates and models data integration that meet results arethe bothmanual input edition. into These parametric models modelingcan represent software. the existing According environmental to the urban manual mor- edition of parameterphology but, types more and importantly, their correlations, generate in afinite computer models automatically by adjusting parameter calculates values models that meetor correlations. the manual They edition. can depict These various models scenarios, can represent morphology the characteristics, existing environmental and socio- urban morphologyenvironments but,to generate more importantly, urban ideals. generate Comparing infinite with modelsconventional by adjusting paper-based parameter or values or correlations. They can depict various scenarios, morphology characteristics, and socioenvironments to generate urban ideals. Comparing with conventional paper-based or form-based approaches, the parametric modeling system offers a visualized modeling interface to manipulate parameters and achieve interactive performance feedback at the Int. J. Environ. Res. Public Health 2021, 18, 3558 11 of 13

early stage of morphological shaping. Therefore, the parametric modeling system facilities environmental urban morphology in the aspects of: • Efﬁciency The system synchronizes human thoughts with parametric models—avoiding the investment of time and human resources to generate manual models. • Flexibility It has the capability to support the analysis of multiple morphology types by adjusting parameter values or data. Parametric modeling systems have potentially wide-ranging applicability, from parametric urbanism to environmental sustainability. • Visualization Urbanism strategies become more foreseeable and visible. Strategies no longer exist only in text descriptions and static images. Parametric models provide vivid pictures for planners, urban designers, environmentalists, architects, municipalities, developers, and neighborhoods and communities. The models also can connect with networks for public participation toward a rational and practicable environmental urban morphology scheme. Although the parametric modeling system experiment explored in this paper can facilitate the generation of environmental urban morphology, it is not a panacea resolving all issues of urbanism. This paper simulates urban morphology with a few parameters related only to physical form. However, many more factors and parameters impact real life. For example, intangible factors such as cultural backgrounds, social development, and economic inﬂuences have not been considered here. Overemphasizing parametric techniques in physical environments may limit the success of addressing urban challenges.

3.2. An Alternative Approach to Environment Optimization Urbanism requires consideration of environmental impacts. Current science provides processes for evaluating indices such as the potential for global warming, acid rain, photochemicals, etc. However, environments—particularly those with complex urban textures—require consideration alongside other concerns, such as habitability, walkability, and sustainability. Natural daylight, wind, etc., are not in the range of urban morphology shaping. This paper introduces parametric simulation and evaluation to support environmental optimization in urbanism in an urban morphology context. The simulation and evaluation results indicate that parametric approaches can partly reflect people’s behavior trend and route tendency. Urban designers and environmental analysts decide where the unattractive morphology is and which areas should be improved. Pleasantness of an environment is a broad term. The simulation and examination process analyses a small part—morphological evaluation—to see whether the environment needs to be improved or not. The results clearly contribute to environmental optimization and parametric urbanism. Parametric tools are applied in living comfort for environmental optimization. This does not mean that parametric tools create a perfect system for predicting people’s behavior and their understanding or appreciation of environmental comfort. Human behavior is sub- ject to individual differences, and it varies with external effects. The simulated constraint lines indicate human movement only as it corresponds to the parametric model. Other factors that motivate people’s behavior, such as the price of real estate or the commercial quality of a shopping mall, are not reflected through parametric approaches. Addition- ally, the chromatography of environmental comfort in parametric models is calculated by computer processes. No clear evidence proves absolute accuracy. In reality, environmental comfort comprises multiple parameters such as solar altitude, season and temperature, air pressure, and wind speed and direction on a specific day. They are not included in the modeling scripts. Former researchers normally use sensors, e.g., light sensors, smoke detectors, or air quality sensors, to collect data on natural conditions. These data types are different from those used in this research. This research analyzes environment quality from the perspective of urban morphology. It is about physical forms, including building height, street width, FAR, etc. Parametric techniques can assist in environment quality improvement through modeling, simulating, and evaluating the environmental urban morphology, but it is not a panacea. It is not the sole solution for addressing all urban environmental issues. Overemphasizing parametric modeling results may create inaccu- Int. J. Environ. Res. Public Health 2021, 18, 3558 12 of 13

racies in decision-making. There is no evidence demonstrating that parametric models provide better solutions than conventional manual works. The parametric modeling system has its features of effective, automatic, and ﬂexible simulation. It is an alternative to assist decision-making rather than totally replacing manual methods. As a result, the parametric approach to environmental optimization may work best at the conceptual stage of urbanism, where it provides parameter- and data-based simulation and evaluation.

4. Conclusions This paper critically analyzes the principles and mechanisms of parametric technolo- gies. It generates an experimental parametric modeling system to demonstrate the potential application of that technology in environmental optimization to achieve a quality environmental urban morphology. Earlier environment research endeavors relied largely on collecting data and samples to evaluate environments. This paper proposes an alternative method. Parametric models of environmental urban morphology are generated to depict urban situations and trends. Parametric urbanism can include environmental optimization from the outset. The results answer the research questions as follows: Parametric thinking and approaches can indeed facilitate environmental urban morphology. Parametric urbanism does have the capability to support environmental optimization in complex urban areas. However, it does not necessarily result in perfect environmental optimization strategies. The parametric modeling system in this paper can work best at the conceptual stage of urban morphology shaping and environmental optimization analysis. The parametric simulation and evaluation can act as a reference for the study of urbanism. In the longer term, parametric methodologies will bring more convenient and dynamic support for urban environmental development. However, there is no clear evidence that it can produce better morphology design solutions than manual methods yet. The implementation of parametric technology requires further research.

Author Contributions: Conceptualization, Y.Z. and C.L.; methodology, Y.Z.; software, Y.Z.; valida- tion, C.L.; formal analysis, Y.Z.; investigation, C.L.; resources, C.L.; data curation, Y.Z.; writing— original draft preparation, Y.Z.; writing—review and editing, C.L.; visualization, Y.Z.; supervision, Y.Z.; project administration, C.L.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Beijing Municipal Education Commission, grant number KM202110016017. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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