Kraak, M.J.

Wang, Jiong

Wu, Cai

English

Street patterns are planar street layouts in a given urban area, which serve as tools for researchers and urban planners to comprehend the structure of urban environments. Nonetheless, the task of mapping street patterns for extensive inter-city studies remains daunting due to the lack of consistency in manual identification methods. With recent technological advancements and data accessibility, new avenues have opened for data-driven techniques in mapping street patterns. This study proposes an innovative framework that employs open data platforms and data processing methods, including network science and supervised machine learning, to map street patterns in cities across the globe effortlessly. Case studies were applied to six cities worldwide and made two key observations from the resulting maps. Firstly, the spatial distribution of street patterns mirrors the urban spatial structure within a city. Secondly, the innate differences between cities become apparent. This study is confident that the novel methodology not only unveils the urban spatial structure across diverse cities but can also be employed to investigate the connection between urban built form and urban activities.<br/

University of Twente Research Information

Urban Spatial StructureFrom a Street NetworkPerspective: Mapping Street PatternsWith RandomForest ClassificationCai Wu1[0000-0002-5578-5525], JonWang1 and Kraak, M. J. 11 University of Twente, Faculty of Geo-Information Scienceand Earth Observation (ITC),Hallenweg 8, 7522NH Enschede, TheNetherlandsAbstract. Street patterns are planar street layouts in a given urban area, whichserveas tools for researchers and urban planners to comprehend the structureofurban environments. Nonetheless, the task of mapping street patterns for exten-sive inter-city studies remainsdaunting due to the lack of consistency in manualidentification methods. With recent technological advancementsand dataacces-sibility, new avenues have opened for data-driven techniques in mapping streetpatterns. This study proposes an innovative framework that employs open dataplatforms and data processing methods, including network science and super-vised machine learning, to map street patterns in cities across the globe effort-lessly. Case studieswere applied to six cities worldwide and made two key ob-servations from the resulting maps. Firstly, the spatial distribution of street pat-ternsmirrors theurban spatial structurewithin acity. Secondly, the innatediffer-ences between cities become apparent. This study is confident that the novelmethodology not only unveils theurban spatial structureacrossdiversecitiesbutcan also beemployed to investigatetheconnection between urbanbuilt formandurban activities.Keywords: Street Pattern, Urban Spatial Structure, UrbanMorphology, Ma-chineLearning.1 IntroductionThe study of urban morphology is crucial to understanding the built environment [1],[2] in increasingly complex cities, and the street is a critical element of morphologicalinspection. Streets are also a very complex subject to study as countless factors areinvolved in how astreet performsand isperceived by people [3], [4]. To ease thestud-ying process, streets are simplified as a network layout with street junctions as nodesand streets as edges. Street patterns summarising the types of street network layoutswere also introduced to further help scholars and planners understand and designstreets. Lately, new opportunities have arisen for identifying and mapping street pat-ternswith the introduction of new dataand quantitativemethods. Thenew method hasgreat potential to enhance theability for large-scale urban studieswith street patterns.2This research exemplifies thestreet pattern asan instrument to reveal theurban spatialstructure in multiplecitieswith quantitativemethods.Themotivation for this study comes from two aspects: the extensive application ofstreet patternurbanstudieswhilelacking scalability and transferability; thequantitativerepresentation of street network providesnew tools for potential solutions for the con-straintsmentioned above that havenot been explored. Existing literaturehassuggestedthat, ingeneral, thespatial distributionof street patternscould reflect thedifferent urbanfunctionsof the citiesand thedifferent stagesof urban development due to changes inurbanmobility and planning ethos[5], [6]. Street patternswerefirst introduced to studythemorphological differencesbetween thecitiesbased on their visual distinctions. Ste-phen Marshall[4] introduced the ABCD types of street patterns, which associated themorphological propertieswith functionality and visual appearances. Thesearethemoregeneral street patternswhich arewidely applicable to different typesof studies [7], [8].However, the large-scalestudy iscomplex because it isdifficult to identify thesestreetpatterns in a consistent manner. More recently, scholars also identified more categori-sations of street patterns for specific purposes of study, like street patterns based onconnectivity for transportation studies [9]–[12]. These specialised street patterns aremore easily identifiable in large-scale studies but are also subject to limited fields ofstudy. Hence, theexistingmethods tomap street patternsareconstantly constrained bytheproblemof transferability and consistency. Street patternsareeither constrained bythe limited casestudy scaleor limited applicability in different study fields. With tech-nological advancement, access and calculation of street network data and metrics be-comes increasingly available, such asOSMnx and NetworkX making analysing streetnetworks intuitive. Chen [8] used the lasted deep leaning tomap thestreet morphologyby their visual identity. Such amethod allows for themass identification of street pat-ternsacrosslargeanddifferent study areas. However, themoreconventional supervisedmachine learning method was left unexplored with the advantage of using the abun-dancemetrics to explore thedifferent characteristicsof street networks.To allow for ageneralised global morphological study with expandability, thisstudyexploresasupervisedmachinelearningapproach, namely random forest, to identify thestreet pattern based on variousmetrics. The potential use case will be exemplified bymapping the street patterns to show the urban spatial structure. The casestudy is con-ducted in six major cities in Asia, Europe, andNorthAmerica. Each casestudy city hasgone through urbanisation, transferring from avernacular to acosmopolitanmegacity.2 MethodologyThe primary research objective of this research is to reveal the urban spatial structurein multiple cities by mapping the street pattern with a random forest classifier. Toachievethis, thisstudy hasproposedanew quantitativemethod to ensuretransferabilityacross different cities and consistency in identifying street patterns. There are threemain components. First, street networks are extracted from a single source of Open-StreetMap; second, all metrics were calculated quantitatively via NetworkX and OS-Mnx. Lastly, a random forest classifier was eventually applied to identify the street3patterns. To ensure the consistency and optimisation of the study unit, this studyadopted theStreet-based Local Area (SLA) as the primary study unit [13]. Four typesof street patterns were adopted from existing studies for mapping: Gridiron, organic,hybrid and cul-de-sacs. Themapping of the four street patterns across the six cities iseventually analysed and compared to show theurban spatial structure.2.1 Casestudy area and quantitativemetr icsSix citiesareselected as thecasestudy. Since they havedifferent sizesand administra-tivedivisions, thisstudy unifies thecasestudy area in asquarewith a25kmside length.SLA is the primary study unit compared to a grid or administrative boundary in pastresearch. ThisisbecauseSLAsaremoreoptimised for studying streets[13] andensuresuniversal applicability between different cities. Street network data areextracted fromOpenStreet Map, an open dataplatform. Theseensureauniversal datasource. By rep-resenting streetsasnetworks, several street morphological metricsarecomputed to cap-ture the physical characteristics of the streets. The computation is implemented usingnetwork analysistoolssuchasNetwork andOSMnx [14]. Thisstudy eliminatedmetricswith high correlations to ensure a better classification result. The result is 1054 SLAsacross six cities with 13 metrics. The explanation of the metrics is shown below inTable1.Table 1. List of Metrics.Street Length Calculate thegraph'saverageedge length.Diameter It is theshortest distancebetween the twomost distant nodes in thenetworkCircuity Circuity is thesum of edge lengthsdivided by thesumof straight-linedistancesbetween edgeendpoints.Or ientation En-tropyOrientation entropy is theentropy of itsedges' bidirectional bearingsacrossevenly spaced bins.k_avg graph'saveragenodedegree (in-degreeand out-degree)Self-loop Calculate thepercentageof edges that areself-loops in agraph.L-junction Theproportion of nodeswith two streetsconnectedT-junction Theproportion of nodeswith threestreetsconnectedX-junction Theproportion of nodeswith four streetsconnectedDegreePearson Compute thedegreeassortativity of agraph. Assortativity measures thesimilar-ity of connections in thegraph with respect to thenodedegree.Transitivity The transitivity or clustering coefficient of anetwork isameasureof the ten-dency of thenodesto cluster together.Averagecluster -ingTheclustering coefficient (Watts-Strogatz) isameasureof how complete theneighbourhood of anode is. Here, it is theaverageclustering coefficient overall of thenodes in thenetwork.Global reachingcentralitytheproportion of thegraph is reachable from thenode'sneighbours.Global Efficiency theaveragemultiplicative inverseof the shortest path distanceof all pairsofnodes42.2 Typesof Street Patternsand Random Forest ClassificationExisting studies have proposed various street patterns based on the different purposesof the study. This study adopted themost common street patterns: Gridiron, Organic,Hybrid and Cul-de-sacs [10], [15]. Gridiron is a typical street pattern with uniform di-rections, straight streets, and right-angled X-shaped crossroads. Theorganic street pat-tern is contrary to thegridiron, thestreet is curly with variousdirections, and thestreetjunction also hasadiverseappearance. Hybrid street patterns fall between thegridironand organic. Lastly, cul-de-sacs aremost recognisable for their dead-endsand circularstreets. 300 SLAsare randomly picked and manually identified for their street pattern.They are used for training and testing the random forest model. A standard randomforest classification procedure iscarried out, and theresulting classifier has thehighestaccuracy of 64%when using six features. Asapreliminary exploration of using super-vised learning to identify street patterns, this research deemsit adequate for thecurrentstudy.Fig. 1. The four street patterns3 Resultsand DiscussionThe random forest classifier indicates that the fivemost crucial attributesare: Circuity(14.4%), X-junction (12.2%), Street length (10.1%), DegreePearson (9.8%), and ori-entation entropy (8%), collectively accounting for 55% of theclassification outcome'sexplanation. Table2 presents theaveragevaluesof thesemetrics for each street pat-tern. Themajority of thesevaluescorrespond to thegeneral descriptionsof thepat-terns, with gridiron and organic typesat oppositeendsof thespectrum and thehybridsituated in between. Thecul-de-sac style standsout with thehighest circuity, streetlength, and the lowest X-junction and DegreePearson values. Most of thesecharac-teristicscan beeasily discerned through visual observation, which is likely due to theoriginal training dataset beingmanually identified based on thepatterns' visual dis-tinctions.5Table2. Theaveragevalue for TOP5 features.Gridiron Organic Hybrid Cul-de-sacsCircuity 1.023 1.071 1.063 1.098X-junction (%) 38.4 13.5 18.8 12.0Street Length (m) 119.3 82.8 115.5 131.7DegreePearson 0.343 0.104 0.160 0.047Orientation Entropy 2.803 3.349 3.132 3.265Several observations can bemadewith respect to urban spatial structureafter mappingthestreet patterns. Generally, most citiesshow aring structurewith two to three layersof street patterns. A core is present in the centre of the case study area, which can beconsidered thehistorical urbanarea. Dependingon thecity, thecore ismainly occupiedby either gridiron or organic types of street patterns. Surrounding the urban core is thesecond layer; it results from urban expansion and is considered the extension of theurban core. Street network in this layer generally formsat adifferent timeperiod com-pared to theurban core, which breaksaway from theconventional pattern and appearsto behybrid. Lastly, theoutermost layer at thecity'speriphery mostly showsaCul-de-sacs pattern. They are considered suburban areas. Hence, the urban spatial structurerevealed by thestreet pattern not only showsurban functions through theurban-subur-ban division but also reflects thedifferent stagesof urban development.Themapping of street patterns also shows the urban structural differences betweencities. First, asmentioned earlier, somecities show three layersof ring structurewhileothersonly have two. Houston and London are typical citieswith three layersof urbanspatial structurewhereanurbancore, urbanextensionand suburbandivisionareclearlyidentifiable. The gridiron inner core in Houston and the organic inner core in Londonreflect the difference in the planning and urban development in North American andtraditional Europeancities.On thecontrary, ChengduandAmsterdamshow atwo-layerurban spatial structurewhere theurban core isnot recognisable from thestreet pattern.In addition, the street pattern mapping also, to somedegree, reflects thepolycentricity[16], [17] of the cities. For example, the study area in the Amsterdam region clearlyshows a polycentric urban spatial structure with multiple urban core present. This isprobably due to the fact thestudy areahascovered surrounding cities likeHaleem andZaandam, which the street pattern is able to capture. While cities like Chengdu andHouston appear to bemoremonocentric.The proposed method and the street patterns mapping show promising results andprove their potential in the urban morphological study. A further application in morecitiescan help to study thespatial distribution of street patternsand urban spatial struc-tureat a larger scale. Theproposed study unit, SLA, also performswell in capturing thestreet networks' character. Still, some limitations need to be addressed. First, due totimeconstraints, thestreet patterncategorisationadopted in thisstudy isstill toogeneraland vague for amore refined study. Themanual identification in the training set alsolacks cross-referencing. Second, among the fourteenmetrics, only six weresignificantin the classifier, meaning some information was lost. This either means thesemetricsare less relevant in thedefinition of street pattern or the street pattern categorisation is6ineffective in capturing the character of the street network. Lastly, we would like topoint out the inherent limitation of using streets pattern in urban morphology. Streetsare one of the many urban elements present in the built environment. Hence, streetsalone are insufficient for a holistic urban study and present an opportunity for a studyinvolvingmultipleurban elementssuch asbuildings.7Chengdu AmsterdamNew York SeoulLondon HoustonFig. 2. Themapping of street patterns in six casestudy cities.84 ConclusionThrough theproposed framework, this paper hasexemplified how thequantitativeap-proach could identify street patterns, and in turn, it reveals the urban spatial structurefor cross-comparison between different cities. In short, this article first proposed thatnetwork andmachinelearningmethodshaveshownpromising resultsin studying streetpatterns. Second, this research provides a new perspective for viewing urban spatialstructure and presents opportunities for further research. Their distribution roughlyshowsa ring structurewhich reflects thehistorical core, new urban extension, and sub-urban/rural division of urban spatial structure. In addition, the different street patternspresented in different citieswith different percentagesand their spatial distribution in-dicate the inherent differences and characters in cities. This research provides a newperspective for viewing urban spatial structure and presents opportunities for furtherresearch. However, the limitation of asingleurbanmorphological element, such as thestreet network, to reflect the urban spatial structure needs to be further addressed togeneratemore fruitful results.References1. V. Oliveira and V. Medeiros, "Morpho: Combining morphological measures," Environ.Plan. BPlan. Des., vol. 43, no. 5, pp. 805–825, Sep. 2016, doi: 10.1177/0265813515596529.2. S. M. Wheeler, "Built Landscapesof Metropolitan Regions: An International Typology," J.Am. Plan. Assoc., vol. 81, no. 3, pp. 167–190, Jul. 2015, doi:10.1080/01944363.2015.1081567.3. A. 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Urban spatial structure from a street network perspective:mapping street patterns with random forest classification

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Urban spatial structure from a street network perspective:mapping street patterns with random forest classification

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