Skip to main content

A Review of Spatial Network Insights and Methods in the Context of Planning: Applications, Challenges, and Opportunities

  • Chapter
  • First Online:
Urban Informatics and Future Cities

Part of the book series: The Urban Book Series ((UBS))

Abstract

With the rise of geospatial big data, new narratives of cities based on spatial networks and flows have replaced the traditional focus on locations. While plenty of research that have empirically analyzed network structures, there lacks a state-of-the-art synthesis of applicable insights and methods of spatial networks in the planning context. In this chapter, we reviewed the theories, concepts, methods, and applications of spatial network analysis in cities and their insights for planners from four areas of concerns: spatial structures, urban infrastructure optimizations, indications of economic wealth, social capital, and residential mobility, and public health control (especially COVID-19). We also outlined four challenges that planners face when taking the planning knowledge from spatial networks to actions: data openness and privacy, linkage to direct policy implications, lack of civic engagement, and the difficulty to visualize and integrate with GIS. Finally, we envisioned how spatial networks can be integrated into a collaborative planning framework.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://flowmap.blue/.

References

  • Albrechts L, Mandelbaum S (2007) The network society: a new context for planning. Routledge

    Google Scholar 

  • Andris C (2016) Integrating social network data into GISystems. Int J Geogr Inf Sci 30(10):2009–2031

    Google Scholar 

  • Andris C (2020) Regions from social networks: what’s next? NARSC Newsl 8(1):7–10

    Google Scholar 

  • Andris C, O’Sullivan D (2019) Spatial network analysis. Handb Reg Sci 1–24

    Google Scholar 

  • Andris C, Liu X, Ferreira J Jr (2018) Challenges for social flows. Comput Environ Urban Syst 70:197–207

    Article  Google Scholar 

  • Andris C, Liu X, Mitchell J, O’Dwyer J, Van Cleve J (2019) Threads across the urban fabric: youth mentorship relationships as neighborhood bridges. J Urban Aff 1–16

    Google Scholar 

  • Ansell C, Bichir R, Zhou S (2016) Who says networks, says oligarchy? Oligarchies as “Rich Club” networks. Connect-Off J Int Netw Soc Netw Anal 35(2):20–32

    Google Scholar 

  • Bailey M, Cao R, Kuchler T, Stroebel J, Wong A (2018) Social connectedness: measurement, determinants, and effects. J Econ Perspect 32(3):259–280

    Article  Google Scholar 

  • Bajardi P, Poletto C, Ramasco JJ, Tizzoni M, Colizza V, Vespignani A (2011) Human mobility networks, travel restrictions, and the global spread of 2009 H1N1 pandemic. PLoS One 6(1):

    Article  Google Scholar 

  • Bao J, He T, Ruan S, Li Y, Zheng Y (2017) Planning bike lanes based on sharing-bikes’ trajectories. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1377–1386

    Google Scholar 

  • Barthélemy M (2011) Spatial networks. Phys Rep 499(1–3):1–101

    Article  MathSciNet  Google Scholar 

  • Bathelt H, Glückler J (2003) Toward a relational economic geography. J Econ Geogr 3(2):117–144

    Article  Google Scholar 

  • Bathelt H, Glückler J (2005) Resources in economic geography: from substantive concepts towards a relational perspective. Environ Plan A 37(9):1545–1563

    Article  Google Scholar 

  • Batty M (2013) The new science of cities. MIT Press

    Google Scholar 

  • Benzell SG, Collis A, Nicolaides C (2020) Rationing social contact during the COVID-19 pandemic: transmission risk and social benefits of US locations. Proc Natl Acad Sci 117(26):14642–14644

    Google Scholar 

  • Bettencourt LMA (2013) The origins of scaling in cities. Science 340(6139):1438–1441

    Article  MathSciNet  MATH  Google Scholar 

  • Blumenstock J, Fratamico L (2013) Social and spatial ethnic segregation: a framework for analyzing segregation with large-scale spatial network data. In: Proceedings of the 4th annual symposium on computing for development, pp 1–10

    Google Scholar 

  • Boeing G (2017) OSMnx: new methods for acquiring, constructing, analyzing, and visualizing complex street networks. Comput Environ Urban Syst 65:126–139

    Article  Google Scholar 

  • Boeing G (2019) Urban spatial order: street network orientation, configuration, and entropy. Appl Netw Sci 4(1):67

    Article  Google Scholar 

  • Bonaccorsi G, Pierri F, Cinelli M, Flori A, Galeazzi A, Porcelli F et al (2020) Economic and social consequences of human mobility restrictions under COVID-19. Proc Natl Acad Sci 117(27):15530–15535

    Article  Google Scholar 

  • Booher DE, Innes JE (2002) Network power in collaborative planning. J Plan Educ Res 21(3):221–236

    Article  Google Scholar 

  • Borgatti SP (2005) Centrality and network flow. Soc Netw 27(1):55–71

    Article  MathSciNet  Google Scholar 

  • Brelsford C, Martin T, Hand J, Bettencourt LMA (2018) Toward cities without slums: topology and the spatial evolution of neighborhoods. Sci Adv 4(8):eaar4644

    Google Scholar 

  • Brenner N, Schmid C (2015) Towards a new epistemology of the urban? City 19(2–3):151–182

    Article  Google Scholar 

  • Caggiani L, Camporeale R, Marinelli M, Ottomanelli M (2019) User satisfaction based model for resource allocation in bike-sharing systems. Transp Policy 80:117–126

    Article  Google Scholar 

  • Cao J, Liu XC, Wang Y, Li Q (2013) Accessibility impacts of China’s high-speed rail network. J Transp Geogr 28:12–21

    Article  Google Scholar 

  • Castells M (1996) The information age, vol 98. Oxford Blackwell Publishers

    Google Scholar 

  • City of Chicago (2020) Transportation network providers—vehicles. https://data.cityofchicago.org/Transportation/Transportation-Network-ProvidersVehicles/bc6b-sq4u

  • Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70(6):66111

    Article  Google Scholar 

  • Crucitti P, Latora V, Porta S (2006) Centrality measures in spatial networks of urban streets. Phys Rev E 73(3):36125

    Article  MATH  Google Scholar 

  • Delmelle EM, Li S, Murray AT (2012) Identifying bus stop redundancy: a GIS-based spatial optimization approach. Comput Environ Urban Syst 36(5):445–455

    Article  Google Scholar 

  • Dempwolf CS, Lyles LW (2012) The uses of social network analysis in planning: a review of the literature. J Plan Literat 27(1):3–21

    Article  Google Scholar 

  • Derrible S (2012) Network centrality of metro systems. PLoS One 7(7):

    Article  Google Scholar 

  • Eagle N, Macy M, Claxton R (2010) Network diversity and economic development. Science 328(5981):1029–1031

    Article  MathSciNet  MATH  Google Scholar 

  • Ersoy O, Hurter C, Paulovich F, Cantareiro G, Telea A (2011) Skeleton-based edge bundling for graph visualization. IEEE Trans Visual Comput Graphics 17(12):2364–2373

    Article  Google Scholar 

  • Fainstein SS (2005) Local networks and capital building. The network society: a new context for planning, pp 222–228

    Google Scholar 

  • Fan C, Cai T, Gai Z, Wu Y (2020) The relationship between the migrant population’s migration network and the risk of COVID-19 transmission in China—Empirical analysis and prediction in prefecture-level cities. Int J Environ Res Pub Health 17(8):2630

    Article  Google Scholar 

  • Fortunato S (2010) Community detection in graphs. Phys Rep 486(3–5):75–174

    Article  MathSciNet  Google Scholar 

  • Friedmann J (1987) Planning in the public domain. From knowledge to action. Princeton University Press, Princeton, New Jersey

    Google Scholar 

  • Galeazzi A, Cinelli M, Bonaccorsi G, Pierri F, Schmidt AL, Scala A, Pammolli F, Quattrociocchi W (2020) Human mobility in response to COVID-19 in France, Italy and UK. ArXiv Preprint http://arxiv.org/abs/2005.06341

  • Gans HJ (1962) The urban villagers. Group and Class in the life of Italian–Americans. Free Press of Glencoe, New York

    Google Scholar 

  • Gao S, Wang Y, Gao Y, Liu Y (2013a) Understanding urban traffic-flow characteristics: a rethinking of betweenness centrality. Environ Plan 40(1):135–153

    Article  Google Scholar 

  • Gao S, Liu Y, Wang Y, Ma X (2013b) Discovering spatial interaction communities from mobile phone data. Trans GIS 17(3):463–481

    Article  Google Scholar 

  • Gao S, Janowicz K, Couclelis H (2017) Extracting urban functional regions from points of interest and human activities on location-based social networks. Trans GIS 21(3):446–467

    Article  Google Scholar 

  • Gao S, Rao J, Liu X, Kang Y, Huang Q, App J (2019) Exploring the effectiveness of geomasking techniques for protecting the geoprivacy of Twitter users. J Spat Inform Sci 19:105–129. https://doi.org/10.5311/JOSIS.2019.19.510

    Article  Google Scholar 

  • Gao S, Rao J, Kang Y, Liang Y, Kruse J (2020) Mapping county-level mobility pattern changes in the United States in response to COVID-19. SIGSPATIAL Spec 12(1):16–26

    Article  Google Scholar 

  • Gatto M, Bertuzzo E, Mari L, Miccoli S, Carraro L, Casagrandi R, Rinaldo A (2020) Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures. Proc Natl Acad Sci 117(19):10484–10491

    Article  Google Scholar 

  • Goetz SJ (2020) COVID-19, networks and regional science. NARSC Newsl 8(1):5–7

    Google Scholar 

  • Graham S, Healey P (1999) Relational concepts of space and place: Issues for planning theory and practice. Eur Plan Stud 7(5):623–646

    Article  Google Scholar 

  • Graif C, Lungeanu A, Yetter AM (2017) Neighborhood isolation in Chicago: violent crime effects on structural isolation and homophily in inter-neighborhood commuting networks. Soc Netw 51:40–59

    Article  Google Scholar 

  • Grantz KH, Meredith HR, Cummings DA, Metcalf CJE, Grenfell BT, Giles JR, Mehta S, Solomon S, Labrique A, Kishore N, Buckee CO (2020) The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology. Nat Commun 11(1):1–8

    Article  Google Scholar 

  • Gruteser M, Grunwald D (2003) Anonymous usage of location-based services through spatial and temporal cloaking. In: Proceedings of the 1st international conference on mobile systems, applications and services, pp 31–42

    Google Scholar 

  • Gu Z, Zhu Y, Zhang Y, Zhou W, Chen Y (2019) Heuristic bike optimization algorithm to improve usage efficiency of the station-free bike sharing system in Shenzhen, China. ISPRS Int J Geo-Inform 8(5):239

    Article  Google Scholar 

  • Guimera R, Mossa S, Turtschi A, Amaral LAN (2005) The world-wide air transportation network: anomalous centrality, community structure, and cities’ global roles. Proc Natl Acad Sci 102(22):7794–7799

    Article  MathSciNet  MATH  Google Scholar 

  • Guo D (2009) Flow mapping and multivariate visualization of large spatial interaction data. IEEE Trans Visual Comput Graphics 15(6):1041–1048

    Article  Google Scholar 

  • Haggett P, Chorley RJ (1969) Network analysis in geography, vol 1. Hodder Education

    Google Scholar 

  • Hajer M, Zonneveld W (2000) Spatial planning in the network society-rethinking the principles of planning in the Netherlands. Eur Plan Stud 8(3):337–355

    Article  Google Scholar 

  • Hausmann R, Hidalgo CA, Bustos S, Coscia M, Simoes A (2014) The atlas of economic complexity: mapping paths to prosperity. MIT Press

    Google Scholar 

  • Hidalgo CA, Hausmann R (2009) The building blocks of economic complexity. Proc Natl Acad Sci 106(26):10570–10575

    Article  Google Scholar 

  • Holtz D, Zhao M, Benzell SG, Cao CY, Rahimian MA, Yang J, Allen JNL, Collis A, Moehring AV, Sowrirajan T, Ghosh D (2020) Interdependence and the cost of uncoordinated responses to COVID-19. Proc Natl Acad Sci 117(33):19837–19843

    Google Scholar 

  • Hou X, Gao S, Li Q, Kang Y, Chen N, Chen K, Rao J, Ellenberg JS, Patz JA (2020) Intra-county modeling of COVID-19 infection with human mobility: assessing spatial heterogeneity with business traffic, age and race. Proc Natl Acad Sci 118(24)

    Google Scholar 

  • Hristova D, Williams MJ, Musolesi M, Panzarasa P, Mascolo C (2016) Measuring urban social diversity using interconnected geo-social networks. In: Proceedings of the 25th international conference on World Wide Web, pp 21–30

    Google Scholar 

  • Huang Q, Wong DWS (2016) Activity patterns, socioeconomic status and urban spatial structure: what can social media data tell us? Int J Geogr Inf Sci 30(9):1873–1898

    Article  Google Scholar 

  • Innes JE (1995) Planning theory’s emerging paradigm: communicative action and interactive practice. J Plan Educ Res 14(3):183–189

    Article  Google Scholar 

  • Innes JE, Booher DE (1999) Consensus building and complex adaptive systems: a framework for evaluating collaborative planning. J Am Plan Assoc 65(4):412–423

    Article  Google Scholar 

  • Innes JE, Booher DE (2018) Planning with complexity: an introduction to collaborative rationality for public policy. Routledge

    Google Scholar 

  • Kang Y, Gao S, Liang Y, Li M, Rao J, Kruse J (2020) Multiscale dynamic human mobility flow dataset in the US during the COVID-19 epidemic. Scientific Data 7(1):1–13

    Google Scholar 

  • Kempinska K, Longley P, Shawe-Taylor J (2018) Interactional regions in cities: making sense of flows across networked systems. Int J Geogr Inf Sci 32(7):1348–1367

    Article  Google Scholar 

  • Kim H (2020) Some thoughts concerning network analysis approach in regional science. NARSC Newsl 8(1):11–12

    Google Scholar 

  • Kitchin R (2016) The ethics of smart cities and urban science. Philos Trans Royal Soc A: Math Phys Eng Sci 374(2083):20160115

    Article  Google Scholar 

  • Knoke D, Yang S (2019) Social network analysis, vol 154. Sage

    Google Scholar 

  • Kwan M, Casas I, Schmitz B (2004) Protection of geoprivacy and accuracy of spatial information: How effective are geographical masks? Cartographica: Int J Geogr Inform Geovisualization 39(2):15–28

    Google Scholar 

  • Lai S, Bogoch II, Ruktanonchai NW, Watts A, Lu X, Yang W, Yu H, Khan K, Tatem AJ (2020) Assessing spread risk of Wuhan novel coronavirus within and beyond China. Janurary–April 2020: a travel network-based modeling study. MedRxiv

    Google Scholar 

  • Lai S, Ruktanonchai NW, Zhou L, Prosper O, Luo W, Floyd JR, Wesolowski A, Santillana M, Zhang C, Du X, Yu H (2020) Effect of non-pharmaceutical interventions to contain COVID-19 in China. Nat 685(7825):410-413

    Google Scholar 

  • Laniado D, Volkovich Y, Scellato S, Mascolo C, Kaltenbrunner A (2018) The impact of geographic distance on online social interactions. Inform Syst Front 20(6):1203–1218

    Article  Google Scholar 

  • Li B, Gao S, Liang Y, Kang Y, Prestby T, Gao Y, Xiao R (2020) Estimation of regional economic development indicator from transportation network analytics. Sci Rep 10(1). https://doi.org/10.1038/s41598-020-59505-2

  • Liu Y, Sui Z, Kang C, Gao Y (2014a) Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data. PLoS One 9(1):e86026

    Article  Google Scholar 

  • Liu X, Gong L, Gong Y, Liu Y (2015) Revealing travel patterns and city structure with taxi trip data. J Transp Geogr 43:78–90

    Article  Google Scholar 

  • Liu X, Hollister R, Andris C (2018) Wealthy hubs and poor chains: constellations in the US urban migration system. In: Agent-based models and complexity science in the age of geospatial big data. Springer, pp 73–86

    Google Scholar 

  • Liu X, Chen H, Andris C (2018) trajGANs: using generative adversarial networks for geo-privacy protection of trajectory data (Vision paper). Location Privacy and Security Workshop, pp 1–7

    Google Scholar 

  • Liu S, Wan Y, Ha H-K, Yoshida Y, Zhang A (2019) Impact of high-speed rail network development on airport traffic and traffic distribution: evidence from China and Japan. Transp Res Part A: Policy Pract 127:115–135

    Google Scholar 

  • McKenzie G, Janowicz K, Gao S, Yang J-A, Hu Y (2015) POI pulse: a multigranular, semantic signature–based information observatory for the interactive visualization of big geosocial data. Cartographica: Int J Geogr Inform Geovisualization 50(2):71–85

    Google Scholar 

  • Mesbah M, Thompson R, Moridpour S (2012) Bilevel optimization approach to design of network of bike lanes. Transp Res Rec 2284(1):21–28

    Article  Google Scholar 

  • Metaxa-Kakavouli D, Maas P, Aldrich DP (2018) How social ties influence hurricane evacuation behavior. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), pp 1–16

    Google Scholar 

  • Montjoye D, Alexandre Y, Hidalgo C, Verleysen M, Blondel V (2013) Unique in the crowd: the privacy bounds of human mobility. Sci Rep 3:1376

    Article  Google Scholar 

  • Neal Z (2011) Differentiating centrality and power in the world city network. Urban Stud 48(13):2733–2748

    Article  Google Scholar 

  • Neal Z (2012) The connected city: How networks are shaping the modern metropolis. Routledge

    Google Scholar 

  • Netto VM, Soares MP, Paschoalino R (2015) Segregated networks in the city. Int J Urban Reg Res 39(6):1084–1102

    Article  Google Scholar 

  • O’Kelly ME (1998) A geographer’s analysis of hub-and-spoke networks. J Transp Geogr 6(3):171–186

    Article  Google Scholar 

  • Park J, Wood IB, Jing E, Nematzadeh A, Ghosh S, Conover MD, Ahn YY (2019) Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters. Nat Commun 10(1):1–10

    Article  Google Scholar 

  • Pei T, Sobolevsky S, Ratti C, Shaw S-LL, Li T, Zhou C (2014) A new insight into land use classification based on aggregated mobile phone data. Int J Geogr Inf Sci 28(9):1988–2007. https://doi.org/10.1080/13658816.2014.913794

    Article  Google Scholar 

  • Peng Z, Wang R, Liu L, Wu H (2020) Exploring urban spatial features of COVID-19 transmission in Wuhan based on social media data. ISPRS Int J GeoInform 9(6):402

    Article  Google Scholar 

  • Pepe E, Bajardi P, Gauvin L, Privitera F, Lake B, Cattuto C, Tizzoni M (2020) COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Sci Data 7(1):1–7

    Article  Google Scholar 

  • Phillips NE, Levy BL, Sampson RJ, Small ML, Wang RQ (2019) The social integration of American cities: network measures of connectedness based on everyday mobility across neighborhoods. Sociol Methods Res. https://doi.org/10.1177/0049124119852386

  • Pons P, Latapy M (2005) Computing communities in large networks using random walks. In: International symposium on computer and information sciences, pp 284–293

    Google Scholar 

  • Prestby T, App J, Kang Y, Gao S (2020) Understanding neighborhood isolation through spatial interaction network analysis using location big data. Environ Plan A: Econ Space. https://doi.org/10.1177/0308518X19891911

  • Pullano G, Valdano E, Scarpa N, Rubrichi S, Colizza V (2020) Population mobility reductions during COVID-19 epidemic in France under lockdown. MedRxiv

    Google Scholar 

  • Radil SM, Walther OJ (2018) Social networks and geography: a review of the literature and its implications. ArXiv Preprint https://arxiv.org/abs/1805.04510

  • Rae A (2009) From spatial interaction data to spatial interaction information? Geovisualisation and spatial structures of migration from the 2001 UK census. Comput Environ Urban Syst 33(3):161–178

    Article  Google Scholar 

  • Rao J, Gao S, Kang Y, Huang Q (2020) LSTM-TrajGAN: a deep learning approach to trajectory privacy protection. ArXiv Preprint https://arxiv.org/pdf/2006.10521

  • Ratti C, Sobolevsky S, Calabrese F, Andris C, Reades J, Martino M, Claxton R, Strogatz SH (2010) Redrawing the map of Great Britain from a network of human interactions. PLoS One 5(12):e14248

    Article  Google Scholar 

  • Reichardt J, Bornholdt S (2006) Statistical mechanics of community detection. Phys Rev E 74(1):16110

    Article  MathSciNet  Google Scholar 

  • Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci 105(4):1118–1123

    Article  Google Scholar 

  • Rosvall M, Bergstrom CT (2010) Mapping change in large networks. PLoS One 5(1):e8694

    Article  Google Scholar 

  • Shelton T, Poorthuis A (2019) The nature of neighborhoods: using big data to rethink the geographies of Atlanta’s neighborhood planning unit system. Ann Am Assoc Geogr 109(5):1341–1361

    Google Scholar 

  • Shimamoto H, Murayama N, Fujiwara A, Zhang J (2010) Evaluation of an existing bus network using a transit network optimisation model: a case study of the Hiroshima City Bus network. Transportation 37(5):801–823

    Article  Google Scholar 

  • Siła-Nowicka K, Vandrol J, Oshan T, Long JA, Demšar U, Fotheringham AS (2016) Analysis of human mobility patterns from GPS trajectories and contextual information. Int J Geogr Inf Sci 30(5):881–906

    Article  Google Scholar 

  • Sobolevsky S, Szell M, Campari R, Couronné T, Smoreda Z, Ratti C (2013) Delineating geographical regions with networks of human interactions in an extensive set of countries. PLoS One 8(12):e81707

    Article  Google Scholar 

  • Steiger E, De Albuquerque JP, Zipf A (2015) An advanced systematic literature review on spatiotemporal analyses of t witter data. Trans GIS 19(6):809–834

    Article  Google Scholar 

  • Strano E, Viana MP, Sorichetta A, Tatem AJ (2018) Mapping road network communities for guiding disease surveillance and control strategies. Sci Rep 8(1):1–9

    Google Scholar 

  • Taylor PJ, Derudder B (2004) World city network: a global urban analysis. Routledge

    Google Scholar 

  • Thomas LJ, Huang P, Yin F, Luo XI, Almquist ZW, Hipp JR, Butts CT (2020) Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity. Proc Natl Acad Sci. 117(39)24180–24187

    Google Scholar 

  • Van Eijk G (2010) Unequal networks: spatial segregation, relationships and inequality in the city, vol 32. Gwen van Eijk

    Google Scholar 

  • Viry G (2012) Residential mobility and the spatial dispersion of personal networks: effects on social support. Soc Netw 34(1):59–72

    Article  MathSciNet  Google Scholar 

  • Von Landesberger T, Brodkorb F, Roskosch P, Andrienko N, Andrienko G, Kerren A (2015) Mobility graphs: visual analysis of mass mobility dynamics via spatio-temporal graphs and clustering. IEEE Trans Visual Comput Graphics 22(1):11–20

    Article  Google Scholar 

  • Wang Y (2019) Deck. gl: Large-scale web-based visual analytics made easy. ArXiv Preprint http://arxiv.org/abs/1910.08865

  • Wang J, Mo H, Wang F, Jin F (2011) Exploring the network structure and nodal centrality of China’s air transport network: a complex network approach. J Transp Geogr 19(4):712–721

    Article  Google Scholar 

  • Wang Y, Kang C, Bettencourt LMA, Liu Y, Andris C (2015) Linked activity spaces: embedding social networks in urban space. In: Computational approaches for urban environments. Springer, pp 313–336

    Google Scholar 

  • Wang S, Du Y, Jia C, Bian M, Fei T (2018) Integrating algebraic multigrid method in spatial aggregation of massive trajectory data. Int J Geogr Inf Sci 32(12):2477–2496

    Article  Google Scholar 

  • Warren MS, Skillman SW (2020) Mobility changes in response to COVID-19. ArXiv Preprint https://arxiv.org/pdf/2003.14228

  • Wei Y, Song W, Xiu C, Zhao Z (2018) The rich-club phenomenon of China’s population flow network during the country’s spring festival. Appl Geogr 96:77–85

    Article  Google Scholar 

  • Woodruff A (2013). Neighborhoods as seen by the people. https://bostonography.com/2013/neighborhoods-as-seen-by-the-people/

  • Yang P, Yamagata Y (2020) Urban systems design: shaping smart cities by integrating urban design and systems science. In: Urban systems design. Elsevier, pp 1–22

    Google Scholar 

  • Yang Z, Algesheimer R, Tessone CJ (2016) A comparative analysis of community detection algorithms on artificial networks. Sci Rep 6:30750

    Article  Google Scholar 

  • Yang J, Han Y, Wang Y, Jiang B, Lv Z, Song H (2020) Optimization of real-time traffic network assignment based on IoT data using DBN and clustering model in smart city. Fut Gen Comput Syst 108:976–986

    Article  Google Scholar 

  • Yao X, Wu L, Zhu D, Gao Y, Liu Y (2019) Visualizing spatial interaction characteristics with direction-based pattern maps. J Visual 22(3):555–569

    Article  Google Scholar 

  • Yuan NJ, Zheng Y, Xie X, Wang Y, Zheng K, Xiong H (2014) Discovering urban functional zones using latent activity trajectories. IEEE Trans Knowl Data Eng 27(3):712–725

    Article  Google Scholar 

  • Zhong C, Arisona SM, Huang X, Batty M, Schmitt G (2014) Detecting the dynamics of urban structure through spatial network analysis. Int J Geogr Inf Sci 28(11):2178–2199

    Article  Google Scholar 

  • Zhu X, Guo D (2014) Mapping large spatial flow data with hierarchical clustering. Trans GIS 18(3):421–435

    Article  Google Scholar 

  • Zhu D, Wang N, Wu L, Liu Y (2017) Street as a big geo-data assembly and analysis unit in urban studies: a case study using Beijing taxi data. Appl Geogr 86:152–164

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiaofan Liang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Liang, X., Kang, Y. (2021). A Review of Spatial Network Insights and Methods in the Context of Planning: Applications, Challenges, and Opportunities. In: Geertman, S.C.M., Pettit, C., Goodspeed, R., Staffans, A. (eds) Urban Informatics and Future Cities. The Urban Book Series. Springer, Cham. https://doi.org/10.1007/978-3-030-76059-5_5

Download citation

Publish with us

Policies and ethics