Stratified mobility, segregated boundary, and socioeconomic mixing in New York

Abstract

Mobility cross spatial units represents the embodiment of how people manage activities between locations along temporal sequences. Spatiotemporal pattern nevertheless interacts with the socioeconomic characteristics of respected origin (push factors) and destination (pull factors) which widely discussed in spatial interaction literature. Observing this dynamics at higher spatial resolution allows us to entangle multifaceted nature of city, its complexity as a system or network, and the way it shapes movement of people. This study explore the extent interconnected elements of urban system or urban networks, in parallel with the appearance of external shock namely COVID outbreak, may affect estimation of mobility flows. To improve predictive power, Gravity Model is extended to Urban System Model by augmenting the complexities of urban network based on micro-analytical approach (intra-city networks). Our findings reveals better performance of a more complex Urban System Model as to compared with Gravity Model. Here, we leverage stratification in mobility by specifying mobility flows with respect to income status of respected areas. The occurrence of COVID outbreak followed by lockdown measure increases intra-class mobility, indicating the coupling between socioeconomic distance and geographical distance. Flows between areas with similar economic ranges are more predictable than the one of different level. Furthermore, the presence of pull factors is more affluent than push factors in determining mobility regardless the severity of external shock.

Keywords

Mobility flows / Spatial interaction / Urban system / COVID outbreak

References

[1] Albeverio, S., Andrey, D., Giordano, P., & Vancheri, A. (2007). The dynamics of complex urban systems: An interdisciplinary approach. Springer.

[2] Anciaes PR, Metcalfe PJ, Heywood C. Social impacts of road traffic: Perceptions and priorities of local residents. Impact Assessment and Project Appraisal, 2017, 35(2): 172-183

[3] Andersen HT, Møller-Jensen L, Engelstoft S. The end of urbanization? Towards a new urban concept or rethinking urbanization. European Planning Studies, 2011, 19(4): 595-611

[4] Anderson JE. The gravity model. Annual Review of Economics, 2011, 3(1): 133-160

[5] Barbosa H, Barthelemy M, Ghoshal G, James CR, Lenormand M, Louail T, Menezes R, Ramasco JJ, Simini F, Tomasini M. Human mobility: Models and applications. Physics Reports, 2018, 734: 1-74

[6] Barthélemy M, Flammini A. Modeling urban street patterns. Physical Review Letters, 2008, 100(13 138702

[7] Benedictis, L. D. and Taglioni, D. (2011). The gravity model in international trade. In The trade impact of European Union preferential policies, pages 55–89. Springer.

[8] Berli J, Bunel M, Ducruet C. Sea-land interdependence in the global maritime network: The case of Australian port cities. Networks and Spatial Economics, 2018, 18(3): 447-471

[9] Bielik M, König R, Schneider S, Varoudis T. Measuring the impact of street network configuration on the accessibility to people and walking attractors. Networks and Spatial Economics, 2018, 18(3): 657-676

[10] Boeing G. Street network models and measures for every US city, county, urbanized area, census tract, and Zillow-defined neighborhood. Urban Science, 2019, 3(1 28

[11] Boeing G. Urban spatial order: Street network orientation, configuration, and entropy. Applied Network Science, 2019, 4(1): 1-19

[12] Bokányi, E., Juhász, S., Karsai, M., & Lengyel, B. (2021). Universal role of commuting in the reduction of social assortativity in cities. arXiv preprint arXiv:2105.01464.

[13] Bureau, US Census. (2021). 2019 American Community Survey Single-Year estimates. United States Census Bureau. https://www.census.gov/newsroom/press-kits/2020/acs-1year.html

[14] Cottineau C, Finance O, Hatna E, Arcaute E, Batty M. Defining urban clusters to detect agglomeration economies. Environment and Planning B: Urban Analytics and City Science, 2019, 46(9): 1611-1626

[15] Dong X, Morales AJ, Jahani E, Moro E, Lepri B, Bozkaya B, Sarraute C, Bar-Yam Y, Pentland A. Segregated interactions in urban and online space. EPJ Data Science, 2020, 9(1 20

[16] Dueñas, M., Campi, M., & Olmos, L. E. (2021). Changes in mobility and socioeconomic conditions during the covid-19 outbreak. Humanities and Social Sciences Communications, 8(1). https://www.nature.com/articles/s41599-021-00775-0#citeas

[17] Ebrahimpour Z, Wan W, Cervantes O, Luo T, Ullah H. Comparison of main approaches for extracting behavior features from crowd flow analysis. ISPRS International Journal of Geo-Information, 2019, 8(10 440

[18] Fang C, Yu D. Urban agglomeration: An evolving concept of an emerging phenomenon. Landscape and Urban Planning, 2017, 162: 126-136

[19] Faraway, J. J. (2016). Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. Chapman and Hall/CRC.

[20] Gross B, Havlin S. Epidemic spreading and control strategies in spatial modular network. Applied Network Science, 2020, 5(1): 1-14

[21] Hazarie S, Soriano-Paños D, Arenas A, Gómez-Gardeñes J, Ghoshal G. Interplay between population density and mobility in determining the spread of epidemics in cities. Communications Physics, 2021, 4(1): 1-10

[22] Helsley RW, Zenou Y. Social networks and interactions in cities. Journal of Economic Theory, 2014, 150: 426-466

[23] Herrera-Yagüe C, Schneider CM, Couronne T, Smoreda Z, Benito RM, Zufiria PJ, González MC. The anatomy of urban social networks and its implications in the searchability problem. Scientific Reports, 2015, 5(1): 1-13

[24] Huff DL. A probabilistic analysis of shopping center trade areas. Land Economics, 1963, 39(1): 81-90

[25] Jq S. An inverse distance variation for certain social distances [j]. Science, 1941, 93: 89-90

[26] Kang, Y., Gao, S., Liang, Y., Li, M., & Kruse, J. (2020). Multiscale dynamic human mobility flow dataset in the u.s. during the covid-19 epidemic. Scientific Data, 1–13.

[27] Li W, Hu D, Liu Y. An improved measuring method for the information entropy of network topology. Transactions in GIS, 2018, 22(6): 1632-1648

[28] Liu X, Derudder B, Wu K. Measuring polycentric urban development in China: An intercity transportation network perspective. Regional Studies, 2015, 50(8): 1302-1315

[29] Marshall S, Gil J, Kropf K, Tomko M, Figueiredo L. Street network studies: From networks to models and their representations. Networks and Spatial Economics, 2018, 18(3): 735-749

[30] McPhearson T, Haase D, Kabisch N, Gren Å. Advancing understanding of the complex nature of urban systems. Ecological Indicators, 2016

[31] Montanari A. Cross-national co-operation and human mobility: An introduction. International Review of Sociology, 2012, 22(2): 175-190

[32] Nagy AM, Simon V. Survey on traffic prediction in smart cities. Pervasive and Mobile Computing, 2018, 50: 148-163

[33] Neal, Z. P. (2012). The connected city: How networks are shaping the modern metropolis. Routledge.

[34] Nelder JA, Wedderburn RW. Generalized linear models. Journal of the Royal Statistical Society, Series A (General), 1972, 135(3): 370-384

[35] Newman ME. Mixing patterns in networks. Physical Review E, 2003, 67(2 026126

[36] Noulas A, Scellato S, Lambiotte R, Pontil M, Mascolo C. A tale of many cities: Universal patterns in human urban mobility. PloS one, 2012, 7(5 e37027

[37] Org, OpenMobility. (2022). OpenMobilityData - public transit feeds from around the world. OpenMobilityOrg, https://transitfeeds.com/

[38] OSM, OpenStreetMap. (2022). OpenStreetMap, https://www.openstreetmap.org/#map=7/52.154/5.295

[39] Oshan TM. A primer for working with the spatial interaction modeling (spint) module in the python spatial analysis library (pysal). Region, 2016, 3(2): R11-R23

[40] (OTI), NYC Office of Technology and Innovation. (2022). About Building Footprints | NYC open data. NYC Office of Technology and Innovation (OTI), https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh/about

[41] Pappalardo L, Vanhoof M, Gabrielli L, Smoreda Z, Pedreschi D, Giannotti F. An analytical framework to nowcast well-being using mobile phone data. International Journal of Data Science and Analytics, 2016, 2(1): 75-92

[42] Pflieger, G., & Rozenblat, C. (2010). Introduction. urban networks and network theory: the city as the connector of multiple networks. Urban Studies, 47(13), 2723–2735. https://doi.org/10.1177/0042098010377368

[43] Prieto Curiel R, Pappalardo L, Gabrielli L, Bishop SR. Gravity and scaling laws of city to city migration. PloS one, 2018, 13(7 e0199892

[44] Roy JR, Thill J-C. Spatial interaction modelling. Papers in Regional Science, 2003, 83(1): 339-361

[45] Schneider CM, Belik V, Couronné T, Smoreda Z, González MC. Unravelling daily human mobility motifs. Journal of the Royal Society Interface, 2013, 10(84 20130246

[46] Sîrbu A, Andrienko G, Andrienko N, Boldrini C, Conti M, Giannotti F, Guidotti R, Bertoli S, Kim J, Muntean CI, et al.. Human migration: The big data perspective. International Journal of Data Science and Analytics, 2021, 11(4): 341-360

[47] Tomko M, Winter S, Claramunt C. Experiential hierarchies of streets. Computers, Environment and Urban Systems, 2008, 32(1): 41-52

[48] Valgañón P, Useche AF, Soriano-Paños D, Ghoshal G, Gómez-Gardeñes J. Quantifying the heterogeneous impact of lockdown policies on different socioeconomic classes during the first COVID-19 wave in Colombia. Scientific Reports, 2023, 13(1 16481

[49] Voukelatou V, Gabrielli L, Miliou I, Cresci S, Sharma R, Tesconi M, Pappalardo L. Measuring objective and subjective well-being: Dimensions and data sources. International Journal of Data Science and Analytics, 2021, 11(4): 279-309

[50] Wang M, Vermeulen F. Life between buildings from a street view image: What do big data analytics reveal about neighbourhood organisational vitality?. Urban Studies, 2021, 58(15): 3118-3139

[51] Wen T-H, Lai P-C, et al.. Understanding the topological characteristics and flow complexity of urban traffic congestion. Physica A: Statistical Mechanics and Its Applications, 2017, 473: 166-177

[52] Wilson AG. A family of spatial interaction models, and associated developments. Environment and Planning A, 1971, 3(1): 1-32

RIGHTS & PERMISSIONS

The Author(s)