Cities as complex systems—Collection overview

Rybski, Diego; González, Marta C.

doi:10.1371/journal.pone.0262964

Cited by 18 publications

(8 citation statements)

References 25 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, cities have become a topic of considerable scientific interest [1][2][3] . In the last decade, the development of technologies applied to inform the activities of humans has made available an unprecedented amount of data associated with the digital trace of humans in cities 4,5 . For instance: the use of credit card transactions 6 , the use of digital devices to access transportation services, like taxis 7 , buses and subway 8 , bicycle 9 , and telecommunications networks like cell phones [10][11][12][13][14][15][16] and GPS devices 17,18 .…”

Section: Temporal Visitation Patterns Of Points Of Interest In Cities...mentioning

confidence: 99%

Temporal visitation patterns of points of interest in cities on a planetary scale: a network science and machine learning approach

Betancourt

Riascos

Mateos

2023

Sci Rep

View full text Add to dashboard Cite

We aim to study the temporal patterns of activity in points of interest of cities around the world. In order to do so, we use the data provided by the online location-based social network Foursquare, where users make check-ins that indicate points of interest in the city. The data set comprises more than 90 million check-ins in 632 cities of 87 countries in 5 continents. We analyzed more than 11 million points of interest including all sorts of places: airports, restaurants, parks, hospitals, and many others. With this information, we obtained spatial and temporal patterns of activities for each city. We quantify similarities and differences of these patterns for all the cities involved and construct a network connecting pairs of cities. The links of this network indicate the similarity of temporal visitation patterns of points of interest between cities and is quantified with the Kullback-Leibler divergence between two distributions. Then, we obtained the community structure of this network and the geographic distribution of these communities worldwide. For comparison, we also use a Machine Learning algorithm—unsupervised agglomerative clustering—to obtain clusters or communities of cities with similar patterns. The main result is that both approaches give the same classification of five communities belonging to five different continents worldwide. This suggests that temporal patterns of activity can be universal, with some geographical, historical, and cultural variations, on a planetary scale.

show abstract

Section: Temporal Visitation Patterns Of Points Of Interest In Cities...mentioning

confidence: 99%

Temporal visitation patterns of points of interest in cities on a planetary scale: a network science and machine learning approach

Betancourt

Riascos

Mateos

2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Although there is not a single accepted definition of complex systems, Maguire (2011), building on Cilliers (1998), suggests that there is considerable consensus about their key features (see Table 1). To illustrate these features, it is useful to consider a common example of a complex system, such as a city (Rybski and Gonz alez, 2022). Cities consist of large numbers of diverse elements, such as people, infrastructure and technology, which continuously interact with and influence each other.…”

Section: Complexity Science and Complexity Theorymentioning

confidence: 99%

“…The dynamics of these interactions are also non-linear in the sense that the effects of individual actions may be diminished or, on the contrary, amplified. Thus, doubling a city's size normally increases its economic output not by 100%, but by 120% (Rybski and Gonz alez, 2022). Finally, cities are in a constant state of evolution, which is nevertheless often constrained by their past.…”

Section: Complexity Science and Complexity Theorymentioning

confidence: 99%

Rethinking organizational performance management: a complexity theory perspective

Pavlov

Micheli

2022

IJOPM

View full text Add to dashboard Cite

PurposeTraditional approaches to organizational performance management that emphasize objectivity, control and predictability are rapidly losing relevance in an environment characterized by increasing levels of complexity and dynamism. This paper draws on complexity theory to suggest a new paradigm for managing performance in organizations.Design/methodology/approachThe paper draws on the common features of complex systems and the corresponding concept of emergence to revisit key themes in organizational performance management and propose a set of implications for research and practice.FindingsUnderstanding organizations as complex systems and performance as an emergent property of such systems leads to a set of new research questions, the adoption of alternative methods and the formulation of novel propositions. It also has various implications for both academic research and managerial practice, from moving away from the traditional notion of organizational alignment to adopting a more explicit stakeholder-based view in the design and use of measurement systems.Originality/valueThe paper highlights the great potential of complexity theory for addressing contemporary issues in the field of organizational performance management and charting the landscape for its future development.

show abstract

“…The paper by Neumann and Taruttis (in this Special Issue) applies a spatial autoregressive model with autoregressive disturbances (SARAR) to explore hedonic pricing in urban neighborhoods 2 . Spatial dependence of model variables and residuals (represented by the range of spatial econometric models) provide more insight into determinants of particular spatial heterogeneity, such as scaling laws of cities (Rybski and Gonzalez 2021 ; Arcaute and Ramasco 2021 ). A further pertinent econometric tool to process small-scale spatial data is the Geographically Weighted Regression (GWR), both in its basic and multiscale implementation (cf.…”

Section: The Different Dimensions Of the Use Of Small-scale Spatial Datamentioning

confidence: 99%

The potential of small-scale spatial data in regional science

Bergs¹,

Budde

2022

Rev Reg Res

View full text Add to dashboard Cite

Cities as complex systems—Collection overview

Cited by 18 publications

References 25 publications

Temporal visitation patterns of points of interest in cities on a planetary scale: a network science and machine learning approach

Temporal visitation patterns of points of interest in cities on a planetary scale: a network science and machine learning approach

Rethinking organizational performance management: a complexity theory perspective

The potential of small-scale spatial data in regional science

Contact Info

Product

Resources

About