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The settlements in pastoral areas have the spatial distribution characteristics of "broad scattering and small agglomeration". The extension of range makes their physical information attributes change into a spatial awareness with psychological information attributes. In this paper, the psychological cognitive range in herdsmen's spatial awareness was taken as the basis for establishing a spatial network and the social network analysis method was used to quantitatively study the spatial network relationship of settlements and the layout of public service facilities in pastoral areas of Inner Mongolia. The results show that the construction of control equilibrium point of interaction between spatial characteristics of settlements and public service facilities in pastoral areas can be rebuilt by using 10 km psychological cognitive range information; the zoning characteristics of spatial settlement network constructed on the basis of Pajek social network and ArcGIS spatial analysis are obvious; It is of great theoretical and practical significance for pastoral areas and low population density areas to take the index of network node degree as the basis of constructing dynamic layer layouts of public service facilities network and to establish "spatial Network + dynamic layout" layer model of public service facilities layout according to the characteristics of spatial network zoning. [ABSTRACT FROM AUTHOR]

Complex network theory is a multidisciplinary research direction of complexity science which has experienced a rapid surge of interest over the last two decades. Its applications in land-based urban traffic network studies have been fruitful, but have suffered from the lack of a systematic cognitive and integration framework. This paper reviews complex network theory related knowledge and discusses its applications in urban traffic network studies in several directions. This includes network representation methods, topological and geographical related studies, network communities mining, network robustness and vulnerability, big-data-based research, network optimization, co-evolution research and multilayer network theory related studies. Finally, new research directions are pointed out. With these efforts, this physics-based concept will be more easily and widely accepted by urban traffic network planners, designers, and other related scholars. [ABSTRACT FROM AUTHOR]

Community detection is an important topic in spatial network researches. It can discover interesting spatial patterns, which help understand the spatial structures hidden in the networks. However, community detection in spatial networks is more difficult than traditional networks. Since it has to consider some other spatial correlations, e.g., spatial contiguity, geographic distance. This paper proposes a new spatial community detection algorithm-geographial weighted central node distance based Louvain method (GND-Louvain). It uses a geographical and network dual-constrain to measure the distance and to calculate the distances decay effects between spatial nodes and meta-communities. It also extends the famous fast unfolding community detection algorithm-Louvain, by using the distance-modularity. In addition, a merge order is defined during the GND-Louvain's optimization process to get high quality and stable community results. Comparative experiments are designed on five different community detection methods based on five different spatial constraints, which are ① no distance constraint, ② spatial contiguity edge constraint, ③ geometry median center distance, ④ degree center distance, and ⑤ degree weighted geometry median center distance. The experimental data comes from the Chinese railway lines. And results prove that the newly defined GND-Louvain algorithm can produce more accurate spatial communities than others. And the merge orders also ensure the high quality and stable results. [ABSTRACT FROM AUTHOR]

Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite image classification, relying on both supervised or unsupervised machine learning approaches, these models either require multiple input data to function or need further development with regards to precision. In this paper, we introduce a novel method for identifying and informal settlements using street intersection data. With such minimal input data, we attempt to provide planners and policy-makers with a pragmatic tool that can aid in identifying informal zones in cities. The algorithm of the model is based on spatial statistics and Artificial Neural Networks (ANN). The proposed model relies on defining informal settlements based on two ubiquitous characteristics of informal settlements. We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present. These cities are Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. The predictSLUMS model shows high validity and accuracy for identifying and predicting informality within the same city the model was trained on or in different ones of a similar context. To enable this model to be used as a pragmatic tool for future prediction or further research concerning informal settlements and slums, we have made the python code for the pre-trained ANN models of the five studied cities. • A modelling framework is developed for predicting informal settlements. • The model only requires street intersection data as input, making it broadly applicable. • The model is applied to five cities; Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. • By training the model using data from one city, the model can predict informal settlements in another city. • Using ground truth data of planning status for validation, the model achieves accuracy of between 82.1 and 97.6%. [ABSTRACT FROM AUTHOR]

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Animals use a variety of proximate cues to assess habitat quality when resources vary spatiotemporally. Two nonmutually exclusive strategies to assess habitat quality involve either direct assessment of landscape features or observation of social cues from conspecifics as a form of information transfer about forage resources. The conspecific attraction hypothesis proposes that individual space use is dependent on the distribution of conspecifics rather than the location of resource patches, whereas the resource dispersion hypothesis proposes that individual space use and social association are driven by the abundance and distribution of resources. We tested the conspecific attraction and the resource dispersion hypotheses as two nonmutually exclusive hypotheses explaining social association and of adult female caribou (Rangifer tarandus). We used location data from GPS collars to estimate interannual site fidelity and networks representing home range overlap and social associations among individual caribou. We found that home range overlap and social associations were correlated with resource distribution in summer and conspecific attraction in winter. In summer, when resources were distributed relatively homogeneously, interannual site fidelity was high and home range overlap and social associations were low. Conversely, in winter when resources were distributed relatively heterogeneously, interannual site fidelity was low and home range overlap and social associations were high. As access to resources changes across seasons, caribou appear to alter social behavior and space use. In summer, caribou may use cues associated with the distribution of forage, and in winter caribou may use cues from conspecifics to access forage. Our results have broad implications for our understanding of caribou socioecology, suggesting that caribou use season‐specific strategies to locate forage. Caribou populations continue to decline globally, and our finding that conspecific attraction is likely related to access to forage suggests that further fragmentation of caribou habitat could limit social association among caribou, particularly in winter when access to resources may be limited. [ABSTRACT FROM AUTHOR]

Objective: We investigate surname affinities among areas of modern‐day China, by constructing a spatial network, and making community detection. It reports a geographical genealogy of the Chinese population that is result of population origins, historical migrations, and societal evolutions. Materials and methods: We acquire data from the census records supplied by China's National Citizen Identity Information System, including the surname and regional information of 1.28 billion registered Chinese citizens. We propose a multilayer minimum spanning tree (MMST) to construct a spatial network based on the matrix of isonymic distances, which is often used to characterize the dissimilarity of surname structure among areas. We use the fast unfolding algorithm to detect network communities. Results: We obtain a 10‐layer MMST network of 362 prefecture nodes and 3,610 edges derived from the matrix of the Euclidean distances among these areas. These prefectures are divided into eight groups in the spatial network via community detection. We measure the partition by comparing the inter‐distances and intra‐distances of the communities and obtain meaningful regional ethnicity classification. Discussion: The visualization of the resulting communities on the map indicates that the prefectures in the same community are usually geographically adjacent. The formation of this partition is influenced by geographical factors, historic migrations, trade and economic factors, as well as isolation of culture and language. The MMST algorithm proves to be effective in geo‐genealogy and ethnicity classification for it retains essential information about surname affinity and highlights the geographical consanguinity of the population. [ABSTRACT FROM AUTHOR]

A city is a complex system that never sleeps; it constantly changes, and its internal mobility (people, vehicles, goods, information, etc.) continues to accelerate and intensify. These changes and mobility vary in terms of the attributes of the city, such as space, time and cultural affiliation, which characterise to some extent how the city functions. Traditional urban studies have successfully modelled the ‘low-frequency city’ and have provided solutions such as urban planning and highway design for long-term urban development. Nevertheless, the existing urban studies and theories are insufficient to model the dynamics of a city’s intense mobility and rapid changes, so they cannot tackle short-term urban problems such as traffic congestion, real-time transport scheduling and resource management. The advent of information and communication technology and big data presents opportunities to model cities with unprecedented resolution. Since 2018, a paradigm shift from modelling the ‘low-frequency city’ to the so-called ‘high-frequency city’ has been introduced, but hardly any research investigated methods to estimate a city’s frequency. This work aims to propose a framework for the identification and analysis of indicators to model and better understand the concept of a high-frequency city in a systematic manner. The methodology for this work was based on a content analysis-based review, taking into account specific criteria to ensure the selection of indicator sets that are consistent with the concept of the frequency of cities. Twenty-two indicators in five groups were selected as indicators for a high-frequency city, and a framework was proposed to assess frequency at both the intra-city and inter-city levels. This work would serve as a pilot study to further illuminate the ways that urban policy and operations can be adjusted to improve the quality of city life in the context of a smart city.

Human-induced land modification is a significant contributor to carbon emissions. To facilitate the collective reduction of emissions within city clusters and promote eco-friendly urban development, this paper developed a research framework for analyzing carbon emissions. An empirical study was conducted within the Beijing-Tianjin-Hebei (BTH) region of China, focusing on the spatio-temporal patterns of carbon emissions and network features. The results showed that: (1) Carbon emissions among cities in the BTH region displayed significant variation from 2005 to 2020, with an increasing gap in carbon deficit. (2) Inter-city carbon emission correlation showed a declining trend, accompanied by pronounced spatial spillover effects in the region. (3) The network exhibited a bicentric pattern, centered around BJ and TJ, and the network correlations were more likely to occur between nearby developed cities, with poor regional synergistic emission reduction capacity. The implications of this study are valuable for promoting the establishment of an economic system that fosters green, low-carbon, and circular development. Furthermore, the findings contribute to the effective pursuit of carbon reduction and mitigation of climate change in China. • Distinction in carbon deficit between cities was gradually growing. • Spatial correlation network of carbon emissions had serious spillover effects. • Regional carbon emission correlations broke through geographical proximity. • The network presented a dual-center distribution pattern. • Carbon emission linkages were more likely to occur between nearby developed regions. [ABSTRACT FROM AUTHOR]

Based on network analysis and exponential random graph analysis methods (ERGM), the spatial network pattern of gas consumption and its evolution mechanism are investigated using Chinese inter-provincial panel data from 2008 to 2021. This paper shows that the spatial association strength level of natural gas consumption shows an enhanced spatiotemporal evolution. Network density is enhanced. Hierarchy decreases in a stepped way, the network efficiency increases in a fluctuating way, the redundant relation decreases, and the number of its bidirectional spillover relation increases year by year. The eastern seaboard is an economically developed region which is in the "net benefit" position. The provinces of the Tianjin, Hebei, and Shandong are the "two-way spillover" segments. Giving full play to the advantage of the balance between local markets and foreign markets gas consumption. Fujian, Chongqing, Inner Mongolia, Shanxi and Hubei are in the "broker" segment, which tends to concentrate on natural gas consumption outside the segment by their resource endowments. The block of "net spillover" involves the risk of carbon transfer and it should be more responsible for energy saving and carbon reduction; The convergence of per capita economic development, the size of population, industrial structure and urbanization rate leads to selective preferences for inter-provincial natural gas consumption. The large population sizes of provinces have "economies of scale" and "local market effects", which prefer to deploy natural gas. Carbon emission linkage network is the most important for the formation of spatial network patterns of natural gas consumption, followed by the influence of economic linkage networks and land proximity. Less stringent environmental regulations and an open regional environment are conducive to the formation of a network of spatial correlations in natural gas consumption. Accordingly, new ideas and policy recommendations for inter-provincial emission reduction are finally proposed. • Natural gas consumption spatial correlation network correlation has increased and has a star-shaped structure. • Structural barriers in the spatially linked network of natural gas consumption are gradually being broken down. • The eastern region is at risk of carbon transfer and should take on more energy-saving and emission reduction tasks. • Economic, demographic and industrial convergence leads to selective preferences in natural gas consumption. [ABSTRACT FROM AUTHOR]

This paper focuses on modeling a disorder ensemble of quantum dots (QDs) as a special kind of Random Geometric Graphs (RGG) with weighted links. We compute any link weight as the overlap integral (or electron probability amplitude) between the QDs (=nodes) involved. This naturally leads to a weighted adjacency matrix, a Laplacian matrix, and a time evolution operator that have meaning in Quantum Mechanics. The model prohibits the existence of long-range links (shortcuts) between distant nodes because the electron cannot tunnel between two QDs that are too far away in the array. The spatial network generated by the proposed model captures inner properties of the QD system, which cannot be deduced from the simple interactions of their isolated components. It predicts the system quantum state, its time evolution, and the emergence of quantum transport when the network becomes connected.

The chordless cycle sizes of spatially embedded networks are demonstrated to follow an exponential growth law similar to random graphs if the number of nodes Nx is below a critical value N*. For covalent polymer networks, increasing the network size, as measured by the number of cross-link nodes, beyond N* results in a crossover to a new regime in which the characteristic size of the chordless cycles h* no longer increases. From this result, the onset and intensity of finite-size effects can be predicted from measurement of h* in large networks. Although such information is largely inaccessible with experiments, the agreement of simulation results from molecular dynamics, Metropolis Monte Carlo, and kinetic Monte Carlo suggests the crossover is a fundamental physical feature which is insensitive to the details of the network generation. These results show random graphs as a promising model to capture structural differences in confined physical networks. [ABSTRACT FROM AUTHOR]