Migration in Libya: A spatial network analysis

Predicting how changes to the urban environment layout will affect the spatial distribution of pedestrian flows is important for environmental, social and economic sustainability. We present longitudinal evaluation of a model of the effect of urban environmental layout change in a city centre (Cardiff 2007–2010), on pedestrian flows. Our model can be classed as regression based direct demand using Multiple Hybrid Spatial Design Network Analysis (MH-sDNA) assignment, which bridges the gap between direct demand models, facility-based activity estimation and spatial network analysis (which can also be conceived as a pedestrian route assignment based direct demand model). Multiple theoretical flows are computed based on retail floor area: everywhere to shops, shop to shop, railway stations to shops and parking to shops. Route assignment, in contrast to the usual approach of shortest path only, is based on a hybrid of shortest path and least directional change (most direct) with a degree of randomization. The calibration process determines a suitable balance of theoretical flows to best match observed pedestrian flows, using generalized cross-validation to prevent overfit. Validation shows that the model successfully predicts the effect of layout change on flows of up to approx. 8000 pedestrians per hour based on counts spanning a 1 km2 city centre, calibrated on 2007 data and validated to 2010 and 2011. This is the first time, to our knowledge, that a pedestrian flow model with assignment has been evaluated for its ability to forecast the effect of urban layout changes over time.

Performance evaluation method for network monitoring based on separable temporal exponential random graph models with application to the study of autocorrelation effects

PurposeThe purpose of this paper is to describe how to measure workplace configuration, show its application in, and the results of, a field experiment aimed at improving collaborative knowledge work and identify and discuss larger problems involved with research on workplace configuration.Design/methodology/approachThe paper reviews recent thought on density, proximity and problems with evaluating configuration. It then describes a method of spatial network analysis used in a field experiment involving the reconfiguration of a workplace. This is followed with a discussion of recent research on knowledge work from economic geography.FindingsIt was found that, instead of increasing it, the reconfigured workplace decreased collaborative activity. The spatial network analysis shows how this occurred.Research limitations/implicationsThis method of spatial network analysis, when used carefully, is a robust technique for analyzing and comparing spatial configuration. Further research needs to address the links between spatial proximity and information and communications technologies as well as the relation of types of knowledge bases and associated forms of proximity that can stimulate collaboration.Practical implicationsWhile spatial network analysis methods are not do‐it‐yourself tools, corporate real estate managers should employ them, especially in larger‐scale projects, before committing to final workplace designs. They can also use them to identify and map best spatial patterns (like best practices) to identify strategic spatial patterns.Originality/valueThis appears to be the first rigorous application of spatial network methods in a field experiment involving a real workplace. The paper shows the method can clearly extract and discriminate spatial network patterns underlying configurations and relate them both quantitatively and graphically to employee evaluations of collaborative performance. It introduces concepts of comparative knowledge bases that need to be understood in determining the types of collaboration needed in a workplace.

Since entering the 21st century, many developing countries around the world have begun to enter a stage of rapid urbanization; large-scale “rural-urban” population migration has become a typical social phenomenon in these countries. Against this backdrop, this paper aims to elucidate the spatial migration characteristics of rural populations and to discuss future rural–urban development strategies. For this purpose, this paper takes China as a case and employs methods such as spatial autocorrelation analysis, hotspot analysis, and network analysis to construct an intercity migration network of rural migrants and analyze its spatial characteristics and internal structure. The results indicate that the migration pattern of the rural population exhibits notable spatial clustering features. Cities in the eastern and central regions are, respectively, hotspots for the inflow and outflow of rural populations, with internal migration dominating in western cities and relatively inactive rural population movements in northeastern cities. Municipalities directly under the central government, sub-provincial cities, and provincial capitals show a significant tendency to attract rural populations, while prefecture-level and county-level cities mainly radiate rural populations outward. Cities nationwide form seven major clusters in the migration network, and these clusters exhibit distinct structural characteristics. Rural population migration is influenced by various factors. In the future, considerations should focus on the county as the primary unit, attracting rural populations for local employment, and promoting rural revitalization and agriculture development. The findings of this paper are of reference significance not only to China but also to many developing countries with similar national conditions in the world.

Using spatial network analysis to model pedal cycle flows, risk and mode choice

Using prefecture-level panel data and social media data, this study investigates how industrial agglomeration, environmental regulations, and technology affect the pollutant intensity and spillover channels of pollutant emissions by integrating social and economic networks into a Spatial Durbin Model. The results show that industrial agglomeration, environmental regulations, and technological inputs facilitate the emissions intensity abatement. The outcomes also confirm that these factors affect the intensity of pollutant emissions in neighboring regions through social, economic, and spatial networks. Agglomeration has a negative spillover effect on the intensity of pollutant emissions in surrounding cities via social and spatial networks, while environmental regulations affect pollutant emissions intensity in related cities through social networks. Technology can effectively lower pollutant emissions through economic networks. These findings highlight the network linkages and spillover channels affecting the intensity of pollutant emissions.

Singapore’s public housing policies are widely known for their success in providing housing for over 80% of the residential population, with most owning homes. Extensive housing schemes generally offer a set of fixed housing layouts; yet, at the microscale of an individual dwelling, alterations to the units can be made by rearranging the floor plans to reflect the usage patterns of the household, thus adding diversity to the fixed configuration. The aim of this paper is to identify associations among a housing unit’s spatial properties by analysing fixed configurations and altered floor plans as well as determining the spatial preferences of the inhabitants. The research methodology applied in this paper has been divided into two parts: spatial network analysis and survey analysis. In the spatial network analysis, the configurations of individual housing units and altered units were compared by translating floor plans into graphs. The survey functions as a qualitative analysis to relate the lifestyle patterns of contemporary society to housing configurations.

Future resilient cities need to integrate increasing density with the development of urban infrastructure and systems, especially natural and green systems. In Singapore’s post-Independence history, urban green spaces have evolved from leftover manicured grassy areas to biodiverse spaces with recreational areas and facilities. Singapore has recognized the need to provide varying access to the public to protect the more natural areas from too much public access. In urban green spaces, different areas have varying access depending on the scale and significance of the natural habitat. This paper uses Spatial Network Analysis — or the study of topological spatial relationships — to inform the planning and design of varying levels of access in urban green spaces in Singapore. It investigates the influence of spatial configuration of urban green spaces on pedestrian movement, by studying two types of urban green spaces in Singapore: one-north Park and Jurong Lakeside Garden. In both cases, the research utilizes weighted Spatial Network Analysis using sDNA, an analytical tool, to gain insights into the correlations between spatial connectivity and pedestrian movement distribution patterns. Empirical quantitative data are collected using infrared-sensor devices and manual pedestrian count and activity mapping. The methodology can provide the basis for the future planning and design of pedestrian infrastructure in urban green spaces in Singapore, and other high-density cities.

The human ecosystems embrace complex human-dominated systems, which often result in disparaging multifaceted social and ecological outcomes in various localities of the world. Green infrastructure (GI) with a well-planned and managed spatial organization and network of multifunctional landscapes does not only help improve the quality of life, but also promotes the multifunctional use of natural capital and enhances the resiliency of urban systems by enabling “disaster risk reduction”, or “DRR”, in real practice. To achieve more socially and ecologically resilient cities, the engagement of GI into the spatial network of the human ecosystem is inevitable. Moving on from this argument, the research utilizes several quantitative analysis tools, including space syntax methodology, graph theory, depth map analysis, linkage mapper analysis, and Arc-GIS to model the complex spatial patterns of the human ecosystem in the city center of Amman. To conclude, the study provides both theoretical evidence and practical assessment tools for the implementation of urban GI towards the sustenance of the social and ecological resiliency and NDRR within complex inner-city human ecosystems. The theoretical framework of this study embraces a novel contribution toward how resiliency and DRR theories can be merged into real practice through the utilization of a new methodological approach wherein the analysis, measurement, and visualization of human ecosystem spatial networks can be realized.

Abstract: Geospatial intelligence (GEOINT) is transforming the way organizations analyze, visualize, and interpret spatial data to drive decision-making in critical domains such as urban planning, disaster management, defense, and environmental monitoring. This chapter explores the integration of ArcGIS and Python for advanced spatial analytics, providing a comprehensive framework for automating geospatial workflows, enhancing big data processing, and applying AI-driven modeling techniques. Key topics include spatial data management, network and topological analysis, machine learning for geospatial intelligence, and cloud-based GIS solutions. With advancements in real-time geospatial analytics, edge computing, and blockchain for geospatial security, this chapter provides cutting-edge methodologies for harnessing Python's geospatial libraries alongside ArcGIS to build scalable, automated, and AI-powered GIS solutions. The application of deep learning in satellite imagery classification, predictive spatial modeling, and geospatial intelligence automation positions this integration as a game-changer in modern geospatial decision-making. Keywords: Geospatial intelligence, ArcGIS, Python, spatial analytics, AI-driven GIS, machine learning, remote sensing, geospatial big data, network analysis, predictive modeling, geostatistical analysis, cloud GIS, edge computing, digital twins, GIS automation, real-time geospatial analytics, blockchain GIS, urban planning, disaster management, spatial optimization, geospatial cybersecurity, deep learning for GIS.

To make and keep friends: The role of health status in adolescent network tie formation and persistence

A Boon or a Bane? An Examination of Social Communication in Social Trading

Estimated at 2.6 million annually, stillbirths worldwide have stayed alarmingly high, in contrast to neonatal and under-five mortality rates. It is a neglected public health challenge globally, with less attention to its social determinants. We examined spatial patterns of country-level stillbirth rates and determined the influence of social determinants of health on spatial patterns of stillbirth rates. We also estimated probabilistic relationships between stillbirth rates and significant determinants from the spatial analysis. Using country-level aggregated data from the United Nations databases, it employed ecological spatial analysis and artificial intelligence modeling based on Bayesian network among 194 World Health Organization member countries. From the spatial analysis, thirty-seven countries formed a cluster of high values (hot-spots) for stillbirth and 13 countries formed a cluster of low values (cold-spots). In the multivariate regression, gender inequality and anaemia in pregnancy were significantly associated with spatial patterns of higher stillbirth rates, while higher antenatal care (ANC) coverage and skilled birth attendants during delivery were associated with clusters of lower stillbirth rates. The Bayesian network model suggests strong dependencies between stillbirth rate and gender inequality index, geographic regions and skilled birth attendants during delivery. The Bayesian network predicted that the probability of low stillbirth rate increased from 56% to 100% when the percentage of countries with high skilled birth attendants during delivery increased from 70% to 88%, high ANC coverage increased from 55% to 70%, high prevalence of anaemia in pregnancy decreased from 27% to 11% and high gender inequality index decreased from 43% to 21%. Recognizing the urgency in reducing stillbirths globally, multi-pronged strategies should be designed to promote gender equality and strengthen the reproductive and maternal health services in Africa, Eastern Mediterranean, South Eastern Asia, and other countries with disproportionately high stillbirth rates.

BackgroundMaternal mortality remains a public health problem despite several global efforts. Globally, about 830 women die of pregnancy-related death per day, with more than two-third of these cases occurring in...

Analyzing the driving factors and mechanisms of urban carbon emission correlation networks can provide effective carbon reduction decision-making support for Shandong Province and other regions with similar industrial characteristics. Based on industrial carbon emission data from various cities in Shandong Province from 2013 to 2021, the spatial correlation network of carbon emission was established by using a modified gravity model. The characteristics of the network were explored by using the Social Network Analysis (SNA) method, and significant factors affecting the network were identified through Quadratic Assignment Procedure (QAP) correlation analysis and motif analysis. The driving mechanism of the carbon emission correlation network was analyzed by using Temporal Exponential Random Graph Models (TERGMs). The results show that: (1) The spatial correlation network of urban carbon emission in Shandong Province exhibits multi-threaded complex network correlations with a relatively stable structure, overcoming geographical distance limitations. (2) Qingdao, Jinan, and Rizhao have high degree centrality, betweenness centrality, and closeness centrality in the network, with Qingdao and Jinan being relatively central. (3) Shandong Province can be spatially clustered into four regions, each with distinct roles, displaying a certain “neighboring clustering” phenomenon. (4) Endogenous network structures such as Mutual, Ctriple, and Gwesp significantly impact the formation and evolution of the network, while Twopath does not show the expected impact; FDI can promote the generation of carbon emission reception relationships in the spatial correlation network; IR can promote the generation of carbon emission spillover relationships in the spatial correlation network; GS, differences in GDP, differences in EI, and similarities of IR can promote the generation of organic correlations within the network; on the temporal level, the spatial correlation network of urban carbon emission in Shandong Province has shown significant stability during the study period.