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

Libya is the African country with the highest proportion of migrants and it is the major migration gateway to Europe. Since the fall of Gaddafi’s regime in 2011, Libya has been experiencing a prolonged period of instability and conflict which has also affected migration flows. This paper provides the first analytical study of the networks of irregular migrants toward, within, and from Libya since the beginning of the Second Civil War in 2014. We use longitudinal-geolocalised data on migrants’ movements and combine tools taken from spatial statistics and network analysis to reconstruct the network of migrants’ international movements towards and from Libya, and the network of migrants’ internal movements within Libya. Employing the information obtained from these networks, we map the different migrant routes within the country, we provide evidence that migrants sort into routes according to their nationality, and we show how these routes have evolved over time. Moreover, using a Separable Temporal Exponential Random Graph (STERG) model, we document that migrants are more likely to move towards areas with higher economic opportunities, lower level of conflict intensity, more same country-of-origin migrants, and connecting to multiple routes. Our analysis shows how to combine migration data with insights from spatial statistics and network analysis in order to reconstruct migrants’ movements.

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.

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.

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.

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.

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.

Traditional mode choice models consider travel modes of an individual in a consecutive trip to be independent. However, a persons choice of the travel mode of a trip is likely to be affected by the mode choice of the previous trips, particularly when it comes to car driving. Furthermore, traditional travel mode choice models involve discrete choice models, which are largely derived from expert knowledge, to build rules or heuristics. Their approach relies heavily on a predefined specific model structure (utility model) and constraining it to hold across an entire series of historical observations. These studies also assumed that the travel diaries of individuals in travel survey data is complete, which seldom occurs. Therefore, in this chapter, we propose a data-driven methodology with artificial neural networks (ANNs) and fuzzy sets (to better represent historical knowledge in an intuitive way) to model travel mode choices. The proposed methodology models and analyses travel mode choice of an individual trip and its influence on consecutive trips of individuals. The methodology is tested using the Household Travel Survey (HTS) data of Sydney metropolitan area and its performance is compared with the state-of-the-art approaches such as decision trees. Experimental results indicate that the proposed methodology with ANN and fuzzy sets can effectively improve the accuracy of travel mode choice prediction.

Detecting Foodborne Disease Outbreaks in Florida through Consumer Complaints

Purpose – The aim of this paper is to find suitable measures that facility managers can use to prove the importance of a building for employee behaviour (and thus organizational performance). The amount of knowledge sharing between colleagues is studied, because it influences innovation, which is one of the most important organizational goals these days.Design/methodology/approach – Knowledge sharing in open plan areas is studied with the help of measures derived from spatial network analysis methods. Data were collected from 138 employees working together in a research building and used to show the effect of co‐presence on the amount of knowledge sharing and the way knowledge is shared.Findings – Co‐presence explains 12 percent of the variability in the number of knowledge‐sharing meetings. Also the way knowledge is shared differs between people working in co‐presence and people who have separate rooms/areas. Spatial network analysis is an adequate way to describe open plan workplaces.Originality/value –...

A tour-based microsimulation approach to modeling destination choice and mode choice of San Francisco residents is presented. These models were developed as part of an overall tour-based travel demand forecasting model (SF model) for the San Francisco County Transportation Authority to provide detailed forecasts of travel demand for various planning applications. The models described represent two of the nine primary components of the SF model. Both model components consist of multiple logit choice models and include both tour-level models (which refer to the primary activity of the tour) and trip-level models (other activities on the tour). A separate model was estimated for each tour purpose, including work, school, other, and work-based. The destination choice models combine the trip attraction and trip distribution components of the traditional four-step process and use a multinomial logit specification. The mode choice models utilize a nested logit formulation to capture the similarities among sets of similar modes. The two models are linked by incorporating the mode choice utility logsum in the destination choice models; the result is equivalent to a nested structure with a mode choice nest under destination choice. It is demonstrated that the microsimulation approach easily allows the inclusion of a number of key variables in destination and mode choice models that have a significant explanatory power compared with those in traditional models. It is also shown that this approach allows estimation of the effects of tour characteristics on the choice of destination and mode using widely available data and estimation procedures.

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...

In the context of promoting sustainable mobility, walking and cycling have been widely recognized for their environmental and health benefits. However, a notable gap often exists between residents’ motivation to engage in these modes and their actual behavior. This study focuses on this motivation–behavior discrepancy and explores how heterogeneous information interventions—within the constraints of the existing built environment—can effectively influence residents’ travel psychology and behavior. Drawing on Protection Motivation Theory, this study aims to uncover the psychological mechanisms behind travel-mode choices and quantify the relative impacts of different types of information interventions. A travel survey was conducted in Yangzhou, China, collecting data from 1052 residents. Cluster analysis was performed using travel psychology data to categorize travel motivations and examine their alignment with actual travel behavior. A random forest model was then employed to assess the effects of individual attributes, travel characteristics, and information intervention attributes on the choice of walking and cycling. The results reveal a significant motivation–behavior gap: while 76% of surveyed residents expressed motivation to walk or cycle, only 30% actually adopted these modes. Based on this, further research shows that informational attributes exhibit a stronger effect in terms of promoting walking and cycling behavior compared to individual attributes and travel characteristics. Among these, health-related information demonstrates the maximum efficacy in areas with well-developed infrastructure. Specifically, health-related information has a greater impact on cycling (21.4%), while environmental information exerts a stronger influence on walking (7.31%). These findings suggest that leveraging information to promote walking and cycling should be more targeted.

The scenic byways in Western Sichuan are some of the most popular self-driving tourism destinations in China. However, the current network of scenic byways in the region is not well-coordinated with the level of regional tourism development. This paper, based on travel digital footprints, uses methods such as spatial design network analysis, GIS spatial analysis, social network analysis models, and spatial econometric models to analyze the accessibility and self-driving tourism spatial behavior characteristics in Western Sichuan. The main research results are as follows: (1) the accessibility level of scenic byways in Western Sichuan exhibits significant spatial variation, with the majority of areas demonstrating moderate to poor accessibility; (2) the network structure of self-driving tourism spatial behavior displays characteristics of low overall network density, but with a high clustering coefficient and relatively short average path length, indicating a significant small-world phenomenon. All network node indicators exhibit significant heterogeneity, with the core nodes displaying clear clustering characteristics; (3) the accessibility of scenic byways and self-driving tourism spatial behavior exhibit significant spatial spillover effects. This study analyzes the relationship between the accessibility of scenic byways and self-driving tourism spatial behavior in Western Sichuan, providing valuable insights for the planning and construction of scenic byways and the development of self-driving tour routes.

Current spatial decision support systems (SDSS) in manufacturing environments, which are mostly CAD-based, have many drawbacks and thus may make less effective and efficient spatial decisions. GIS-based SDSS can overcome those drawbacks and support decision-making in a much effective manner. Unfortunately, there is little research on GIS-based SDSS in a manufacturing environment. This thesis study focuses on a systematic approach to the development of GIS-based SDSS in a manufacturing environment. By using Trane China as an example, the prototype of Trane China SDSS (TCSDSS) is developed by adopting the unified software development process. The TCSDSS prototype contains eight object modules, which incorporate ArcGIS embedded models, ArcGIS feature data, and MS Access attribute data, to support decision-making in Trane China. The benefits from the use of the TCSDSS prototype that have been found include its spatial analysis, network analysis, and 3D analysis functions besides the improvement on communication and efficiency. It is also found that the newly developed GIS software packages such as ArcGIS 8.3, with the enhanced drawing functions, can provide enough adequate precision level required by facility layout.