Urban heat islands in Mediterranean coastal cities have become an increasing concern, as climate changes in these regions exacerbate heat stress and other environmental challenges. The aim of this study was to investigate the intensity and temporal dynamics of urban heat island in the Tel Aviv Metropolis during the summer and winter months, nighttime and daytime, using an integrative approach combines local climate zone classifications, remote sensing, and meteorological data. The findings indicate daily and seasonal variations in both canopy urban heat islands and surface urban heat islands across the examined metropolis. In summer, the surface urban heat island was most pronounced at midday, whereas in winter it peaked at night. From a spatial aspect, its focus was found to shift eastward during the daytime and westward during the nighttime – regardless of season. The most notable finding is that the daily canopy urban heat islands in the Tel Aviv Metropolis were more pronounced during the summer (up to 4 °C) than in the winter (up to 2 °C), while the canopy nocturnal heat islands were more pronounced during the winter (up to 9 °C) than during the summer (up to 4 °C). Using the local climate zone (LCZ) classification, spatial analysis conducted in this study identified industrial areas (LCZ 8) and compact mid-rise residential ones (LCZ2) as the hottest, whereas open and vegetated areas were found to mitigate urban heating. These insights contribute to our understanding of urban heat dynamics in Mediterranean coastal contexts, supporting more sustainable planning strategies in these urban environments.

Pedestrian flow distributions can inform planning for walkability and improve understanding of factors that influence pedestrian activity. However, detailed data is rarely available so pedestrian volume models, commonly relying on the Space Syntax framework, are often utilized to predict pedestrian volumes. This study compares the performance and dominant variables of three modelling families - multiple regression analyses, machine learning models, and agent-based models - in Tel Aviv-Yafo, Israel. Using 247 flow observations, optimal models from each family were fitted and validated for 3 separate areas that differ in their urban growth and morphological characteristics, as well for the whole city. Results showed that ensemble-based machine learning models were best for city-wide predictions while agent-based models had an advantage at the local scale of neighborhoods - especially in neighborhoods that did not develop in a self-organized process. Regression analyses fell short for all areas, even when using principal component analysis to reduce multicollinearity and overfitting. These differences are attributed to the relative influence of cognitive-behavioral and structural factors on pedestrian flows: agent-based models outperform statistical models in individual areas, where behavior is captured more accurately using a small set of cognitive-behavioral parameters. Statistical models are dominant in the city-wide context, where structural variables can predict aggregate patterns. This is crucially important when evaluating the distribution of pedestrians in a planned urban environment. Overall, our results indicate that stepwise regression are not sufficient for pedestrian volume modelling, that agent-based models better capture complex interactions between independent variables, and that machine learning models have a strong potential for city-wide pedestrian volume modelling.

Humans use natural language to describe places, relying on mental processes to perceive, interpret, and communicate information about spatial relationships, environments, and navigation. Computational location-based systems strive to replicate this capability by enabling the retrieval of geographic locations from textual descriptions or queries. However, progress in this domain remains constrained by the limited availability of extensive, linguistically diverse textual datasets, which are essential for developing and evaluating robust geographic information retrieval methodologies. In this study, we conducted a review of existing geocoding datasets used for textual geolocation. Our objectives were to systematically compare these datasets, characterize their attributes, and assess their impact on retrieval performance as reported in the literature. A critical challenge we identified was the inconsistency in evaluation practices across studies, which complicates direct comparisons and underscores the need for standardized benchmarks. This review synthesizes the current landscape of geocoding datasets, offering insights for informed dataset development and fosters more consistent evaluation practices. By addressing the imperative of dataset standardization and availability, we aim to support the creation of more effective geographic information retrieval systems and establish a solid foundation for future research in this field.

In recent years, the concept of urban ‘walkability’ has become common in multiple fields connected to urban geography, urban planning, and has both social and environmental benefits. In the last decade urban climatology research has paid more attention to the effect of urban outdoor thermal conditions on walkability. Walkability can be defined as the extent to which the built environment enables, supports and encourages walking, by providing pedestrians on the move friendly and safety environment, visual interest in street network’ and thermal comfort This study examined the relations between outdoor thermal conditions and walkability in the Mediterranean City of Tel Aviv, aiming to: (1) assess the relationship between urban morphology at street level on the objective and subjective thermal comfort of pedestrians on the move, (2) quantify the influence of land use and centrality of streets on pedestrians' thermal perception, and (3) evaluate the seasonal and hourly effect of thermal comfort on pedestrian volume.Field campaigns were conducted in summer and winter, in six different types of streets including micro-climatic measurements, pedestrian observation and counting, and a bio-meteorology questionnaire survey.The results showed that the effect of thermal comfort is more pronounced in summer than winter thus during summer less pedestrian volume was observed during the hottest hours of the day. The pedestrian volume in winter is much higher than in summer. In commercial streets, the relation between thermal perception and pedestrian volume is weak, compared to noncommercial streets. During the summer the pedestrian volume in boulevards and shaded streets is higher compared to exposed streets.The findings indicate that thermal conditions affect pedestrian volume, but this is dependent upon the street network structure and type of land use.

Describing where a place is situated is an innate communication skill that relies on spatial cognition, spatial reasoning, and linguistic systems. Accordingly, textual geolocation, a task for retrieving the coordinates of a place from linguistic descriptions, requires computerized spatial inference and natural language understanding. Yet, machine-based textual geolocation is currently limited, mainly due to the lack of rich geo-textual datasets necessitated to train natural language models that, in-turn, cannot adequately interpret the language-based expressions. These limitations are intensified in morphologically rich and resource-poor languages, such as Hebrew. This study aims to analyze and understand the linguistic systems used for place descriptions in Hebrew, later to be used to train machine learning natural language models. A novel crowdsourced geo-textual dataset is developed, composed of 5,695 written place descriptions provided by 1,554 native Hebrew speakers. All place descriptions rely on memory only, which increases spatial vagueness and requires referring expression resolution. Qualitative linguistic analysis of place descriptions shows that geospatial reasoning is greatly used in Hebrew, while empirical analysis with textual geolocation engines indicates that literal descriptions pose challenges for existing methods, as they require real understanding of space and geospatial references and cannot simply be geolocated by matching gazetteer with textual geo-entity extractions. The findings offer improved understanding of the challenges entailed in natural language processing of Hebrew geolocation, contributing to formalizing computerized systems used in future machine learning models for complex geographic information retrieval tasks.

Cities have been shown to exhibit empirical scaling behavior where numerous variables of urban performance are allometric, like greenhouse gas emissions. Polluting emissions have negative environmental and health impacts. This paper will elucidate the empirical urban scaling of atmospheric emissions for the Israeli urban system. It has been shown that cities may be environmentally efficient with CO2 emissions that seem to be sub-linear, so large cities are more “green”. However, other reports suggest a super-linear relationship with respect to population size, so the large cities are less “green”. We report here for the first time the results of the nonlinear allometric power-law properties of multiple air pollutants, expanding the analysis to include electricity consumption and atmospheric emissions of CO2, NOx, SO2, CO, NMVOC, PM10, PM2.5, Benzene and 1,3-Butadiene together in one study in the case of Israel. They show the recurring mathematical patterns of cities similar to those reported elsewhere. Electricity usage is super-linear. Pollutant emissions of these greenhouse gases tend to exhibit significant super-linear dynamics (β > 1), though NMVOC and Benzene were linear. These results were conserved when regressing against the urban vehicle fleet size. This evidence supports the hypothesis that large cities may be less “green”. Indeed, different urban characteristics such as geography, local climate and weather conditions, population density, may also affect the pollution levels of cities. Taken together these results give evidence to the effect of urban agglomerations on the environment.

Empirically based theorization of walking range patterns is rather limited, leading researchers and planners to rely on simplistic assumptions as to the typical distance and duration that pedestrians may walk. Using high-resolution GPS data collected from over 11,000 participants in the Tel-Aviv metropolitan area, we provide an empirical estimate for the distribution of walking route distance and duration, while examining potential factors that may affect it. In addition, we develop a general analytical framework that describes walking route patterns. Our results show that the average route distance and duration in Tel-Aviv metropolitan is 630 m and 7.9 min. Factors associated with walking range include socio-demographic characteristics of walkers (age-group, socioeconomic status and number of cars in a household) and city characteristics (longer routes in cities with a larger population and in areas with high density of street intersections). Our main finding is that walking route distance distribution can be best described using the theoretical log-normal distribution and can be characterized using its mean-log and SD-log parameters. The log-normal parameters make an analytical framework that enables the evaluation of differences in walking patterns between places and identification of where interventions are required to promote active travel. We explain why the log-normal distribution is likely to be suitable to other cases worldwide.

Smartphones and mobile applications (apps) have become indispensable tools for travelers. Despite their pivotal role in the tourism industry and continuous advancements, our understanding of their usage and integration is limited. Adopting a bottom-up approach, we analyzed and characterized 347 tourism mobility apps, differentiating between globally-used apps and those that are developed and used locally in four renowned tourist destinations: Amsterdam, Barcelona, Venice, and Dubrovnik. The central attributes that characterize these apps were revealed through factor analysis, including tourist-oriented functionality, orientation and navigation, efficacy, effective mobility, social (interaction), and activities. Four types of apps, namely mobility, navigation, interact and experience, and social media, were then grouped using k-means clustering. Our typology facilitates a better understanding of the tourism apps market and the apps' added value. This topic is becoming increasingly important, considering the smartization processes that destinations are undergoing.

The task of textual geolocation - retrieving the coordinates of a place based on a free-form language description - calls for not only grounding but also natural language understanding and geospatial reasoning. Even though there are quite a few datasets in English used for geolocation, they are currently based on open-source data (Wikipedia and Twitter), where the location of the described place is mostly implicit, such that the location retrieval resolution is limited. Furthermore, there are no datasets available for addressing the problem of textual geolocation in morphologically rich and resource-poor languages, such as Hebrew. In this paper, we present the Hebrew Geo-Location (HeGeL) corpus, designed to collect literal place descriptions and analyze lingual geospatial reasoning. We crowdsourced 5,649 literal Hebrew place descriptions of various place types in three cities in Israel. Qualitative and empirical analysis show that the data exhibits abundant use of geospatial reasoning and requires a novel environmental representation.

Accessibility is fundamentally thought to be related to functional, economic, and social performances of cities and geographical systems and, therefore, constitutes an essential aspect for spatial planning. Previous studies focused on cities or metropolitan scales, often disregarding their position within regional and national systems, which can greatly affect their performance. Although accessibility at various spatial scales has been examined, the studies focused on accessibility patterns at different scales, with no reference to the level of accessibility of cities over local, regional, and national scales simultaneously, i.e. multiscale accessibility. This study aims to elucidate the multiscale accessibility level of individual cities and examine its relationship to urban performance in the urban system of Israel. Spatial accessibility was analyzed using the space syntax methodology for the entire national road network across multiple geographic scales—from the local to the national scale. Based on three distinct spatial accessibility systems identified, a unique multiscale accessibility profile was created for individual cities in Israel. Subsequently, each city’s multiscale accessibility profiles were examined against urban performance indicators determined from urban scaling theory. We found that the superiority of cities characterized by high accessibility level plays a role not only for a specific scale but also over scales and spatial systems. Moreover, most urban performance indicators related to the multiscale accessibility profiles of cities, while some multiscale accessibility profiles can be related to over- or under-performance of cities. The findings suggest that pervasive accessibility across spatial scales is inherently connected to urban performance and may indicate on the implementation and interpretation of accessibility. These findings may assist in various aspects of spatial planning at various scales.

Abstract Cities have been shown to exhibit empirical scaling behavior where numerous variables of urban performance are allometric, like greenhouse gas emissions. Polluting emissions have negative environmental and health impacts. Therefore, recently, this methodology of urban scaling has been implemented to study the dynamics of vehicle and industrial emissions into the environment. It has been shown that cities may be environmentally efficient with CO2 emissions that seem to be sublinear, so the large cities may be more "green". However, a number of reports suggest a superlinear relationship with respect to population size, so the large cities may be less "green". We report here the results of the nonlinear allometric power-law scaling properties of multiple air pollutants in the Israel urban system, expanding the analysis to include electricity consumption and atmospheric emissions of CO2, NOx, SO2, CO, NMVOC, PM10, PM2.5, benzene and 1,3-butadiene. The results show the recurring mathematical patterns of cities similar to those reported elsewhere. Electricity usage is superlinear. Pollutant emissions of these greenhouse gases tend to exhibit significant superlinear dynamics (β > 1), though NMVOC and Benzene were linear. The superlinear result was conserved when regressing against the number of vehicles. This evidence supports the hypothesis that large cities may be less "green". Indeed, different urban characteristics such as geography, local climate and weather conditions, population density, may also affect the pollution levels of cities. Taken together these results give evidence to the effect of urban agglomerations on the environment. With this perspective it may be possible to implement sustainable policy to improve the environment and increase human wellbeing.

Spatial accessibility is fundamentally related to the functional, economic and social performances of cities and geographical systems and, therefore, constitutes an essential aspect for spatial planning. Despite the significant progress made in accessibility research, little attention is given to the central role of accessibility in space organization and structuration. This study aimed to fill this gap. Based on an intensive literature review, our work shows the critical role of accessibility in space organization at different scales and sizes, starting from the basic concept of accessibility and its foundations in the classical locational theories and further to the methods and theories at the forefront of research. These processes also point to a unique contribution of multiscale accessibility in space structuration. Accordingly, we offer a conceptual framework to describe the multiscale process of space structuration with respect to local-urban, regional and national scales. We believe this framework may help in studying space and, more importantly, in understanding space. We hope this perspective forms an additional tier at the conceptual and methodological levels concerning accessibility and spatial organization and will encourage empirical studies in light of the suggested view.

Space is a natural and indispensable part of the human communication form, mostly based on natural-spatial descriptions with varying lexical structures that rely on human spatial cognition and perception. This is a “geographic language” which machines do not understand, and accordingly do not properly process. Consequently, geographic information retrieval is limited due to the lack of rich and comprehensive textual-geographical databases required, for example, for spatio-query processes. While in English there exists a relatively rich set of libraries and tools, in Hebrew there is a void, with no automatic tools for addressing this problem. We propose a methodology that mimics human literal place descriptions, utilizing implicit geometries and topologies existing in geospatial databases. This study focuses on the first stage, which includes collecting a lingual dataset of human place descriptions with an online survey. Using Hebrew Natural Language Processes, place entities and their spatial relations were extracted from the survey descriptions. Similar place entities and relations were simultaneously extracted from OpenStreetMap database. Through place queries that rely on textual phrases from these two sources, human descriptions of places were geolocated. Finally, these locations were compared to retrieved locations acquired through Google maps API on survey descriptions - showing very promising results in accurately locating the described places.

Recent approaches in the research on walkable environments and wellbeing go beyond correlational analysis to consider the specific characteristics of individuals and their interaction with the immediate environment. Accordingly, a need has been accentuated for new human-centered methods to improve our understanding of the mechanisms underlying environmental effects on walking and consequently on wellbeing. Immersive virtual environments (IVEs) were suggested as a potential method that can advance this type of research as they offer a unique combination between controlled experimental environments that allow drawing causal conclusions and a high level of environmental realism that supports ecological validity. The current study pilot tested a walking simulator with additional sensor technologies, including biosensors, eye tracking and gait sensors. Results found IVEs to facilitate extremely high tempo-spatial-resolution measurement of physical walking parameters (e.g., speed, number of gaits) along with walking experience and wellbeing (e.g., electrodermal activity, heartrate). This level of resolution is useful in linking specific environmental stimuli to the psychophysiological and behavioral reactions, which cannot be obtained in real-world and self-report research designs. A set of guidelines for implementing IVE technology for research is suggested in order to standardize its use and allow new researchers to engage with this emerging field of research.

The traditional approach to mobile phone positioning is based on the assumption that the geographical location of a cell tower recorded in a Call Details Record (CDR) is a proxy for a device's location. A Voronoi tessellation is then constructed based on the entire network of cell towers and this tessellation is considered as a coordinate system, with the device located in a Vomnoi polygon of a cell tower that is recorded in the CDR. If Voronoi-based positioning is correct, the uniqueness of the device trajectory is very high, and the device can be identified based on 3-5 of its recorded locations. We investigate a probabilistic approach to device positioning that is based on knowledge of each antennas' parameters and number of connections, as dependent on the distance to the antenna. The critical difference between the Voronoi-based and the real world layout is in the essential overlap of the antennas' service areas: The device that is located in a cell tower's polygon can be served by a more distant antenna that is chosen by the network system to balance the network load. Combining data on the distance distribution of the number of connections available for each antenna in the network, we resolve the overlap problem by applying Bayesian inference and construct a realistic distribution of the device location. Probabilistic device positioning demands a full revision of mobile phone privacy and new full set of tools for data analysis.

The traditional approach to mobile phone positioning is based on the assumption that the geographical location of a cell tower recorded in a call details record (CDR) is a proxy for a device's location. A Voronoi tessellation is then constructed based on the entire network of cell towers and this tessellation is considered as a coordinate system, with the device located in a Voronoi polygon of a cell tower that is recorded in the CDR. If Voronoi-based positioning is correct, the uniqueness of the device trajectory is very high, and the device can be identified based on 3-4 of its recorded locations. We propose and investigate a probabilistic approach to device positioning that is based on knowledge of each antennas' parameters and number of connections, as dependent on the distance to the antenna. The critical difference between the Voronoi-based and the real world layout is in the essential overlap of the antennas' service areas: the device that is located in a cell tower's polygon can be served by a more distant antenna that is chosen by the network system to balance the network load. This overlap is too significant to be ignored. Combining data on the distance distribution of the number of connections available for each antenna in the network, we succeed in resolving the overlap problem by applying Bayesian inference and construct a realistic distribution of the device location. Probabilistic device positioning demands a full revision of mobile phone data analysis, which we discuss with a focus on privacy risk estimates.

Street patterns of Israeli cities were investigated by comparing three time periods of urban development: (I) the late 19th century until the establishment of the state of Israel in 1948; (II) 1948 until the 1980s; and (III) the late 1980s until the present. These time periods are related respectively to the pre-modern, modern and late-modern urban planning approach. Representative urban street networks were examined in selected cities by means of morphological analysis of typical street pattern properties: curvature, fragmentation, connectivity, continuity and differentiation. The study results reveal significant differences between the street patterns of the three examined periods in the development of cities in Israel. The results show clearly the gradual trends in the intensification of curvature, fragmentation, complexity and hierarchical organization of street networks as well as the weakening of the network's internal and external connectivity. The implications of these changes on connectivity and spatial integration are discussed with respect to planning approaches.

Accessibility is a well-known basic term in spatial science and planning and is inherently related to functional aspects of places and regions. Although previous studies have examined functional systems and spatial accessibility few have attended to the association among them across geographical scales. Our study attempts to fill this gap. Using the space syntax methodology, spatial accessibility was analyzed for the entire national road network of Israel across different geographic scales - from local culminating in the national scale. The analysis was based on angular segment analyses of the road center-line network. Following this, the correlation between spatial accessibility across scales and functional performance of employment and commuting flows was examined. The study findings show a significant correspondence and exposes transitions between local, regional and national spatio-functional systems. First, a significant correlation between local (2 km radius) accessibility levels of settlements with the number of employees and commuters. Second, the regional/metropolitan system (10-15 km radius) accessibility is highly correlated to the emergence of the main employment centers. Third, the main metropolitan areas are integrated at higher scale (from 30 km radius) and form together a core region characterized by high accessibility as well as well connectivity through commuting flows. In contrast, no substantial commuting flows were found within the periphery, as well as between periphery-core. The findings show clearly that this functional structure corresponds to the multi-scale accessibility levels of settlements. We conclude that the core region functions at multiple scales (local-regional-national) while the periphery functions mostly at a local scale.

Geographic space is better understood through the topological relationship of the underlying streets (note: entire streets rather than street segments), which enables us to see scaling or fractal or living structure of far more less-connected streets than well-connected ones. It is this underlying scaling structure that makes human activities predictable, albeit in the sense of collective rather than individual human moving behavior. This topological analysis has not yet received its deserved attention in the literature, as many researchers continue to rely on segment analysis for predicting human activities. The segment analysis-based methods are essentially geometric, with a focus on geometric details of locations, lengths, and directions, and are unable to reveal the scaling property, which means they cannot be used for the prediction of human activities. We conducted a series of case studies using London streets and tweet location data, based on related concepts such as natural streets, and natural street segments (or street segments for short), axial lines, and axial line segments (or line segments for short). We found that natural streets are the best representation in terms of human activities or traffic prediction, followed by axial lines, and that neither street segments nor line segments bear a good correlation between network parameters and tweet locations. These findings point to the fact that the reason why space syntax based on axial lines, or the kind of topological analysis in general, works has little to do with individual human travel behavior or ways that humans conceptualize distances or spaces. Instead, it is the underlying scaling hierarchy of streets – numerous least-connected, a very few most-connected, and some in between the least- and most-connected – that makes human activities predictable.

Social and economic interactions between people are a crucial property of cities. These interactions create the conditions for groupings of individuals in social and functional areas within the city. Accordingly, urban models describe and explain the dynamics of residential and non-residential land-use patterns, movement flows, and the relationships between them. However, new social media and digital social networks (DSN) linked to the use of information and communications technology (ICT) and new travel behaviour patterns are calling into questioning traditional models and definitions of what characterises a city. The goal of this chapter is to explore how these new DSN and ICT tools relate to urban models and their potential contribution for urban modelling, i.e., the design process of urban models. To this end, we examine how the meaning of the city and urban models are called into question or advanced when placed in a dialogue with DSN and new mobility patterns, and how this process could affect urban comparative and policy analysis. We suggest that DSN can enhance urban modelling, not only as a source of data but should be integral part of them. This is so because DSN not only tell us how and when individuals use urban spaces; they themselves affect how and when individuals use urban spaces. In its conclusion, the chapter highlights the implications of ICT data, particularly social-media and DSN data, on some aspects of city's dynamics.