Application of high-throughput sequencing to whole rabies viral genome characterisation and its use for phylogenetic re-evaluation of a raccoon strain incursion into the province of Ontario

Raccoon rabies remains a serious public health problem throughout much of the eastern seaboard of North America due to the urban nature of the reservoir host and the many challenges inherent in multi-jurisdictional efforts to administer co-ordinated and comprehensive wildlife rabies control programmes. Better understanding of the mechanisms of spread of rabies virus can play a significant role in guiding such control efforts. To facilitate a detailed molecular epidemiological study of raccoon rabies virus movements across eastern North America, we developed a methodology to efficiently determine whole genome sequences of hundreds of viral samples. The workflow combines the generation of a limited number of overlapping amplicons covering the complete viral genome and use of high throughput sequencing technology. The value of this approach is demonstrated through a retrospective phylogenetic analysis of an outbreak of raccoon rabies which occurred in the province of Ontario between 1999 and 2005. As demonstrated by the number of single nucleotide polymorphisms detected, whole genome sequence data were far more effective than single gene sequences in discriminating between samples and this facilitated the generation of more robust and informative phylogenies that yielded insights into the spatio-temporal pattern of viral spread. With minor modification this approach could be applied to other rabies virus variants thereby facilitating greatly improved phylogenetic inference and thus better understanding of the spread of this serious zoonotic disease. Such information will inform the most appropriate strategies for rabies control in wildlife reservoirs

Unraveling urban form and collision risk: The spatial distribution of traffic accidents in Zanjan, Iran

Official statistics demonstrate the role of traffic accidents in the increasing number of fa-talities, especially in emerging countries. In recent decades, the rate of deaths and injuries caused by traffic accidents in Iran, a rapidly growing economy in the Middle East, has risen significantly with respect to that of neighboring countries. The present study illustrates an exploratory spatial analysis’ framework aimed at identifying and ranking hazardous locations for traffic accidents in Zanjan, one of the most populous and dense cities in Iran. This framework quantifies the spatiotem-poral association among collisions, by comparing the results of different approaches (including Kernel Density Estimation (KDE), Natural Breaks Classification (NBC), and Knox test). Based on descriptive statistics, five distance classes (2–26, 27–57, 58–105, 106–192, and 193–364 meters) were tested when predicting location of the nearest collision within the same temporal unit. The empirical results of our work demonstrate that the largest roads and intersections in Zanjan had a significantly higher frequency of traffic accidents than the other locations. A comparative analysis of distance bandwidths indicates that the first (2–26 m) class concentrated the most intense level of spatiotem-poral association among traffic accidents. Prevention (or reduction) of traffic accidents may benefit from automatic identification and classification of the most risky locations in urban areas. Thanks to the larger availability of open-access datasets reporting the location and characteristics of car accidents in both advanced countries and emerging economies, our study demonstrates the potential of an integrated analysis of the level of spatiotemporal association in traffic collisions over metropolitan regions

Population Dynamics, Agglomeration Economies and Municipal Size: a Long-Term Analysis

Under the hypothesis that modifications in municipal boundaries and creation (or suppression) of new administrative units reflect a progressive adjustment toward a more balanced distribution of population over space, the present study investigates the long-term relationship (1928-2012) between urban expansion, population dynamics and municipal area in a growing metropolitan region (Athens, Greece). In expanding regions, municipal size is a key variable outlining the amount and spatial concentration of services and infrastructures, resulting to be functionally related to population density, agglomeration factors, land availability to building and characteristic socioeconomic profiles of local communities. A statistical analysis of the relationship between population density and municipal area provides basic knowledge to policy and planning adjustments toward a more balanced spatial distribution of population and land among the local government units. Descriptive statistics, mapping, correlation analysis and linear regressions were used to assess the evolution of such relationship over a sufficiently long time period. The average municipal area in Athens decreased moderately over time, with a slight increase in spatial heterogeneity. Conversely, the average population density per municipality increased more rapidly, with a considerable reduction in spatial heterogeneity. The observed goodness-of-fit of the linear relationship between population density and municipal area increased significantly over time. The empirical results of our study indicate that municipal size has progressively adjusted to population density across metropolitan areas, determining a more balanced spatial distribution of the resident population, which was consolidated by the recent administrative reform of the local authorities in Greece (the so called ‘Kallikratis’ law). Such conditions represent a base for the informed analysis of the spatial structure of local administrative units and they contribute to the debate on the optimal size of municipalities and other administrative districts with relevant impact on both urban and metropolitan scales of governance

Population dynamics, agglomeration economies and municipal size. A long-term analysis

Under the hypothesis that modifications in municipal boundaries and creation (or suppression) of new administrative units reflect a progressive adjustment toward a more balanced distribution of population over space, the present study investigates the long-term relationship (1928-2012) between urban expansion, population dynamics and municipal area in a growing metropolitan region (Athens, Greece). In expanding regions, municipal size is a key variable outlining the amount and spatial concentration of services and infrastructures, resulting to be functionally related to population density, agglomeration factors, land availability to building and characteristic socioeconomic profiles of local communities. A statistical analysis of the relationship between population density and municipal area provides basic knowledge to policy and planning adjustments toward a more balanced spatial distribution of population and land among the local government units. Descriptive statistics, mapping, correlation analysis and linear regressions were used to assess the evolution of such relationship over a sufficiently long time period. The average municipal area in Athens decreased moderately over time, with a slight increase in spatial heterogeneity. Conversely, the average population density per municipality increased more rapidly, with a considerable reduction in spatial heterogeneity. The observed goodness-of-fit of the linear relationship between population density and municipal area increased significantly over time. The empirical results of our study indicate that municipal size has progressively adjusted to population density across metropolitan areas, determining a more balanced spatial distribution of the resident population, which was consolidated by the recent administrative reform of the local authorities in Greece (the so called \u2018Kallikratis\u2019 law). Such conditions represent a base for the informed analysis of the spatial structure of local administrative units and they contribute to the debate on the optimal size of municipalities and other administrative districts with relevant impact on both urban and metropolitan scales of governance

A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk

BACKGROUND: Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. METHODS: We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.). RESULTS: We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures. CONCLUSIONS: Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping

Mobile networks and internet of things infrastructures to characterize smart human mobility

The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches.This work has been supported by FCT–Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. This work has also been supported by national funds through FCT–Fundação para a Ciência e Tecnologia through project UIDB/04728/202

Found in Complexity, Lost in Fragmentation: Putting Soil Degradation in a Landscape Ecology Perspective

The United Nations Convention to Combat Desertification (UNCCD) assumes spatial disparities in land resources as a key driver of soil degradation and early desertification processes all over the world. Although regional divides in soil quality have been frequently observed in Mediterranean-type ecosystems, the impact of landscape configuration on the spatial distribution of sensitive soils was poorly investigated in Southern Europe, an affected region sensu UNCCD. Our study proposes a spatially explicit analysis of 16 ecological metrics (namely, patch size and shape, fragmentation, interspersion, and juxtaposition) applied to three classes of a landscape with different levels of exposure to land degradation (‘non-affected’, ‘fragile’, and ‘critical’). Land classification was based on the Environmentally Sensitive Area Index (ESAI) calculated for Italy at 3 time points along a 50-year period (1960, 1990, 2010). Ecological metrics were calculated at both landscape and class scale and summarized for each Italian province—a relevant policy scale for the Italian National Action Plan (NAP) to combat desertification. With the mean level of soil sensitivity rising over time almost everywhere in Italy, ‘non-affected’ land became more fragmented, the number of ‘fragile’ and ‘critical’ patches increased significantly, and the average patch size of both classes followed the same trend. Such dynamics resulted in intrinsically disordered landscapes, with (i) larger (and widely connected) ‘critical’ land patches, (ii) spatially diffused and convoluted ‘fragile’ land patches, and (iii) a more interspersed and heterogeneous matrix of ‘non affected’ land. Based on these results, we discussed the effects of increasing numbers and sizes of ‘critical’ patches in terms of land degradation. A sudden expansion of ‘critical’ land may determine negative environmental consequences since (i) the increasing number of these patches may trigger desertification risk and (ii) the buffering effect of neighboring, non-affected land is supposed to be less efficient, and this contains a downward spiral toward land degradation less effectively. Policy strategies proposed in the NAPs of affected countries are required to account more explicitly on the intrinsic, spatio-temporal evolution of ‘critical’ land patches in affected regions

Seeking the Optimal Dimension of Local Administrative Units: A Reflection on Urban Concentration and Changes in Municipal Size

In the search for a better administrative functioning as a key dimension of economic performances, changes in municipal boundaries and the creation (or suppression) of local administrative units reflect a progressive adjustment to a spatially varying population size and density. With intense population growth, municipal size reflects the overall amount (and spatial concentration) of services and infrastructures, being functionally related with agglomeration economies, land availability for building, and specific sociodemographic attributes of local communities. Based on these premises, the intrinsic relationship between settlement expansion, population growth, and municipal size in a metropolitan region of Southern Europe (Attica, hosting the Greater Athens’ area in Central Greece) was investigated in this study over nearly one century as a contribution to a refined investigation of the (changing) organization of local administrative units under a complete metropolitan cycle from urbanization to reurbanization. Based on descriptive statistics, mapping, (parametric and nonparametric) correlation coefficients, and econometric techniques, a quantitative analysis of the relationship between population size and density and municipal area provides pivotal knowledge to policy and planning adjustments toward a more balanced spatial distribution of population and administered land among local government units. Together with a slight decrease in the average municipal size over time, the average population density per municipal unit increased systematically, with a considerable reduction in spatial heterogeneity of settlements. The observed goodness-of-fit of the linear model explaining municipal area with population density, increased significantly over time. Empirical results of our study indicate that municipal size has slowly adjusted to population density across metropolitan areas, determining an imbalanced spatial distribution of resident population and a supposedly less efficient government partition. The recent administrative reform of local authorities in Greece (the so-called ‘Kallikratis’ law) seems to consolidate–rather than rebalance this organizational structure over space, reflecting spatially polarized settlements. Such conditions represent a base for informed analysis of the spatial structure of local administrative units as a pivotal element of economic sustainability and may contribute to the debate on the optimal size of municipalities at both urban and metropolitan scales of governance

Opportunities and challenges of geospatial analysis for promoting urban livability in the era of big data and machine learning

Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement

Metodología para deducir relaciones de linaje en el Catastro de España

En España, los datos catastrales de acceso público, obtenidos a través de la Sede Electrónica del Catastro (SEC), no incluyen información sobre la genealogía o las relaciones de linaje existente entre las parcelas, de forma que la gestión de la información histórica es muy limitada. Este artículo presenta un método para obtener las relaciones de linaje más frecuentes entre las parcelas (agregación y segregación) y propone un prototipo de estructura relacional para el almacenamiento y la gestión histórica de los datos catastrales de acceso público. El proceso de análisis para deducir el linaje se basa en superposiciones espacio-temporales junto con secuencias de sentencias SQL. El método proporciona un 70% de relaciones de agregación y segregación correctas; el resto presentan errores debidos, en general, a anomalías presentes en los propios datos catastrales