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Quantifying urban disaster resilience is needed to strengthen the capacity of urban disaster prevention and mitigation strategies. In previous studies, the spatial unit used for urban disaster resilience assessment was coarse, which could easily cause local differences to be overlooked. To rectify the problem, this study integrated high spatial-resolution urban grid data and built an index system from economic, social, administrative, and technological dimensions. On this basis, both exponential decay function and spatial analysis techniques were applied to establish a high-precision evaluation method of urban disaster resilience. The results showed that the urban disaster resilience of Futian District (FTUDRI) had a certain spatial aggregation effect, with an average of 0.37 ± 0.08, which was at a middle level. Areas with low and middle-low levels of FTUDRI which accounted for 27.28% were mainly located in the western, northern, and southern edges of Futian District, with undeveloped economy and inadequate emergency infrastructures. We considered the neighborhood impact of point indicators on urban disaster resilience and the results indicated if we neglected this there would be an underestimation of the urban disaster resilience assessment. Hospitals, administrations, and emergency material points were key driving indicators for improving FTUDRI. In conclusion, our results demonstrated the necessity of pursuing high-resolution urban disaster resilience assessments and the effectiveness of the newly proposed method. The results of this study can provide policymakers with a clear location of where a low level of urban disaster resilience occurs, providing a basis for urban disaster prevention and improvement measures.

Since the first two novel coronavirus cases appeared in January of 2020, the outbreak of the COVID-19 epidemic seriously threatens the public health of Italy. In this article, the distribution characteristics and spreading of COVID-19 in various regions of Italy were analysed by heat maps. Meanwhile, spatial autocorrelation, spatiotemporal clustering analysis and kernel density method were also applied to analyse the spatial clustering of COVID-19. The results showed that the Italian epidemic has a temporal trend and spatial aggregation. The epidemic was concentrated in northern Italy and gradually spread to other regions. Finally, the Google Trends index of the COVID-19 epidemic was further employed to build a prediction model combined with machine learning algorithms. By using Adaboost algorithm for single-factor modelling,the results show that the AUC of these six features (mask, pneumonia, thermometer, ISS, disinfection and disposable gloves) are all >0.9, indicating that these features have a large contribution to the prediction model. It is also implied that the public's attention to the epidemic is increasing as well as the awareness of the need for protective measures. This increased awareness of the epidemic will prompt the public to pay more attention to protective measures, thereby reducing the risk of coronavirus infection.

Many wildlife species survive in human-modified landscapes and understanding the opinions of those who share space with wildlife will aid conservation efforts. Using a questionnaire, we assessed the presence of 12 mammal species in 78 tea plantations in the Nilgiris, southern India. We obtained data on (i) plantation size, location, and elevation, (ii) species presence over a year, (iii) type and number of wildlife incidents caused, (iv) financial cost of wildlife damage, and (v) support for wildlife conservation. We used a generalized linear model to assess whether the distance to protected areas, elevation, and plantation size influenced species presence and the effect of these variables and wildlife incidents on support for conservation. Among all species reported, Bonnet Macaque, Wild Boar, and Porcupine were the most widespread, and the former two and the Gaur reportedly caused >50% of damages. Crop damage was the most frequent (74%, n = 244), whereas livestock predation, attacks on people, and infrastructure damage constituted

Objective: The identification of neighborhoods where registered sex offenders (RSOs) reside may facilitate evidence-based service delivery and increase neighborhood capacity building. This ...

COVID-19 is threatening the whole world. This paper aims to explore the correlation between climatic factors and the morbidity of COVID-19 in Wuhan, China, mainly by using a geographic detector and GWR model. It was found that the response of the morbidity of COVID-19 to meteorological factors in Wuhan is different at different stages. On the whole, the morbidity of COVID-19 has a strong spatial aggregation, mainly concentrated in the central area of Wuhan City. There is a positive correlation between wind speed and the spread of COVID-19, while temperature has a negative correlation. There is a positive correlation between air pressure and the number of COVID-19 cases. Rainfall is not significantly correlated with the spread of COVID-19. It is concluded that wind speed, relative humidity, temperature, and air pressure are important meteorological factors affecting the spread of COVID-19 in Wuhan. Any two variables have greater interaction with the spatial distribution of the incidence rate of COVID-19 than any one factor alone. Those findings not only provide a new insight for the key intervention measures and the optimal allocation of health care resources accordingly but also lay a theoretical foundation for disease prevention, disease intervention and health services.

Nighttime lights (NTL) are a popular type of data for evaluating economic performance of regions and economic impacts of various shocks and interventions. Several validation studies use traditional statistics on economic activity like national or regional gross domestic product (GDP) as a benchmark to evaluate the usefulness of NTL data. Many of these studies rely on dated and imprecise Defense Meteorological Satellite Program (DMSP) data and use aggregated units such as nation-states or the first sub-national level. However, applied researchers who draw support from validation studies to justify their use of NTL data as a proxy for economic activity increasingly focus on smaller and lower level spatial units. This study uses a 2001–19 time-series of GDP for over 3100 U.S. counties as a benchmark to examine the performance of the recently released version 2 VIIRS nighttime lights (V.2 VNL) products as proxies for local economic activity. Contrasts were made between cross-sectional predictions for GDP differences between areas and time-series predictions of GDP changes within areas. Disaggregated GDP data for various industries were used to examine the types of economic activity best proxied by NTL data. Comparisons were also made with the predictive performance of earlier NTL data products and at different levels of spatial aggregation.

Understanding how species are non-randomly distributed in space, as well as how the resulting spatial structure of diversity responds to ecological, biogeographic and anthropogenic drivers is a critical piece of the biodiversity puzzle. However, most metrics that quantify the spatial structure of diversity (i.e., community differentiation), such as Whittaker’s classical β-diversity metric are influenced by sampling effects. As a result, these measures are influenced by species pool size, species abundance distributions and numbers of individuals. Null models have been proposed to evaluate the degree of differentiation among communities due to spatial structuring relative to that expected from sampling effects. However, to date, these null models do not accommodate the influence of sample completeness (i.e. the proportion of the species pool in the sample). Here, we develop an approach that makes use of individual- and coverage-based rarefaction and extrapolation. Using spatially explicit simulations, we show that our derived metric, βC, captures changes in intraspecific aggregation independently of changes in the species pool size. We then provide two case studies examining spatial structure in forest plots spanning latitudinal gradients: (1) a re-analysis of the “Gentry” plot dataset, and (2) comparing a high diversity plot in Barro Colorado Island, Panama with a low diversity plot in Harvard Forest, Massachusetts, USA. We find no evidence for systematic changes in spatial structure with latitude in these datasets. As it is rooted in biodiversity sampling theory and explicitly controls for sample completeness, our approach represents an important advance over existing null models for spatial aggregation. Potential applications of the approach range from better descriptors of biogeographic diversity patterns to the consolidation of local and regional diversity trends in the current biodiversity crisis.Open research statementThe novel code for the calculation of βC can be found in supplementary material S4. Empirical data sets utilized for this research are as follows: Phillips & Miller (2002), Orwig et al. (2015), Condit et al. (2019). Our research repository including the novel code is also available at https://github.com/t-engel/betaC and will be uploaded to Zenodo upon acceptance of this manuscript.

Based on the conclusions of the previous chapter, there are four main transformations in spatial-temporal distributions of urban ethnic minorities in China. The first is a change in the spatial-aggregation of ethnic minorities in the form of a periphery to core transition as urban minorities increasingly migrated from county-level cities to prefecture-level cities or above. The second is a decline in urban minority populations in the central region as a result of outward economic migration to cities in the east and west. This leads to the third transformed characteristic, which is the rapid growth of ethnic minorities in eastern city agglomerations. Finally, the last transformation is the rise in prominence of provincial capitals as a concentration of urban minorities in western China thanks to the implementation of the Western Development Strategy in 2000.

Understanding how urbanisation and industrialisation interact with carbon emissions (CEs) among different regions informs the approaches for achieving emissions reduction targets and promoting regional economic development. However, this topic has not been adequately addressed in previous studies. Therefore, it is of great significance to conduct an empirical investigation of the interactions between urbanisation, industrialisation and CEs. Aided by the gravity model, the barycentre trajectories of urbanisation, industrialisation and CEs are analysed. Additionally, the total transfer amounts of industry and CEs are quantitatively examined by using an improved shift-share analysis method, and the spatial aggregation features of urbanisation, industrialisation and CEs are elucidated by constructing a three-dimensional (3D) contribution matrix model. The key conclusions are as follows: (1) The barycentre of industrialisation and urbanisation migrated to the southwest, while that of CE migrated to the northwest and the speed of this movement was faster than that of urbanisation and industrialisation. (2) Industrial transfer mainly occurred in 2005-2012, while the turning point of industrial upgrading appeared in 2013. Furthermore, midwestern regions mainly underwent industrial transfer, while northwest regions always showed CEs transfer from 2005 to 2016. (3) To achieve coordination among urbanisation, industrialisation and emission reductions, 30 provinces are grouped into 8 types according to the 3D contribution matrix model, and optimisation strategies are proposed to highlight regional disparities. These findings have significant implications for making informed decisions regarding urbanisation and industrialisation development as well as emissions-reduction policies.

Information concerning the factors affecting the circulation and distribution of free-roaming dogs is crucial in developing control actions and limiting the spread of zoonoses. The present study analyzes the influence of gender, sterilization, and environment on the spatial distribution of free-roaming dogs in urban settings. Animals were captured/recaptured in seven consecutive morning sampling efforts conducted at 2-monthly intervals in control and intervention areas in a medium-size town in southeastern Brazil. Capture locations were georeferenced and captured animals were microchipped before being released at their original capture sites. Dogs captured in the intervention area were subjected additionally to surgical sterilization prior to release. Home range (HR) areas were calculated by applying the minimum convex polygon method to dogs that had been captured at least three times. Land coverage zones were determined from satellite images and overlaid on maps of the study areas along with the locations of 22 commercial food outlets. HR areas showed a global mean of 448 m2 and a median of 28 m2, values that were smaller than those reported previously for dogs in rural regions. The median HR of females (64.m2) was higher than that of males (15 m2), while median HRs of animals in the control and intervention areas were similar (27 and 28.5 m2, respectively). Variability of HR was high, although animals with small HRs predominated. Free-roaming dogs grouped primarily in urbanized and transitional regions, and their spatial distribution was positively correlated with locations of commercial food outlets. While sterilization did not influence HR size, the search for food was a key factor in determining mobility and spatial aggregation of free-roaming dogs. Our findings are pertinent in understanding the ecology of free-roaming dogs in urban environments and will be applicable to strategies aimed at promoting animal welfare and preventing the dissemination of zoonoses.

Research Highlights: This study looks at poplar canker caused by Cytospora chrysosperma as a geographical phenomenon, and it applies spatial statistics to reveal the pattern and aggregation effects of the disease on a large scale in time and space. The incidence area of poplar canker in Northeast China has spatial (spatiotemporal) aggregation effects, which emphasize the importance of coordinated prevention. The results of spatial and spatiotemporal clusters can guide specific regional prevention and indicate the possible predisposing factors, respectively. Background and Objectives: Poplar canker, a harmful forest biological disease that is widespread throughout Northeast China, brings enormous ecological and economic losses. The limited cognition of its spatiotemporal pattern and aggregation effects restricts the decision-making for regional prevention and the identification of disease-inducing conditions. This study aims to explore the spatiotemporal pattern and to detect the aggregation effects of the disease, trying to provide references for prevention. Materials and Methods: According to the incidence data of poplar canker reported by each county in Northeast China from 2002 to 2015, we mapped the distribution of the incidence rate in ArcGIS and performed retrospective scan statistics in SaTScan to detect the spatial and spatiotemporal aggregation effects of the incidence area. Results: The spatiotemporal pattern of poplar canker&rsquo, s incidence rate presents the characteristic of &ldquo, outbreak-aggregation-spread-stability.&rdquo, The incidence area of the disease when we performed spatial aggregation scan statistics showed the primary cluster covering Liaoning province (LLR = 86469.86, p &lt, 0.001). The annual spatial scan statistics detected a total of 14 primary clusters and 37 secondary clusters, indicating three phases of aggregation. The incidence area of disease also shows spatiotemporal aggregation effects with the primary cluster located around Liaoning province, appearing from 2009 to 2015 (LLR = 64182.00, p &lt, 0.001). Conclusions: The incidence area of poplar canker presents significant characteristics of spatial and spatiotemporal aggregation, and we suggest attaching importance to the clues provided by the aggregation effects in disease prevention and identification of predisposing factors.

Spatial differentiation in forest transition was measured in terms of space transition and function transition using the exploratory spatial data analysis method (ESDA) and data from 2004—2014 for Zunyi city, Guizhou province, China. The validity of factors affecting forest transition was analyzed by constructing radial basis function neural networks (RBFNN) based on the data processing system (DPS). Our results will provide references for scientific understanding of the potential mechanism underlying forest transition in mountainous areas. We found that Global Moran's / of space transition and function transition of forest land was 0.0336 and 0.2323, respectively. This suggests a significant positive correlation in spatial distribution of space transition and function transition of forest land, and significant spatial aggregation. The Global Moran's / of function transition was higher than that of space transition, and the spatial aggregation characteristics of function transition were more significant than for space transition. The Global Moran's / at each time period tended to increase, and the spatial aggregation degree of the function transition and space transition was further enhanced. Hot and cold spots of space transition of forest land stably evolved, suggesting a minor spatial difference in forest land among different administrative units at the county level. The number of hot spots at the county level in function transition increased. Hot spots were intensively distributed at the western edge and continuously distributed in the northeast. The space transition and function transition of forest land were both greatly influenced by urbanization rate and second and third industries. The development of urbanization and industrialization was the main factor driving forest transition, suggesting a positive role of economic growth on forest transition in mountainous areas. The development of urbanization and industrialization is an effective approach to forest transition in mountainous areas.

Urban green spaces (UGS) are vital for developing and evolving natural elements in urban spaces. The UGS information of a megacity behemoth (Shanghai) from 1996, 2006, and 2016, and a small and medium-sized city (Xuchang) from 2010, 2015, and 2019 was used as the basic data. The UGS evolution spatiotemporal characteristics were analyzed using the area change index, spatial morphological dimension, and spatial aggregation dimension. The changes of greening policies for UGS evolution in the two cities were compared. The results indicated that: (1) the UGS in downtown Shanghai mainly developed southwards from 1996 to 2006, then northeastwards and northwestwards. The UGS in downtown Xuchang mainly developed northeastwards and northwards between 2010 and 2019; (2) the spatial morphological dimensions of UGS in downtown Shanghai gradually increased indicating the UGS had systematic coordination and stability. The spatial morphological dimensions of UGS in downtown Xuchang first increased then decreased, indicating that the UGS tended to be integrated and systematic; (3) the spatial aggregation dimensions of UGS in downtown Shanghai decreased first and then increased, and the focus of UGS construction shifted from the center to the suburbs. The spatial aggregation dimensions of UGS in downtown Xuchang gradually increased, and the spatial distribution of UGS tended to be multi-center clustered; and, (4) under the guidance of greening policies, UGS evolution in Shanghai was progressive with stronger self-organization ability, while that in Xuchang leaped forward with weaker self-organization ability. The research results can provide a reference for other similar sized cities in UGS planning and government decision-making.

A pandemia causada pelo coronavírus disparou a possibilidade da crise em diversos segmentos, sobretudo com a adoção do isolamento social, principal ação para o controle da doença infectocontagiosa. O presente estudo justifica-se pela preocupação em relação às tendências do desenvolvimento sustentável no pós-pandemia da COVID-19, no estado do Rio Grande do Sul. A mensuração do grau de sustentabilidade dos municípios segue a tese de que, quanto maior for o grau de sustentabilidade dos municípios, menor o impacto da pandemia, e maiores os recursos para restabelecer o equilíbrio. O objetivo da pesquisa é analisar a distribuição espacial do potencial para a sustentabilidade no pós-pandemia do COVID-19, nos municípios do Rio Grande do Sul, a fim de gerar um mapa de risco sobre os municípios com menor e maior potencial para a sustentabilidade no pós-pandemia. Foi realizada uma análise ecológica, aplicado o instrumento Barômetro da Sustentabilidade, realizada a distribuição e análise espacial, utilizando o cálculo do Índice de Moran. Os resultados apresentam a distribuição e análise espacial do número de casos confirmados, coeficiente de incidência e quantitativo de óbitos pela COVID-19 no Estado. A espacialidade pode explicar o coeficiente de incidência da doença. Foi estimado o potencial para a sustentabilidade e gerado o mapa de risco do potencial para a sustentabilidade, no pós-pandemia do COVID-19, para os municípios do Rio Grande do Sul. Spatial analysis and distribution of the potential for sustainability in the post-pandemic of COVID-19 in the municipalities of Rio Grande do Sul A B S T R A C TThe pandemic caused by the coronavirus triggered the possibility of the crisis in several segments, especially with the adoption of social isolation, the main action for the control of the infectious disease, therefore, the present study is justified by the concern in relation to the trends of sustainable development in the post-pandemic of COVID-19, in the state of Rio Grande do Sul. Measuring the degree of sustainability of the municipalities follows the thesis that the greater the degree of sustainability of the municipalities, the less the impact of the pandemic, and the greater the resources to restore balance. The objective of the research is to analyze the spatial distribution of the potential for sustainability in the post-pandemic of COVID-19, in the municipalities of Rio Grande do Sul, in order to generate a risk map with less and greater potential for sustainability in the post- pandemic. This study was carried out by means of an ecological analysis and application of the Barometer of sustainability, later the distribution and spatial analysis was carried out by means of the Moran Index. The spatial analysis, performed by calculating the Moran Index, showed significant spatial independence for confirmed cases (I = 0.058; p = 0.024) and deaths (I = 0.032; p = 0.039), and a significant, albeit weak, correlation. for the incidence coefficient (I = 0.234; p = 0.001) of COVID-19. Spatiality does not explain the distribution of cases and deaths. However, when taking into account the population size of the municipalities, in relation to the number of cases, expressed by the incidence coefficient, the spatial aggregation gains merit. The potential for sustainability was estimated and the risk map of the potential for sustainability was generated, in the post-pandemic of COVID-19, for the municipalities of Rio Grande do Sul.Keywords: Moran index; Sustainability Barometer; Sustainability Index; coronavirus; Pandemic.

To analyse the provincial differences in terms of China’s population, this chapter analyses the elderly dependency ratio from 2002 to 2018. Then, the values of Moran’s I index are calculated, and LISA analysis is conducted to analyse the spatial aggregation of China’s provinces. Furthermore, this chapter uses a spatial econometric model to analyse the influencing factors affecting the elderly dependency ratio. The results show that China’s elderly dependency ratio is becoming serious. Over time, the provincial differences in terms of the elderly dependency ratio slightly increase, but the spatial aggregation effect of China’s provinces decreases. During the research period, the provinces with a high-high pattern shift from the eastern provinces to the central provinces, and the provinces with a low-low pattern continuously compress into the north-west provinces. The results of the spatial econometric model reveal that the increases in the urbanization rate and the population size increase the elderly dependency ratio and that household consumption expenditure reduces the elderly dependency ratio.

After analysing the obvious provincial population differences, this chapter studies the degree of correlation between the implementation time of the selective two-child policy and the necessity of family planning policy adjustment, which is calculated in Chap. 7, and conducts spatial aggregation analysis. Then, this chapter uses the provincial population number as the weight to calculate the overall degree of rationality of implementing the selective two-child policy. The results show that Shanghai Province suitably implements the selective two-child policy with the highest degree of correlation and that Jiangxi Province improperly implements the family planning policy with the lowest degree of correlation. When considering all the influencing factors, the degrees of correlation of Shanghai Province and Jiangxi Province are 0.93709 and 0.31425, respectively. When considering only the population-related influencing factors, the degrees of correlation of Shanghai Province and Jiangxi Province are 0.95368 and 0.35088, respectively. In the spatial aggregation analysis, the north-east provinces have relatively high degrees of correlation. When adjusting their family planning policies, most provinces have considered their population factors but have overlooked their economic and social factors. When considering all the influencing factors or only the population-related influencing factors, the overall degrees of correlation for China as a whole are 0.64207 and 0.70159, respectively. The overall score is relatively high but still has high room for improvement. In the future, the entire public should continue to pay attention to the provincial population differences when adjusting the family planning policy.

Although many studies have revealed that both air quality and walking activity are dominant contributors to public health, little is known about the relationship between them. Moreover, previous studies on this subject have given little consideration to the day-to-day atmospheric conditions and floating populations of surrounding areas even though most pedestrian count surveys are not conducted on a single day. Against this backdrop, using the 2015 Pedestrian Volume Survey data and quasi-real-time weather, air quality, and transit ridership data in Seoul, this study investigates the relationship between particulate matter (PM)10 and pedestrian street volumes empirically. The regression results suggest that PM10 concentration determines people&rsquo, s intention to walk and affects the volume of street-level pedestrians. The three regression models, which adopted different spatial aggregation units of air quality, demonstrated that PM10 elasticity of pedestrian volume is the largest in the borough-level (the smallest spatial unit of air quality alert) model. This means that people react to the most accurate information they can access, implying that air quality information should be provided in smaller spatial units for public health. Thus, strengthening air quality warning standards of PM is an effective measure for enhancing public health.

Significant increase in the frequency of occurrences of rainfall extremes has been reported over several parts of the world. These extreme events were defined at individual grids without considering their spatial extent. Here, using ground-based observations over India during boreal summer, we show that the average size of spatially collocated rainfall extremes has been significantly increasing since 1980. However, the frequency of occurrences of such collocated extreme events remains unchanged. Around 90% of the total number of large-sized events (area ≥ 70 × 103 km2) of our study period (1951 to 2015) have occurred after 1980. Some of the major floods in recent decades over India are attributed to these large events. These events have distinctive precursory planetary-scale conditions, unlike their smaller counterparts. As the underlying physical mechanisms of extremes rainfall events are size-dependent, their changing spatial extent needs to be considered to understand the observed trends correctly and obtain realistic future projections.

This study aims to describe the spatial and temporal characteristics of human infections with H7N9 virus in China using data from 19 February 2013 to 30 September 2017 extracted from Centre for Health Protection of the Department of Health (CHP) and electronic databases managed by China&rsquo, s Center for Disease Control (CDC) and provincial CDCs synthetically using the Geographic Information System (GIS) software ArcMap&trade, 10.2 and SaTScan. Based on the multiple analyses of the A(H7N9) epidemics, there was a strong seasonal pattern in A(H7N9) virus infection, with high activity in the first quarter of the year, especially in January, February, and April, and a gradual dying out in the third quarter. Spatial distribution analysis indicated that Eastern China contained the most severely affected areas, such as Zhejiang Province, and the distribution shifted from coastline areas to more inland areas over time. In addition, the cases exhibited local spatial aggregation, with high-risk areas most found in the southeast coastal regions of China. Shanghai, Jiangsu, Zhejiang, and Guangdong were the high-risk epidemic areas, which should arouse the attention of local governments. A strong cluster from 9 April 2017 to 24 June 2017 was also identified in Northern China, and there were many secondary clusters in Eastern and Southern China, especially in Zhejiang, Fujian, Jiangsu, and Guangdong Provinces. Our results suggested that the spatial-temporal clustering of H7N9 in China is fundamentally different, and is expected to contribute to accumulating knowledge on the changing temporal patterns and spatial dissemination during the fifth epidemic and provide data to enable adequate preparation against the next epidemic.

Transport-related social exclusion is a complex issue. It can be studied from a variety of angles, be influenced by a number of factors, and affect diverse population groups. This study investigates transport-related social exclusion from a multi-dimensional view. Transport inequity was measured based on different development stages of a region using the Lorenz Curve and Gini index, and compared socio-economic characteristics, such as housing affordability, employment self-sufficiency, urban sprawl, and transport-mode share at different degrees of spatial aggregation. Two hierarchical spatial aggregation levels are used: (1) Sydney – Perth; (2) Inner – Middle – Outer sectors. Spatial gaps of transport-related social exclusion are identified for both cities and a number of policy implications are considered to provide suggestions to improve transport-related social inclusion in both cities.

Like studies of craft production, research on food production has significant implications for understanding the economic organization of past societies. This paper highlights the tools of maize (Zea mays) processing and cooking as a constructive avenue to examine aspects of food production at Teotihuacan during the Terminal Formative and Classic Periods (100 BC–AD 650). By analyzing the spatial distribution of comales, metates and manos recovered from the Teotihuacan Mapping Project, this study investigates the notion that edible maize products were produced through specialized labor. Two notable patterns are observed. First, the aggregation of comales along the Street of the Dead during the Miccaotli phase (AD 100–170) suggests that tortilla preparation fueled large public work projects, feeding the workers, their families, and other dependents of the state. Secondly, the spatial aggregation of grinding stones, manos and comales in seven concentrated areas within the Classic Period (AD 170–650) occupation of the city suggest that several apartment compounds, possibly entire barrios, may have specialized in the production of tortillas and other finished maize foods for exchange. Such a pattern may have resulted from the costly labor and materials required for the production of tortillas, favoring economies of scale. Several areas within the city conspicuously possessing low frequencies of maize processing artifacts suggest that some sectors were dependent on specialized labor from outside the household for daily needs. Together, these data suggest that food production was a specialized activity at Teotihuacan and increase our understanding of the economic organization of the ancient metropolis.

The increasing availability of telemetry data with high spatial and temporal resolution promises to greatly advance scientific understandings of the movement patterns of individual organisms across space and time. The amount of data provided by such methods, however, can be challenging to analyze and interpret. In this study, we present a new approach for analyzing animal movements that aggregates telemetry locations into spatial clusters and extracts the information from sequences formed by individuals passing through these spatial clusters. We applied this integrated approach of spatial aggregation and sequence analysis to quantify and compare trajectories of cattle (Bos taurus), mule deer (Odocoileus hemionus), and elk (Cervus elaphus) tracked by automated telemetry at the Starkey Experimental Forest and Range in northeastern Oregon, USA. Our approach effectively differentiated movement patterns of the three species. It provides a useful mean of quantifying movement patterns of species in a landscape.

The deep-water rose shrimp Parapenaeus longirostris (DPS), a demersal decapod representing the most important component of crustacean landings in the Mediterranean Sea, has been suggested as a species that may exhibit temperature-driven changes in the spatio-temporal dynamics. Considering that Mediterranean waters are warming up faster than oceans, understanding the relationships of DPS populations with temperature variations and the related changes in spatial patterns is absolutely key for its management. Using a long-term dataset covering 13-years from scientific surveys (International Bottom Trawl-Surveys in the Mediterranean, MEDITS; Italian national trawl surveys, GRUND) in the Strait of Sicily (central Mediterranean Sea), the annual DPS spatial patches and the depth distribution were investigated using geostatistical and quotient analyses. The patches dimension and depth range occupation were then related to sea temperature by using linear regression analysis. Results showed that both the dimension of DPS spatial patches and the depth distribution range occupied increased concurrently with temperature. Our findings corroborate that the ongoing sea warming widen areas suitable for this species and in which it can expand its spatial distribution.

At present, the number of people with tuberculosis in China is second only to India and ranks second in the world. Under such a severe case of tuberculosis in China, prevention and control of pulmonary tuberculosis are urgently needed. This study aimed to study the temporal and geographical relevance of the pathogenesis of pulmonary tuberculosis and the factors affecting the incidence of tuberculosis. Spatial autocorrelation model was used to study the spatial distribution characteristics of pulmonary tuberculosis from a quantitative level. The research results showed that the overall incidence of pulmonary tuberculosis (IPT) in China was low in the east, high in the west and had certain seasonal characteristics. We use Spatial Lag Model to explore influencing factors of pulmonary tuberculosis. It indicates that the IPT is high in areas with underdeveloped economics, poor social services and low average smoking ages. Additionally, the IPT is high in areas with high AIDS prevalence. Also, compared with Classical Regression Model and Spatial Error Model, our model has smaller values of Akaike information criterion and Schwarz criterion. Besides, our model has bigger values of coefficient of determination (R2) and log-likelihood (log L) than the other two models. Apart from that, it is more significant than Spatial Error Models in the spatial dependence test for the IPT.

Background: Temporal incidence patterns provide a crucial window into the dynamics of emerging infectious diseases, yet their utility is limited by the spatially aggregated form in which they are often presented. Weekly incidence data from the 2015-2016 Zika epidemic were available only at the national level for most countries in the Americas. One exception was Colombia, where data at departmental and municipal scales were made publicly available in real time, providing an opportunity to assess the degree to which national-level data are reflective of temporal patterns at local levels. Methods: To characterize differences in epidemic trajectories, our analysis centered on classifying proportional cumulative incidence curves according to six features at three levels of spatial aggregation. This analysis used the partitioning around medoids algorithm to assign departments and municipalities to groups based on these six characteristics. Examination of the features that differentiated these groups and exploration of their temporal and spatial patterns were performed. Simulations from a stochastic transmission model provided data that were used to assess the extent to which groups identified by the classification algorithm could be associated with differences in underlying drivers of transmission. Results: The timing of departmental-level epidemic peaks varied by three months, and departmental-level estimates of the time-varying reproduction number, R(t), showed patterns that were distinct from a national-level estimate. The classification algorithm identified moderate support for two to three clusters at the departmental level and somewhat stronger support for this at the municipal level. Variability in epidemic duration, the length of the tail of the epidemic, and the consistency of cumulative incidence data with a cumulative normal distribution function made the greatest contributions to distinctions across these groups. Applying the classification algorithm to simulated data showed that municipalities with basic reproduction number, R0, greater than 1 were consistently associated with a particular group. Municipalities with R0 < 1 displayed more diverse patterns, although in this case that may be due to simplifications of how the model represented spatial interaction among municipalities. Conclusions: The diversity of temporal incidence patterns at local scales uncovered by this analysis underscores the value of spatially disaggregated data and the importance of locally tailored strategies for responding to emerging infectious diseases.

To estimate species loss from habitat destruction, ecologists typically use species–area relationships, but this approach neglects the spatial pattern of habitat fragmentation. Here, we provide new, easily applied, analytical methods that place upper and lower bounds on immediate species loss at any spatial scale and for any spatial pattern of habitat loss. Our formulas are expressed in terms of what we name the ‘Preston function’, which describes triphasic species–area relationships for contiguous regions. We apply our method to case studies of deforestation and tropical tree species loss at three different scales: a 50 ha forest plot in Panama, the tropical city‐state of Singapore and the Brazilian Amazon. Our results show that immediate species loss is somewhat insensitive to fragmentation pattern at small scales but highly sensitive at larger scales: predicted species loss in the Amazon varies by a factor of 16 across different spatial structures of habitat loss.

Obesity and other chronic diseases are becoming more prevalent in affluent countries such as Australia. Researchers are trying to understand and combat this trend. One related growing stream of research explores the role of the built environment and transport system on an individual’s weight. However, results from many studies conducted have been contradictory. A primary cause of these contradictions is due to how neighborhood areas are defined, which directly affects how the built environment variables are calculated in geographic information systems. The potential impacts on regression analysis resulting from different data aggregation methods are well documented in spatial studies, geography, and regional planning fields, and the problem is primarily referred to as the modifiable aerial unit problem. In this paper, the focus is on reducing the error caused by the modifiable aerial unit problem by introducing a new data aggregation method. Individual health and lifestyle data are obtained from the survey of households, income, and labor dynamics in Australia, and the relationship between the built environment and obesity is evaluated by using a discrete choice model. The proposed aggregation method is evaluated across three spatial scales and compared against a conventional data aggregation method (i.e., using predefined administrative boundaries such as census tracts). The results reveal a stronger relationship between land use variables and obesity when the proposed aggregation method is implemented. This paper is relevant primarily to researchers because it provides an improved aggregation method to deal with some privacy restrictions of surveys. It is also relevant to practitioners and policy makers by its quantification of the association between specific built environment variables and obesity.

Globally, crop diseases result in significant losses in crop yields. To properly target interventions to control crop diseases, it is important to map diseases at a high resolution. However, many surveys of crop diseases pose challenges to mapping because available observations are only proxies of the actual disease, observations often are not normally distributed and because typically convenience sampling is applied, leading to spatially clustered observations and large areas without observations. This paper addresses these challenges by applying a geostatistical methodology for disease incidence mapping. The methodology is illustrated for the case of bacterial wilt of banana (BWB) caused by Xanthomonas campestris pv. musacearum in the East African highlands. In a survey using convenience sampling, 1350 banana producing farmers were asked to estimate the percentage yield loss due to bacterial wilt. To deal with the non-normal distribution of the data, the percentages were classified into two binary variables, indicating whether or not the disease occurred and whether or not the yield loss was severe. To improve the spatial prediction of disease incidence in areas with low sampling density, the target variables were correlated in a logistic regression to a range of environmental variables, for which maps were available. Subsequently, the residuals of the regression analysis were interpolated using simple kriging. Finally, the interpolated residuals were added to the regression predictions. This so-called indicator regression kriging approach resulted in continuous maps of disease incidence. Cross-validation showed that the method yields unbiased predictions and correctly assesses the prediction accuracy. The geostatistical mapping is also more accurate than conventional mapping, which uses the mean of observations within districts as the predicted value for all locations within the district, although the accuracy improvement is not very large. The maps were also spatially aggregated to district level to support regional decision-making. The analysis showed that the disease is widespread on banana farms throughout the study area and can locally reach severe levels.

We evaluate Operation IceBridge (OIB) ‘quick-look’ (QL) snow depth on sea ice retrievals using in situ measurements taken over immobile first-year ice (FYI) and multi-year ice (MYI) during March of 2014. Good agreement was found over undeformed FYI (-4.5 cm mean bias) with reduced agreement over deformed FYI (-6.6 cm mean bias). Over MYI, the mean bias was -5.7 cm but 54% of retrievals were discarded by the OIB retrieval process as compared to only 10% over FYI. Footprint scale analysis revealed a root mean square error (RMSE) of 6.2 cm over undeformed FYI with RMSE of 10.5 cm and 17.5 cm in the more complex deformed FYI and MYI environments. Correlation analysis was used to demonstrate contrasting retrieval uncertainty associated with spatial aggregation and ice surface roughness.

Land use and different scales of spatial data aggregation are important for integrated urban water modelling. Inadequacy of land use classification in such models, however, prompted development of a new water-centric classification. A thirteen-category system (considering land cover, utilisation, and urban zoning aspects) was devised and applied to Melbourne, Australia. Sensitivity of the classification to spatial aggregation was tested by analysing randomly sampled subsets of Melbourne's urban fabric for variation in landscape patterns, land use dominance and fragmentation. Significant differences were found when comparing inner city (highly fragmented, dominated by many land use categories) to fringes, (only few dominant categories e.g. residential, reserves), which are challenging to accurately represent in urban water models if oversimplified. The new classification encompasses key integrated urban water management aspects, but does not capture local-scale features of the urban environment. Significant shortcomings for using spatial aggregation methods to simplify data for urban water models were found. Solely focussing on spatial coverage of land use instead of land use mix neglects important water interactions between categories (e.g. open space and residential). Trade-offs between spatial resolution and computational efficiency, for example, can degrade accuracy of representing urban water characteristics as less-dominant land uses are systematically removed. Finally, spatial aggregation methods likely result in the loss of minor (yet important) land uses (e.g. Civic). More effective methods for representing the variability of urban environments, which depart from simple aggregated rasters and sole reliance on land use, may be required to better define urban complexity in our water models.

The purpose of this chapter is to describe a number of methodological issues, considerations and opportunities when applying phylogeny-based measures at regional to global scales, a process recently labelled as “spatial phylogenetics”. These are divided into four sections: (1) the nature of the source data; (2) issues and effects of spatial aggregation; (3) some useful properties of phylogenetic diversity, endemism and rarity across landscapes; and (4) how to develop analysis tools that will complete in reasonable time for large data sets. The focus of the discussion is on measures of phylogenetic diversity, endemism and rarity, but the issues, considerations and opportunities raised have general application to other related indices across landscape and global scales.

Knowing the spatial scales at which effective management can be implemented is fundamental for conservation planning. This is especially important for mobile species, which can be exposed to threats across large areas, but the space use requirements of different species can vary to an extent that might render some management approaches inefficient. Here the space use patterns of seabirds were examined to provide guidance on whether conservation management approaches should be tailored for taxonomic groups with different movement characteristics. Seabird tracking data were synthesised from 5419 adult breeding individuals of 52 species in ten families that were collected in the Atlantic Ocean basin between 1998 and 2017. Two key aspects of spatial distribution were quantified, namely how far seabirds ranged from their colony, and to what extent individuals from the same colony used the same areas at sea. There was evidence for substantial differences in patterns of space-use among the ten studied seabird families, indicating that several alternative conservation management approaches are needed. Several species exhibited large foraging ranges and little aggregation at sea, indicating that area-based conservation solutions would have to be extremely large to adequately protect such species. The results highlight that short-ranging and aggregating species such as cormorants, auks, some penguins, and gulls would benefit from conservation approaches at relatively small spatial scales during their breeding season. However, improved regulation of fisheries, bycatch, pollution and other threats over large spatial scales will be needed for wide-ranging and dispersed species such as albatrosses, petrels, storm petrels and frigatebirds. Fil: Oppel, Steffen. Royal Society For The Protection Of Birds; Fil: Bolton, Mark. Royal Society For The Protection Of Birds; Fil: Carneiro, Ana P.B.. Birdlife International; Fil: Dias, Maria P.. Birdlife International; Fil: Green, Jonathan A.. University of Liverpool; Reino Unido Fil: Masello, Juan F.. Justus Liebig University Giessen; Fil: Phillips, Richard A.. British Antarctic Survey; Fil: Owen, Ellie. Royal Society For The Protection Of Birds; Fil: Quillfeldt, Petra. Justus Liebig University Giessen; Fil: Beard, Annalea. Environmental And Natural Resources Directorate; Fil: Bertrand, Sophie. Universidad Federal Rural Pernambuco; Brasil. Centre National de la Recherche Scientifique. Institut de Recherche pour le Développement; Francia Fil: Blackburn, Jez. British Trust For Ornithology; Fil: Boersma, P. Dee. University Of Washington, Seattle; Fil: Borges, Alder. Projecto Vitó; Fil: Broderick, Annette C.. University of Exeter; Reino Unido Fil: Catry, Paulo. Ispa - Instituto Universitário; Portugal Fil: Cleasby, Ian. Royal Society For The Protection Of Birds; Fil: Clingham, Elizabeth. Environmental And Natural Resources Directorate; Fil: Creuwels, Jeroen. Naturalis Biodiversity Center; Fil: Crofts, Sarah. Falklands Conservation; Fil: Cuthbert, Richard J.. Royal Society For The Protection Of Birds; Fil: Dallmeijer, Hanneke. Veda Consultancy; Fil: Davies, Delia. University Of Cape Town; Fil: Davies, Rachel. Marinelife; Fil: Dilley, Ben J.. University Of Cape Town; Fil: Dinis, Herculano Andrade. Parque Natural Do Fogo; Fil: Dossa, Justine. Birdlife International; Fil: Dunn, Michael J.. British Antarctic Survey; Fil: Efe, Marcio A.. Universidade Federal de Alagoas; Brasil Fil: Fayet, Annette L.. University of Oxford; Reino Unido Fil: Figueiredo, Leila. Universidade Federal de Alagoas; Brasil Fil: Frederico, Adelcides Pereira. Projecto Vitó; Fil: Gjerdrum, Carina. Environment Canada; Canadá Fil: Godley, Brendan J.. University of Exeter; Reino Unido Fil: Granadeiro, José Pedro. Faculdade de Ciências, Universidade de Lisboa; Portugal Fil: Guilford, Tim. University of Oxford; Reino Unido Fil: Hamer, Keith C.. University Of Leeds; Fil: Hazin, Carolina. Birdlife International; Fil: Hedd, April. Environment Canada; Canadá Fil: Henry, Leeann. Environmental And Natural Resources Directorate; Fil: Hernández-Montero, Marcos. Projeto Biodiversidade; Fil: Hinke, Jefferson. Noaa Fisheries Service; Fil: Kokubun, Nobuo. National Institute Of Polar Research; Fil: Leat, Eliza. Ascension Island Government; Fil: Tranquilla, Laura McFarlane. Royal Society For The Protection Of Birds; . Environmental And Natural Resources Directorate; Fil: Metzger, Benjamin. Birdlife Malta; Malta Fil: Militão, Teresa. Universidad de Barcelona; España Fil: Montrond, Gilson. Projecto Vitó; Fil: Mullié, Wim. Veda Consultancy; Fil: Padget, Oliver. University of Oxford; Reino Unido Fil: Pearmain, Elizabeth J.. Birdlife International; Fil: Pollet, Ingrid L.. Bird Studies Canada; Canadá Fil: Pütz, Klemens. Antarctic Research Trust; Fil: Quintana, Flavio Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Biología de Organismos Marinos; Argentina Fil: Ratcliffe, Norman. British Antarctic Survey; Fil: Ronconi, Robert A.. Environment Canada; Canadá Fil: Ryan, Peter G.. University Of Cape Town; Fil: Saldanha, Sarah. Universidad de Barcelona; España Fil: Shoji, Akiko. University of Oxford; Reino Unido Fil: Sim, Jolene. Ascension Island Government; Fil: Small, Cleo. Royal Society For The Protection Of Birds; Fil: Soanes, Louise. University of Liverpool; Reino Unido Fil: Takahashi, Akinori. National Institute Of Polar Research; Fil: Trathan, Phil. British Antarctic Survey; Fil: Trivelpiece, Wayne. Noaa Fisheries Service; Fil: Veen, Jan. Veda Consultancy; Fil: Wakefield, Ewan. University of Glasgow; Reino Unido Fil: Weber, Nicola. University of Exeter; Reino Unido. Ascension Island Government; Fil: Weber, Sam. Ascension Island Government; . University of Exeter; Reino Unido Fil: Zango, Laura. Universidad de Barcelona; España Fil: González-Solís, Jacob. Universidad de Barcelona; España Fil: Croxall, John. Birdlife International

Spatial clustering analysis is one kind of spatial data mining tools to explore the spatial auto-correlation of things that occur in a particular space, that is, whether the observed values of the spatial variables are related to the spatial position where they occur. In this paper, the cigarettes in 2013 in Guizhou province, China were collected, and the spatial correlation analysis was carried out on the five categories of cigarettes sales in county-level area in this Province. Some valuable spatial aggregation and distribution characteristic of Guizhou cigarette sales were mined out. Experiments and experimental results show that the kind of spatial pattern diming tool is a potential tool for providing valuable spatial reference for Cigarette sales policy development and the progressive realization of the precise cigarettes marketing in this province.

A new accessibility index is designed by using the resident population weighted average value of the inverse of the traveling time distance to analyze the spatial-temporal characteristics of Henan city accessibilities and its evolution patterns from 2004 to 2016.The results show that as the expanding and perfecting of Henan highway traffic network, the city accessibility levels have been effectively improved, and produced obvious spatial convergence effect. The spatial distribution pattern of city accessibilities show more significant trend with time, the city accessibility levels gradually weaken along the northwest-southeast direction, are relatively balanced on the east-west direction. The evolution characteristics have Obviously differences in the different years. The optimization degree in 2004-2008 is obviously higher than that of in 2008-2016. The degrees of improvement of the periphery cities are always higher than that of the other cities, the overall standard deviation gradually becomes larger. There is some non-coordination behaviors are appearing between the growth rates of the highway mileage and the growth rates of the city accessibility. The aggregation characteristics of city accessibility are gradually weakening, the aggregation area gradually decreases. The spatial distribution of city accessibilities and population gradually tend to be harmonious

We explored the spatial dynamics of structural complexity in the living tree stratum in a 10-ha stem-mapped portion of an unmanaged nearly monospecific primaeval European beech (Fagus sylvatica L.) stand in Western Ukraine. Development dynamics were assessed through patterns of change in association across scales (from 156.25 m2 to 1 ha) among stand basal area (BA), tree density, average and standard deviation (STD) of tree diameters, Gini coefficient (GC), the index of spatial aggregation (R), diameter differentiation index (T) and structural complexity index (SCI). At the smallest scales, STD, GC and T contrasted patches of differing structure (i.e. large between-patch structural differences). As subplot area increased and incorporated more heterogeneity, structural differences between subplots became more subtle and measures of tree-to-tree size variation (STD, T) lost sensitivity whereas it was gained for measures of overall within-patch heterogeneity (GC). At small scales, differences in STD largely explained variation in the SCI (between-plot variability); at intermediate scales, size differences among neighbours (T) explained most of the variability; and at large scales, plot-level differences in BA and its allocation to trees of different sizes (GC; within-plot variability) overrode size differences among nearest neighbours. The characterization of a fine-scale shifting mosaic of patches in different development stages appears to hold for primaeval beech forests in this spatially contiguous area of relatively large extent. The coalescence of small-scale processes into neighbourhoods, and then into patches at larger scales, may be best captured by the change in associations among structural measures across scales because the structural imprint of gap dynamics extends considerably beyond the scale of individual gaps

Based on the exploratory spatial data analysis (ESDA) and GIS technology, the spatial differences of the rural economic development level of Qinhuangdao city was investigated by adopting the rural resident’s per capita net income data at town level in Qinhuangdao city from 2007 to 2011. The results of global Moran’s I value for rural resident’s per capita net income at town level showed that there existed significant positive spatial autocorrelation and significant spatial aggregation in the spatial distribution of rural resident’s per capita net income. However, the global Moran’s I value showed a decreasing trend during 2007 to 2011, indicating an enlarged spatial disparity of rural economy at the town level. The results of the Moran scatter plots and LISA cluster maps of 2007 and 2011 showed that most of towns were characterized by positive local spatial association , ie. They were located in the HH or the LL quadrant. The significant HH towns were mostly to be found in the south of Qinhuangdao city, Haigang district, Changli county, Lulong county. The significant LL towns were mostly to be found in the Qinglong county, north of Qinhuangdao city.

This study presents an analysis of selective harvest events in three 10-ha field plots with mapped trees, representing different forest successional stages in the temperate multi-species forests of North-Eastern China. Non-spatial methods of analyzing the harvest events include species and size selection preference and an assessment of harvest intensities for four species groups (identified using bivariate mixed dbh/height distributions). Spatial aggregation increased very slightly after the harvest event, but in most cases, the change was hardly noticeable. In addition, tree selection preferences involving nearest neighbor structure units are presented, using the attributes “species mingling” and “dominance”. The removals occurred within a broad array of neighborhood constellations, involving suppressed as well as dominant individuals. Previous approaches involving harvest event analysis in multi-species forests were limited to assessing size and species selection preferences. This study uses more advanced methods and presents more detailed interpretations, due to the large and detailed observational datasets and improved analytical tools that have become available recently. Models of tree growth and survival, which represent the overwhelming result of traditional observational studies, only describe a part of forest dynamics. Equally important are the modifications caused by regular human disturbance. There is thus increasing motivation for analyzing selective harvesting activities as presented in this contribution.

Based on the panel data of 30 provinces and cities in China from 2008 to 2017, this paper empirically analyzes the current situation of the development of China&rsquo, s sports industry and the impact of the concentration of sports industry on green economic growth by using the exploratory spatial data analysis method (ESDA), static panel model and dynamic panel model. The empirical results show that the sports industry in the southeast coastal provinces of China has a good foundation for concentration. The sports industry concentration in the provinces along the Yangtze River Economic Belt is in the stage of rapid development. The development of sports industry concentration has obvious positive spatial aggregation characteristics, and the spatial spillover effect is obvious, The concentration of sports industry has a significant influence on green economic growth, which expresses the &ldquo, inverted U-type&rdquo, relationship. There are significant differences in influence between regions, which show the greatest influence in the middle, followed by the east and the smallest in the west.

As a part of cultural landscapes, toponyms with abundant cultural connotations and a long history are valuable cultural heritage assets. Choronyms not only reflect natural and social phenomena but also help with relevant management and naming work. In order to explore the historical development sequence of choronymic cultural landscape evolution, we analyze the spatial&ndash, temporal pattern evolution, spatial&ndash, temporal variation, spatial association, and semantic evolution of choronymic cultural landscapes since the Qin dynasty. We adopt the sequent snapshot model and an event-based state amendment model to establish a spatio-temporal database. That can provide decision supports and theoretical reference for the sustainable development of toponymic landscapes. Results indicate the following: (1) Spatial distribution of toponym density has been different since the Qin dynasty. The cores of toponym density spread from the middle-lower reaches of the Yellow River to Yangtze Plain, Chengdu Plain, Pearl River Delta Plain. (2) Spatial distribution of choronyms is agglomerative since the Qin dynasty and uneven at national and provincial scales since the Yuan dynasty. Temporal distribution of toponyms at different levels is centralized. (3) Spatial agglomeration phenomena of toponyms are positively clustered in nine periods. The Ming dynasty presents the largest degree of spatial aggregation. (4) Words relevant to blessings, orientation, and hydrological features have high proportions in the top 20 words. (5) Spatial distribution of county-level choronyms named over the last 1000 years and &ldquo, Millennium Ancient Counties&rdquo, are unbalanced at national and provincial scales.

Using the technologies of GIS and RS for the perspective of landscape ecology, the characteristics of landscape ecological security changes of Chishui River Basin in Guizhou Province from 1995 to 2015 were analyzed. The results showed that: (1) In the past 20 years, the utilized land types in the study area were changed greatly. The disturbance of human factors on utilized land change was serious. The area of construction land was increased rapidly, the area of forest land was decreased, while the cultivated land increased first and then decreased. (2) In the past 20 years, the landscape fragmentation index and landscape separation index of other utilized land types were increased except the construction land. The fractal dimension indices of other landscape types were increased, but the change was not obvious. With the continuous development of economy and society, the disturbance of human activities to various landscape types was increased. (3) From the point of view of temporal and spatial changes of landscape ecological security, the overall situation of landscape ecological security in the study area was better, and the area with high ecological security was increased in the past 20 years. Less than 10% of the area was in low ecological security, but the overall trend was increasing, and the problem of ecological security was serious. (4) In the past 20 years, the spatial aggregation of towns with similar landscape ecological security in the study area decreased first and then increased, and the spatial aggregation types were low to low and high to high .

This study examined analyst variation associated with land cover (LC) image classification using 30 × 30 m Landsat ETM+ data for the assessment of coarse spatial resolution regional/global LC products. The study was designed to test the effect of varying training site selections (location and number) among six analysts performing a supervised classification on a Landsat ETM + image. Design constraints maintained other aspects of the classification process constant (i.e., type of classifier, choice of band combinations, etc.). Results indicated that training site selection alone did not provide a predictive measure of classification accuracy. Only when training data selection was combined with variations in spatial resolution did significant differences occur. Differences in classification accuracies between analysts increased threefold in the aggregation process from 90 × 90 m to 1200 × 1200 m. Error sources (i.e. analyst differences) and the dynamics of the spatial aggregation process can potentially acc...

Municipal fire departments responded to approximately 53,000 intentionally-set fires annually from 2003 to 2007, according to National Fire Protection Association figures. A disproportionate amount of these fires occur in spatio-temporal clusters, making them predictable and, perhaps, preventable. The objective of this research is to evaluate how the aggregation of data across space and target types (residential, non-residential, vehicle, outdoor and other) affects daily arson forecast accuracy for several target types of arson, and the ability to leverage information quantifying the autoregressive nature of intentional firesetting. To do this, we estimate, for the city of Detroit, Michigan, competing statistical models that differ in their ability to recognize potential temporal autoregressivity in the daily count of arson fires. Spatial units vary from Census tracts, police precincts, to citywide. We find that (1) the out-of-sample performance of prospective hotspot models for arson cannot usefully exploit the autoregressive properties of arson at fine spatial scales, even though autoregression is significant in-sample, hinting at a possible bias-variance tradeoff; (2) aggregation of arson across reported targets can yield a model that differs from by-target models; (3) spatial aggregation of data tends to increase forecast accuracy of arson due partly to the ability to account for temporally dynamic firesetting; and (4) arson forecast models that recognize temporal autoregression can be used to forecast daily arson fire activity at the Citywide scale in Detroit. These results suggest a tradeoff between the collection of high resolution spatial data and the use of more sophisticated modeling techniques that explicitly account for temporal correlation.

The Marine Strategy Framework Directive requires the environmental status of European marine waters to be assessed using biodiversity as 1 out of 11 descriptors, but the complexity of marine biodiversity and its large span across latitudinal and salinity gradients have been a challenge to the scientific community aiming to produce approaches for integrating information from a broad range of indicators. The Nested Environmental status Assessment Tool (NEAT), developed for the integrated assessment of the status of marine waters, was applied to 10 marine ecosystems to test its applicability and compare biodiversity assessments across the four European regional seas. We evaluate the assessment results as well as the assessment designs of the 10 cases, and how the assessment design, particularly the choices made regarding the area and indicator selection, affected the results. The results show that only 2 out of the 10 case study areas show more than 50% probability of being in good status in respect of biodiversity. No strong pattern among the ecosystem components across the case study areas could be detected, but marine mammals, birds, and benthic vegetation indicators tended to indicate poor status while zooplankton indicators indicated good status when included into the assessment. The analysis shows that the assessment design, including the selection of indicators, their target values, geographical resolution and habitats to be assessed, has potentially a high impact on the result, and the assessment structure needs to be understood in order to make an informed assessment. Moreover, recommendations are provided for the best practice of using NEAT for marine status assessments.

Delimitating trade areas is a major business concern. Today, mobile communication technologies make it possible to use social media data for this purpose. Few studies however, have focused on methods to extract suitable samples from social media data for trade area delimitation. In our case study, we divided Beijing into regular grid cells and extracted activity centers for each social media user. Ten sample sets were obtained by selecting users based on the retail agglomerations they visited and aggregating user activity centers to each grid cell. We calculated distance and visitation frequency attributes for each user and each grid cell. The distance value of a grid cell is the average distance of user activity centers in this grid cell to a retail agglomeration. The visitation frequency of a grid cell refers to the average count of visits to retail agglomerations by user activity centers for a cell. The calculated attribute values of 10 sets were input into a Huff model and the delimitated trade areas were evaluated. Results show that sets obtained by aggregating user activity centers have a better delimitating effect than sets obtained without aggregation. Differences in the distribution and intensity of trade areas also became apparent.

Tuna purse seine fisheries target fish aggregated in schools, including free schools that are formed naturally based on fish biology and aggregations associated with natural and/or artificial drifting objects. Using data collected from skipjack tuna (Katsuwonus pelamis) fisheries, we evaluated differences in size structures between drifting-floating-object-associated schools and unassociated schools. We developed a generalized linear model to remove impacts of environmental variables on skipjack size composition. This study indicates that the drifting-floating-object-associated schools tended to have significantly wider size ranges than the unassociated schools. This suggests that unassociated schools were likely formed based on similarity in sizes among individuals within a school while drifting-floating-object-associated schools were probably composed of individuals of large size ranges and their formation was not based on the “size selection” rule. We concluded that the unassociated schools and the drifting-floating-object-associated schools were formed through different mechanisms, and drifting floating objects could aggregate unassociated schools of different size structures. Thus, a large scale of deployment of man-made floating objects might disrupt the spatial aggregation pattern of fish that otherwise tended to school based on their sizes in the absence of floating objects.

The lack of data upon which to base private- and public-sector decisions is an important challenge in rural areas of the United States and other countries. Data suppression, done to protect the confidentiality of individual firms and people, compounds the more general paucity of rural economic data in the US. This article evaluates the use of linear programming for estimating suppressed values in the important US Census data series County Business Patterns (CBP). The full CBP dataset, enhanced with the estimates for suppressed values, was then used in shift-share analysis (SSA) at several levels of spatial aggregation. Finally, a publicly available website was developed to map the results of the SSA as well as the values of fifteen variables that may affect regional competitiveness. The overall result is a methodology for mining, analyzing, and visualizing rural economic data.

Summary Air quality indicators summarize overall concentrations of pollution for an urban area and are calculated from routine monitoring data comprising numerous pollutants measured at many locations. The indicator is constructed by aggregating these data over space and pollutants, typically using the sample mean, median or maximum. We propose an alternative approach based on geostatistical modelling, which allows intervals of uncertainty to be calculated for the spatial aggregation stage, and hence for the final indicator. We then extend our geostatistical model by allowing for the fact that the locations that are chosen for the pollution monitors may depend on the hypothesized concentrations at these locations, a phenomenon which is known as preferential sampling. We assess the effectiveness of our methods by simulation and use them to construct an air quality indicator for Greater London, England, for the month of August 2006.

Exposure data available to developers of earthquake loss models are often very crudely aggregated spatially, and in such cases very considerable effort can be required to refine the geographical resolution of the building stock inventory. The influence of the geographical resolution of the exposure data for the Sea of Marmara region in Turkey is explored using several different levels of spatial aggregation to estimate the losses due to a single earthquake scenario. The results show that the total damage over an urban area, expressed as a mean damage ratio (MDR), is rather insensitive to the spatial resolution of the exposure data if a sufficiently large number of ground-motion simulations are used. However, the variability of the MDR estimates does reduce as the spatial resolution becomes higher, reducing the number of simulations required, although there appears to be a law of diminishing returns in going to very high exposure data resolution. This is largely due to the inherent and irreducible spatial variability of ground motion, which suggests that if only mean MDR estimates are needed, the effort required to refine the spatial definition of exposure data is not justified.