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Semantic segmentation is crucial to the autonomous driving, as an accurate recognition and location of the surrounding scenes can be provided for the street scenes understanding task. Many existing segmentation networks usually fuse high‐level and low‐level features to boost segmentation performance. However, the simple fusion may impose a limited performance improvement because of the gap between high‐level and low‐level features. To alleviate this limitation, we respectively propose spatial aggregation and channel fusion to bridge the gap. Our implementation, inspired by the attention mechanism, consists of two steps: (1) Spatial aggregation relies on the proposed pyramid spatial context aggregation module to capture spatial similarities to enhance the spatial representation of high‐level features, which is more effective for the latter fusion. (2) Channel fusion relies on the proposed attention‐based channel fusion module to weight channel maps on different levels to enhance the fusion. In addition, the complete network with U‐shape structure is constructed. A series of ablation experiments are conducted to demonstrate the effectiveness of our designs, and the network achieves mIoU score of 81.4% on Cityscapes test dataset and 84.6% on PASCALVOC 2012 test dataset.

With the development of the Internet of Things, millions of sensors are being deployed in cities to collect real-time data. This leads to a need for checking city states against city requirements at runtime. In this article, we develop a novel spatial–temporal specification-based monitoring system for smart cities. We first describe a study of over 1000 smart city requirements, some of which cannot be specified using the existing logic, such as the signal temporal logic (STL) and its variants. To tackle this limitation, we develop spatial aggregation STL (SaSTL)—a novel spatial aggregation STL—for the efficient runtime monitoring of safety and performance requirements in smart cities. We develop two new logical operators in SaSTL to augment STL for expressing spatial aggregation and spatial counting characteristics that are commonly found in real city requirements. We define the Boolean and quantitative semantics for SaSTL in support of the analysis of city performance across different periods and locations. We also develop efficient monitoring algorithms that can check the SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed spatially in a city). Additionally, we build an SaSTL-based monitoring tool to support decision making of different stakeholders to specify and runtime monitor their requirements in smart cities. We evaluate our SaSTL monitor by applying it to three case studies with large-scale real city sensing data (e.g., up to 10 000 sensors in one study). The results show that SaSTL has a much higher coverage expressiveness than other spatial–temporal logics, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor improves the safety and performance of smart cities via simulated experiments.

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.

Jobs–housing balance (JHB) and employment self-containment (ESC) have been used to examine the jobs–housing relationship. However, the effect of the modifiable areal unit problem (MAUP) on ESC and JHB has received little attention. This study aims to examine the effect of the MAUP on the spatial variation of ESC and JHB by utilizing mobile positioning data from Shenzhen, China. Journey-to-work trips are examined at the individual level and then aggregated into different spatial areal units. It is found that the average ESC increases with the increase in spatial areal units and that the relationship between JHB and ESC is amplified when the spatial areal unit increases with spatial aggregation. A 2 km grid is found to be an ideal spatial unit for the analysis of ESC in Shenzhen because it is the turning point in which the increase in ESC started to slow down, and the decrease in the coefficient of variation began to diminish. In addition, workers were more likely to commute by non-motorized transport modes when their jobs were within 2 km. This study helps elucidate the effect of the MAUP on ESC and JHB as well as determine the appropriate grid size for analysis. This study further suggests that the ideal spatial unit for the analysis of ESC and JHB may be related to the transport mode of the city under study.

Reanalysis data are widely used for simulating renewable energy and in particular wind power generation. While MERRA-2 has been a de-facto standard in many studies for a long time, the newer ERA5-reanalysis recently gained importance. Here, both datasets were used to simulate wind power generation and evaluate their quality in terms of correlations and error measures compared to historical data of wind power generation. Due to their coarse resolution, reanalyses are known to fail to represent local climatic conditions adequately. Hence, mean bias correction was applied with two versions of the Global Wind Atlas (GWA) to the reanalysis data and the quality of the resulting simulations was assessed. Potential users of these datasets can also benefit from our analysis of the impact of spatial and temporal aggregation on indicators of simulation quality. We also assessed regions which differ significantly in terms of the prevailing climate, some of which are underrepresented in similar studies: the US, Brazil, South-Africa, and New Zealand. Our principal findings are threefold. (i) ERA5 outperforms MERRA-2 in terms of the assessed error measures. (ii) Bias-correction with GWA2 does not improve simulation quality substantially, while bias-correction with GWA3 is detrimental. (iii) Temporal aggregation increases correlations and reduces errors, while spatial aggregation does so consistently only when comparing very fine and very coarse granularities.

The spatial characteristics of carbon emissions form the basis for exploring carbon neutrality measures for different regions. As the second largest carbon emission source globally, agricultural carbon emissions should not be ignored. This paper used panel data from 2009 to 2019 to calculate the agricultural carbon emissions of 30 provinces in China, and used the ML index, spatial Morans’I index and three convergence indices to analyze the dynamic changes and spatial pattern of China’s agricultural carbon emission performance (ACEP). The results showed that: (i), China's ACEP has increased by 1.5% from 2009 to 2019, following a spatial pattern of eastern (1.041) > western (1.020) > central (0.974);(ii) the contribution of technological progress to China's ACEP was 1.035, exceeding 1, and thus indicating that technological progress can improve ACEP, while the contribution of efficiency changes was only 0.991; (iii) China’s ACEP has exhibited a spatial aggregation effect over the last 10 years (P

BACKGROUND Unmanned aerial vehicles (UAVs) have been used in agriculture to collect imagery for crop and pest monitoring, and for decision‐making purposes. Spraying‐capable UAVs are now commercially available worldwide for agricultural applications. Combining UAV weed mapping and UAV sprayers into an UAV integrated system (UAV‐IS) can offer a new alternative to implement site‐specific pest management. RESULTS The UAV‐IS was 0.3‐ to 3‐fold more efficient at identifying and treating target weedy areas, while minimizing treatment on non‐weedy areas, than ground‐based broadcast applications. The UAV‐IS treated 20–60% less area than ground‐based broadcast applications, but also missed up to 26% of the target weedy area, while broadcast applications covered almost the entire experimental area and only missed 2–3% of the target weeds. The efficiency of UAV‐IS management practices increased as weed spatial aggregation increased (patchiness). CONCLUSION Integrating UAV imagery for pest mapping and UAV sprayers can provide a new strategy for integrated pest management programs to improve efficiency and efficacy while reducing the amount of pesticide being applied. The UAV‐IS has the potential to improve the detection and control of weed escapes to reduce/delay herbicide resistance evolution. © 2019 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry., The integration of UAVs for weed and pest detection with currently commercially available UAV sprayers can increase the efficiency of pesticide applications and help manage pesticide resistance.

Econometric models with spatial fixed effects (FE) require some kind of spatial aggregation. This aggregation may be based on postcode, school district, county or some other spatial subdivision. Common sense would suggest that the less aggregated, the better inasmuch as aggregation over larger areas tends to gloss over systematic spatial variation. On the other hand, low spatial aggregation results in thin data sets and potentially noisy spatial fixed effects. We show, however, how this trade-off can be substantially lessened if we allow for more flexible aggregations. The key insight is that if we aggregate over areas with similar location premiums, we obtain robust location premiums without glossing over too much of the spatial variation. We use machine learning in the form of a genetic algorithm to identify areas with similar location premiums. The best aggregations found by the genetic algorithm outperform a conventional FE by postcode, even with an order of magnitude fewer spatial controls. This opens the door for spatially sparse FEs, if economy in the number of variables is important. The major takeaway, however, is that the genetic algorithm can find spatial aggregations that are both refined and robust, and thus significantly, lessen the trade-off between robust and refined location premium estimates.

Ecosystem services of Amazonian forests are disproportionally produced by a limited set of hyperdominant tree species. Yet the spatial variation in the delivery of ecosystem services by individual hyperdominant species across their distribution ranges and corresponding environmental gradients is poorly understood. Here, we use the concept of habitat quality to unravel the effect of environmental gradients on seed production and aboveground biomass (AGB) of the Brazil nut, one of Amazonia’s largest and most long-lived hyperdominants. We find that a range of climate and soil gradients create trade-offs between density and fitness of Brazil nut trees. Density responses to environmental gradients were in line with predictions under the Janzen–Connell and Herms–Mattson hypotheses, whereas tree fitness responses were in line with resource requirements of trees over their life cycle. These trade-offs resulted in divergent responses in area-based seed production and AGB. While seed production and AGB of individual trees (i.e., fitness) responded similarly to most environmental gradients, they showed opposite tendencies to tree density for almost half of the gradients. However, for gradients creating opposite fitness-density responses, area-based seed production was invariable, while trends in area-based AGB tended to mirror the response of tree density. We conclude that while the relation between environmental gradients and tree density is generally indicative of the response of AGB accumulation in a given area of forest, this is not necessarily the case for fruit production., Frontiers in Plant Science, 12, ISSN:1664-462X

The input data of energy system models are, in many cases, aggregated data used to represent regions of interest. This paper presents a method of using energy-related spatial data and the max-p-regions method to define regions. The method aims to assign areas to regions that are similar in how much they consume, produce and store electricity.•A spatial dataset of administrative areas for 30 European countries is presented and used to apply the proposed method.•Use of energy-related spatial data to define regions of energy system model.•The method uses the heuristic solution of the max-p regions problem to reduce the spatial resolution of energy system models., Graphical abstract Image, graphical abstract

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.

Effective use of biocontrol agents (BCAs) is a potentially important component of sustainable agriculture. The ecological processes determining the success of biocontrol are complex, which may partly explain the limited success of biocontrol against plant diseases in field crops. Understanding the ecological characteristics of BCAs in addition to biocontrol mechanisms and direct biocontrol efficacy, including their survival and dispersal under heterogeneous conditions, is critically important to improve biocontrol efficacy. In this simulation study, we focused on the effects of the spatial aggregation of initial pathogen and biocontrol propagules (inocula) under spatially homogeneous and heterogeneous conditions on biocontrol potential. The simulation showed that, as expected, increasing the biocontrol propagule density led to increased biocontrol potential. Under a given inoculum density, increasing spatial aggregation of BCAs is not only likely to reduce biocontrol potential but also to increase variabilities in biocontrol outcomes. A spatially random distribution of BCA propagules is most likely to result in best biocontrol outcomes. Spatial aggregation of BCAs is more important than spatial heterogeneity in influencing biocontrol potential. Thus, the present simulation study illustrates the importance of ensuring a close-to-homogeneous distribution of BCA propagules for maximising biocontrol potential. By the same reasoning, a faster host growth rate will reduce biocontrol potential if BCA cannot keep up with host growth in terms of coverage: increasing BCA aggregation essentially leads to decreased effective coverage.

Prior studies conclude that climate plays one of the most important roles in driving variations in residential electricity consumption. While some past studies have quantified sensitivities of electricity use to ambient temperature, 1) few previous studies utilize both high temporal and spatial resolution electricity data, and 2) no research to our knowledge has investigated how the temporal and spatial resolution of electricity data, and choice of ambient temperature indicators, affects quantification of these sensitivities. In this study, we use smart meter data records of electricity use for 1245 households across California, along with hourly ambient temperature records, to compute electricity–temperature sensitivities using a segmented linear regression approach. We find that electricity use and temperature show the strongest relationships when computed using daily accumulated electricity use and daily average temperatures; using these metrics results in a mean electricity–temperature sensitivity of 0.11 kW °C−1. This value is higher than corresponding sensitivities computed using spatially aggregated data, with values ranging from 0.097–0.10 kW °C−1 depending on the amount of spatial aggregation. Through presenting probability density functions of household-level electricity–temperature sensitivities, we illustrate insights that can be gleaned using high resolution electricity datasets such as that used here. We note that values of electricity–temperature sensitivity reported here are representative of the 1245 households under investigation.

Innovative analyses of origin–destination (OD) data derived from automatic fare collection and automatic vehicle location systems in public transport networks enable planners to gain new insights i...

Adverse weather-related risk is a main source of crop production loss and a big concern for agricultural insurers and reinsurers. In response, weather risk hedging may be valuable, however, due to basis risk it has been largely unsuccessful to date. This research proposes the Lévy subordinated hierarchical Archimedean copula model in modelling the spatial dependence of weather risk to reduce basis risk. The analysis shows that the Lévy subordinated hierarchical Archimedean copula model can improve the hedging performance through more accurate modelling of the dependence structure of weather risks and is more efficient in hedging extreme downside weather risk, compared to the benchmark copula models. Further, the results reveal that more effective hedging may be achieved as the spatial aggregation level increases. This research demonstrates that hedging weather risk is an important risk management method, and the approach outlined in this paper may be useful to insurers and reinsurers in the case of agriculture, as well as for other related risks in the property and casualty sector.

The simplebaseline model achieves high performance of human pose estimation with simple network structure. But the model lacks the layer and spatial information fusion. In this paper, we propose DLSAnet, which fuse layers and spatial information efficetively. DLSAnet uses DLA as backbone which has excellent feature extraction capabilities in the field of object detection. In addition, a modified spatial pyramid pooling is introduced to pool and connect multi-scale local area features, allowing the network to learn object features more comprehensively. Using a four-branch SPP module instead of a single-branch SPP module connected by a single hopping layer. This method is effective in alleviating the problem of slow loss drop late in training. Experiments show that DLSAnet can achieve better accuracy.

PM2.5 has become the main pollutant of air pollution in China, and PM2.5 pollution control is one of the important means of atmospheric environmental governance. Aiming on the spatial and temporal distribution characteristics of regional PM2.5 concentration, this paper bases on the monitoring data of city PM2.5 concentration in Henan Province from 2015 to 2018 to study the spatial autocorrelation characteristics of city PM2.5 concentration and explore the city PM2.5 concentration spatial relationship by using the geo-statistical analysis method. The results showed that: the PM2.5 concentration in Henan shows obvious seasonal variation characteristics, the PM2.5 concentrations in the northern cities are significantly higher than that in the southern cities. The PM2.5 concentrations in the northern cities vary significantly with the seasonal transition, PM2.5 pollution is mainly moderately and above polluted, the PM2.5 concentrations in southern cities vary little with the seasonal transition, PM2.5 pollution is mainly lightly polluted. The city PM2.5 concentration shows a trend of regional integration with the significant spatial autocorrelation, the global autocorrelation characteristic is independent of PM2.5 concentration. The city PM2.5 concentration also presents the characteristics of local instability, forms a High-High aggregation region centered on Xinxiang and Zhengzhou, the aggregation degree tends to be significant with the increase of PM2.5 concentration, the aggregation area gradually expands to northward. The PM2.5 concentration in Anyang is no significant autocorrelation with the PM2.5 concentrations in other cities of the province.

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.

With the development of science and technology, the application of neural network model is more and more extensive. In order to avoid the outbreak of financial crisis, economists and policy makers around the world pay special attention to the timely warning of financial crisis. If we can make an accurate early warning of the economic crisis, the government and regulatory agencies will have more time to deal with the possible risks and avoid major interference to economic growth. Based on this, this paper studies the spatial aggregation analysis and systemic risk prevention of Internet Finance Based on neural network model. Based on BP neural network, this paper constructs a spatial econometric model by using spatial econometric analysis method, and analyzes the empirical results of financial spatial aggregation. The results show that the Moran’s I value of China’s Internet finance index in December 2019 is 0.372, P value is 0.001, Moran’s I value of Internet payment is 0.395, P value is 0.002, Moran’s I value of Internet monetary fund is 0.357, P value is 0.001, Moran’s I value of Internet investment and financing is 0.324, P value is 0.002, Moran’s I of Internet insurance is 0.313, P value is 0.001. The results show that there is spatial agglomeration in the development of Internet finance in China. In addition, this paper also studies the systemic risk of Internet finance, analyzes its causes and puts forward corresponding preventive measures.

We use geo-referenced establishment data to estimate parameters of a Gibbs model. The statistical model is used to decompose the conditional intensity of the spatial point process into trend and interaction components. The trend captures covariates related to firms’ costs, accessibility to public transport infrastructures, to technical universities and to cultural and recreational facilities. The ability to specify a Geyer interaction component captures the existence of additional spillovers providing a deeper insight into inter-establishment spatial dynamics. The results challenge some of the outcomes of the inter-urban industrial location literature, confirming that spatial aggregation compromises results in studies of business location. Firms’ location decisions are dominated by site costs, with transportation costs being much less of a consideration; universities geographical knowledge spillovers are confirmed for large establishments; and amenities are unlikely to be important location factors.

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.

Using the index of financial inclusion and the World Bank Global Findex database, this study measures the level of financial inclusion across countries. The results reveal a geographical spatial aggregation distribution in which developed European and North American countries enjoy higher levels of financial inclusion than the less developed countries of Africa and most of Asia. Accordingly, our spatial analysis proves our hypothesis and reveals dependence and aggregation effects among countries. Then, we employ spatial econometric research to identify those factors significantly associated with financial inclusion. The results show that an individual’s income, education and use of communications equipment are important factors that explain the level of financial inclusion, while financial depth and banking health status are the main determinants. Building an inclusive financial system is an important means for most countries to achieve the Millennium Development Goals.

Restoration success is often hampered by the failure of less dominant competitors to establish. An emerging literature on priority effects suggests the manipulation of community assembly as a useful technique to help overcome these difficulties by altering competitive relationships. We present data from a set of four priority experiments, carried out at each of three sites in restoration settings in California grasslands. These data, combined with patterns summarized from the literature, indicate that both short-term priority (1–3 weeks) and long-term priority (1 year) can profoundly shift interspecific relationships and benefit otherwise subordinate plant species, but that these effects are sometimes transitory, asymmetric, and contingent on environmental conditions and species composition. Restoration interventions that can produce priority effects include staggered planting times, weed control, seed pre-germination, plug planting, and spatial aggregation. Such interventions are likely to be at least initially effective, but their strength and persistence can differ considerably across systems in space and time. Further research may help identify the conditions that maximize the strength and persistence of priority effects in restoration settings.

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.

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.

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 identification of contributory factors to crash frequencies observed in different highway facilities can aid transportation and traffic management agencies to improve road traffic safety. In spite of the strategic importance of the national Portuguese road network, there are no recent studies concerned with either the identification of contributory factors to road crashes or Crash Prediction Models (CPMs) for this type of roadway. This study presents an initial contribution to this problem by focusing on the national roads NR-14, NR-101 and NR-206, which are located in Northern region of Portugal. They are two-lane single carriageway rural roads. This study analysed the crash frequencies, Average Annual Daily Traffic (AADT) and geometric characteristics of 88 two-lane road segments. The selected segments were 200-m-long and did not cross through urbanized areas. The fixed length of 200 meters corresponds to the road length used in Portugal to define a critical point. Data regarding the annual crash frequency and the AADT were available from 1999 to 2010. Due to the high number of zero-crash records in the initial database, the data were explored to identify the best statistical modelling approach to be adopted. The Generalized Estimating Equations (GEE) procedure was applied to 10 distinctive databases formed by grouping the original data in time and space. The results show that the different observations within each road segment present an exchangeable correlation structure type. This paper also analyses the impact of the sample size on the model’s capability of identifying the contributing factors to crash frequencies. The major contributing factors identified for the two-lane highways studied were the traffic volume (expressed in AADT), lane width, vertical sinuosity, and Density of Access Points (DAP). Acceptable CPM was identified for the highways considered, which estimated the total number of crashes for 400-m-long segments for a cumulative period of two years. First published online 18 August 2015

Background Life expectancy at birth (LE) has been calculated for states and counties. LE estimates at these levels mask health disparities in local communities. There are no nationwide estimates at the sub-county level. We present a stepwise approach for calculating LE using census tracts in New York state to identify health disparities. Methods Our study included 2751 census tracts in New York state, but excluded New York City. We used population data from the 2010 United States Census and 2008–2010 mortality data from the state health department. Tracts were assigned to 99.97% of the deaths. We removed tracts which had a majority of people living in group quarters. Deaths in these tracts are often recorded elsewhere. Of the remaining 2679 tracts, 6.6% of the tracts had standard errors ≥ 2 years. A geographic aggregation tool was used to aggregate tracts with fewer than 60 deaths, and then aggregate areas that had standard errors of ≥ 2 years. Results Aggregation resulted in a 9.9% reduction in the number of areas. Tracts with

International audience; A multitude of resistance deployment strategies have been proposed to tackle the evolutionary potential of pathogens to overcome plant resistance. In particular, many landscape-based strategies rely on the deployment of resistant and susceptible cultivars in an agricultural landscape as a mosaic. However, the design of such strategies is not easy as strategies targeting epidemiological or evolutionary outcomes may not be the same. Using a stochastic spatially explicit model, we studied the impact of landscape organization (as defined by the proportion of fields cultivated with a resistant cultivar and their spatial aggregation) and key pathogen life-history traits on three measures of disease control. Our results show that short-term epidemiological dynamics are optimized when landscapes are planted with a high proportion of the resistant cultivar in low aggregation. Importantly, the exact opposite situation is optimal for resistance durability. Finally, well-mixed landscapes (balanced proportions with low aggregation) are optimal for long-term evolutionary equilibrium (defined here as the level of long-term pathogen adaptation). This work offers a perspective on the potential for contrasting effects of landscape organization on different goals of disease management and highlights the role of pathogen life history.

Health care accessibility can be measured by the number of prospective patients who could reach a medical facility within a prescribed time limit. The representation of health care demand in estimating accessibility is an important consideration since different spatial aggregations of demand have different consequences with regard to accessibility estimates. This article examines the effects of aggregating population demand for primary health care, ranging from census tract to aggregated census block, on estimates of primary health care accessibility. Spatial representations of aggregated demand were incorporated into a location-allocation model in order to determine a measure of accessibility represented by the unmet demand for primary health care services. The model was implemented for the U.S. State of Idaho, based on the allocation of Idaho residents’ demand for primary health care to the state’s existing primary health care facilities. The results confirm a relationship between the level of demand aggregation and the level of potential accessibility. In case of a rural state such as Idaho the relationship is positive; higher levels of aggregation result in higher measures of accessibility.

A bottom-up analysis of 2012 data suggests that re-dispatching the fleet of coal generating units to natural gas combined-cycle generation holds the technical potential to mitigate over 500 million metric tons of carbon dioxide, or about 25 percent of the sector's total emissions in 2012. As expected, mitigation potential generally increases with spatial aggregation and combined-cycle utilization level.

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 .

A procedure for spatial aggregation of synthetic water demand time series is presented. Starting from synthetic water demand time series generated at user level and reproducing mean and variance of the corresponding observed series, the procedure allows the aggregated series to preserve the statistics of interest observed at the aggregation level considered. The procedure uses a method proposed by Iman and Conover (1982): the synthetic user water demand time series are reordered to preserve the observed spatial correlations of appropriate lag-time; then, the spatial aggregation of these series leads to a series representative of the user group reproducing the corresponding means and variances for the different hours of the generic day. Four different ways of performing this aggregation have been investigated and compared. Application to a case study consisting of the water demands of 21 users highlights that the approaches considered show different levels of effectiveness in reproducing the statistics, but overall the procedure proposed is a valid tool for the bottom-up generation of synthetic water demand time series.

The European electricity sector has undergone significant reforms in recent years, in the direction of market opening, integration and privatization. National and regional markets are now characterized by the presence of domestic and foreign firms, both privately and publicly owned. Did foreign entry induce positive productivity spillovers to domestic firms in the electricity sector, both at the aggregate and disaggregated level, while also controlling for domestic firms' ownership? This paper examines this issue by focusing on regional foreign direct investment (FDI) spillovers in the aggregated electricity sector and in the disaggregated sub-sectors of generation and distribution. The results show the importance of industry aggregation in determining the existence and sign of regional FDI spillovers for domestic firms. FDI spillovers are then calculated based on a purely geographic scale, by considering the distance between each firm's city of location and firms in neighboring cities. The importance and sign of FDI spillovers is different with respect to the analysis based on regional administrative boundaries, suggesting that spatial aggregation, along with industrial aggregation, is relevant in accounting for productivity spillover effects of foreign presence in the EU electricity sector.

The goal of this paper is to study empirically the determinants of foreign firms' location in Polish regions with a special focus on the role of firm size. To derive testable hypotheses on the firm location choice an analytical framework that combines the firm heterogeneity and the new economic geography literatures is used. In this framework, agglomeration economies, technical infrastructure, and labour market characteristics may affect location decisions of foreign investors. The hypotheses derived from the theory are validated empirically using the regional dataset at NUTS 2 level of spatial aggregation for the period 1999–2010. The empirical evidence confirms the existence of firm heterogeneity with respect to the determinants of firm location choice.

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.

Power sector capacity expansion models (CEMs) have a broad range of spatial resolutions. This paper uses the Regional Energy Deployment System (ReEDS) model, a longterm national scale electric sector CEM, to evaluate the value of high spatial resolution for CEMs. ReEDS models the United States with 134 load balancing areas (BAs) and captures the variability in existing generation parameters, future technology costs, performance, and resource availability using very high spatial resolution data, especially for wind and solar modeled at 356 resource regions. In this paper we perform planning studies at three different spatial resolutions-native resolution (134 BAs), state-level, and NERC region level-and evaluate how results change under different levels of spatial aggregation in terms of renewable capacity deployment and location, associated transmission builds, and system costs. The results are used to ascertain the value of high geographically resolved models in terms of their impact on relative competitiveness among renewable energy resources.

This chapter shows how historians’ methodologies and approaches are being used to reconstruct marine ecological phenomena of the past. In terms of the use of history in historical marine ecology, a number of examples are presented of how past species abundance and spatial aggregation as well as long term catch rates has been investigated through the use of past paper documents, including photographs.Equally important, and more frequently, historians are answering anthropogenic questions evolving around individual fishermen, fishing fleets, consumers, managers and marine scientists. Listing all relevant historical scholarship, which can be termed marine environmental history are beyond the scope of this chapter. What is mainly in focus here, is historical research, where the methodologies have been used to infer knowledge on past marine resource use.The final parts of this chapter explore some of the prospects for including historical archival material further in future research in marine environmental history. Accessing these types of data can be a rewarding but labour intensive adventure. Nonetheless, the ongoing process of digitization is a welcome proliferation of the accessibility of such data, where a number of examples were presented.

Functional imaging plays an important role in the assessment of organ functions, as it provides methods to represent the spatial behavior of diagnostically relevant variables within reference anatomical frameworks. The salient physical events that underly a functional image can be unveiled by appropriate feature extraction methods capable to exploit domain-specific knowledge and spatial relations at multiple abstraction levels and scales. In this work we focus on general feature extraction methods that can be applied to cardiac activation maps, a class of functional images that embed spatio-temporal information about the wavefront propagation. The described approach integrates a qualitative spatial reasoning methodology with techniques borrowed from computational geometry to provide a computational framework for the automated extraction of basic features of the activation wavefront kinematics and specific sets of diagnostic features that identify an important class of rhythm pathologies.

a b s t r a c t The objective of this study was to map agricultural intensity on the scale of France with spatial resolution adequate for policy targeting. Using the French Farm Accountancy Data Network (FADN), we computed an intensity indicator based on input costs per ha ("IC/ha"). Common variables between the FADN and four other datasets were included in a two steps multinomial regression to estimate the IC/ha value of each Small Agricultural Region ("SAR", units with homogeneous agro-ecological characteristics with mean width = 22.4 km). The local indicator of spatial association was used to reveal clusters where SARs with homogeneous intensities were aggregated. We showed that the IC/ha indicator displayed a broad intensity gradient where production types were fairly evenly distributed. Multinomial regression models provided a reliable estimate of the intensity indicator (mean cross-validation error = 23%, mean r 2 = 0.7) with SAR resolution. At the scale of France and within the two intensity extremes ( 500 D /ha), SARs were significantly aggregated in several clusters. Most low-input SARs were aggregated into a large cluster ranging across several mountainous regions. Less high-input SARs were significantly aggregated. Our results could be used for infra-regional targeting of conservation policies. © 2012 Elsevier B.V. All rights reserved.

As irrigation demands usually take the largest share of water supply, paddy fallow is considered as a drought relieving measure in some Asian paddy growing countries by transferring the water saved to the municipal and industrial sectors. But the relationship between fallow area and irrigation demand reduction is not necessarily linear, there may be more than dozens combinations of fallow farm that can meet the same amount of irrigation demand reduction requirement. The Joint Count Statistics (JCS), an index commonly used in spatial analysis to measure the spatial coherence among cells was modified as a spatial aggregation index for evaluating the irrigation demand reduction effectiveness from a spatial perspective. This Modified JCS is supposed to identify the degree of spatial aggregation by taking underlying irrigation network into considerations. The modified JCS was proved to be effective to identify better fallow pattern through a case study in Taiwan.

Pollen-mediated gene flow is one of the main concerns associated with the introduction of genetically modified (GM) crops. Should a premium for non-GM varieties emerge on the market, ‘contamination’ by GM pollen would generate a revenue loss for growers of non-GM varieties. This paper analyses the problem of pollen-mediated gene flow as a particular type of production externality. The model, although simple, provides useful insights into coexistence policies. Following on from this and taking GM herbicide-tolerant oilseed rape (Brassica napus) as a model crop, a Monte Carlo simulation is used to generate data and then estimate the effect of several important policy variables (including width of buffer zones and spatial aggregation) on the magnitude of the externality associated with pollen-mediated gene flow.

The potential impacts of climate change on coastal zones combined with the ever increasing number of people living in these areas, motivate a more long-term perspective in coastal management. To explore whether such a perspective raises the need for developing coastal management strategies at larger spatial scales, this paper presents a case study for the dune-protected Holland coast. Long-term (200 years) coastal management strategies are set-up at different levels of spatial aggregation, accounting for specific characteristics of the coast itself and for types of land use in the hinterland. Using a combination of multi-criteria analysis and cost-benefit analysis the new coastal management strategies are compared to the long-term continuation of present coastal management practice. It appears that the large uncertainties that inevitably accompany a long-term perspective do not preclude the comparison of long-term coastal management strategies. Additionally, it appears that a long-term perspective favors solutions for coastal defense enhancement that are more spatially extended and alongshore uniform than those emerging from continuing today's coastal management practice for two centuries.

Freight transportation is an important pathway for the introduction and dissemination of exotic forest insects (EFI). Identifying the final destination of imports is critical in determining the likelihood of EFI establishment. We analyzed the use of regional freight transport information to characterize risk of urban and periurban areas to EFI introductions. Specific objectives were to 1) approximate the final distribution of selected imports among urban areas of the United States, 2) characterize the final distribution of imports in terms of their spatial aggregation and dominant world region of origin, and 3) assess the effect of the final distribution of imports on the level of risk to urban and periurban forests from EFI. Freight pattern analyses were conducted for three categories of imports whose products or packaging materials are associated with EFI: wood products, nonmetallic mineral products, and machinery. The final distribution of wood products was the most evenly distributed of the three selected imports, whereas machinery was most spatially concentrated. We found that the type of import and the world region of origin greatly influence the final distribution of imported products. Risk assessment models were built based on the amount of forestland and imports for each urban area. The model indicated that 84–88% of the imported tonnage went to only 4–6% of the urban areas in the contiguous United States. We concluded that freight movement information is critical for proper risk assessment of EFI. Implications of our findings and future research needs are discussed.

Accurate automatic organ segmentation is an important yet challenging problem for medical image analysis. The pancreas is an abdominal organ with very high anatomical variability. This inhibits traditional segmentation methods from achieving high accuracies, especially compared to other organs such as the liver, heart or kidneys. In this paper, we present a holistic learning approach that integrates semantic mid-level cues of deeply-learned organ interior and boundary maps via robust spatial aggregation using random forest. Our method generates boundary preserving pixel-wise class labels for pancreas segmentation. Quantitative evaluation is performed on CT scans of 82 patients in 4-fold cross-validation. We achieve a (mean ± std. dev.) Dice Similarity Coefficient of 78.01 %±8.2 % in testing which significantly outperforms the previous state-of-the-art approach of 71.8 %±10.7 % under the same evaluation criterion.

Based on the spatial autocorrelation (SA) and Geographic Information System (GIS) technology, the spatial differences of the rural economic development level was investigated by adopting the rural resident income data in Guizhou Province from 2009 to 2013. The results of global Moran’s I value for rural resident income at county level showed that there existed significant positive spatial autocorrelation and significant spatial aggregation in the spatial distribution of rural resident income. However, the global Moran’s I value showed the increasing trend during 2009 to 2013, indicating an enlarged spatial disparity of rural economy in Guizhou. The results of the local indicators of spatial association(LISA) cluster maps of 2009 and 2013 showed that most of counties were characterized by positive local spatial association , ie. They were located in the HH or the LL region. The significant HH counties were mostly to be found in the middle part of Guizhou. The significant LL counties were found in the Wuchuan county and Zhenfeng county, northwest and southwest of Guizhou.