Simulation of urban expansion based on cellular automata and maximum entropy model

Urban expansion is a hot topic in land use/land cover change(LUCC) researches. In this paper, maximum entropy model and cellular automata(CA) model are coupled into a new CA model(Maxent-CA) for urban expansion. This model can help to obtain transition rules from single-period dataset. Moreover, it can be constructed and calibrated easily with several steps.Firstly, Maxent-CA model was built by using remote sensing data of China in 2000(basic data) and spatial variables(such as population density and Euclidean distance to cities). Secondly, the proposed model was calibrated by analyzing training samples,neighborhood structure and spatial scale. Finally, this model was verified by comparing logistic regression CA model and their simulation results. Experiments showed that suitable sampling ratio(sampling ratio equals the proportion of urban land in the whole region) and von Neumann neighborhood structure will help to yield better results. Spatial structure of simulation results becomes simple as spatial resolution decreases. Besides, simulation accuracy is significantly affected by spatial resolution.Compared to simulation results of logistic regression CA model, Maxent-CA model can avoid clusters phenomenon and obtain better results matching actual situation. It is found that the proposed model performs well in simulating urban expansion of China. It will be helpful for simulating even larger study area in the background of global environment changes.     

Analysing space–time tree interdependencies based on individual tree growth functions

We analysed the space–time structure of two spatially explicit forest data sets considering the associated growth function for each tree obtained from the annual radial growth measured from increment cores bored at breast height. We used a new second order formulation based on the mark correlation function, the functional mark correlation function, to analyse spatial pattern involving functions to each spatial location. A decomposition of individual growth function into spatial and non-spatial components was considered and only the spatial components were analysed. Our results confirm the usefulness of these new approach compared with other well-established spatial statistical tools such as the mark correlation function. In particular, the functional mark correlation function of the spatial and temporal components of tree growth determines the space–time structure of tree development regardless of the non-spatial components contained in this function. Moreover, this explicit temporal analysis detects space–time interaction effects that are not evident when analysing the spatial distribution of cumulative growth measures such as the tree basal area.     

Using spatial multilevel regression analysis to assess soil type contextual effects on neural tube defects

The rate of neural tube defects (NTDs) in Shanxi Province is the highest world widely. Both human and environmental factors can induce NTDs, but various studies ignored contextual effects. This research examines whether there are significant soil type contextual effects on the rate of NTDs. A spatial two-level regression model is used to quantify the magnitude of contextual effects. Spatial autocorrelated errors structure is used to control autocorrelation of residuals. The results suggest that the spatial multilevel model fit the data better than non-spatial multilevel models. Our findings indicate that there are significant soil type contextual effects on the rate of NTDs, even after taking into account of fertilizer and net income. More attentions should be focused on how characteristics of each soil type may affect the rates of NTDs in further studies, which is a relevant issue for understanding etiology of NTDs.     

Representing spatial dependence and spatial discontinuity in ecological epidemiology: a scale mixture approach

Variation in disease risk underlying observed disease counts is increasingly a focus for Bayesian spatial modelling, including applications in spatial data mining. Bayesian analysis of spatial data, whether for disease or other types of event, often employs a conditionally autoregressive prior, which can express spatial dependence commonly present in underlying risks or rates. Such conditionally autoregressive priors typically assume a normal density and uniform local smoothing for underlying risks. However, normality assumptions may be affected or distorted by heteroscedasticity or spatial outliers. It is also desirable that spatial disease models represent variation that is not attributable to spatial dependence. A spatial prior representing spatial heteroscedasticity within a model accommodating both spatial and non-spatial variation is therefore proposed. Illustrative applications are to human TB incidence. A simulation example is based on mainland US states, while a real data application considers TB incidence in 326 English local authorities.     

Modeling urban growth with GIS based cellular automata and least squares SVM rules: a case study in Qingpu–Songjiang area of Shanghai,China

A critical issue in urban cellular automata (CA) modeling concerns the identification of transition rules that generate realistic urban land use patterns. Recent studies have demonstrated that linear methods cannot sufficiently delineate the extraordinary complex boundaries between urban and non-urban areas and as most urban CA models simulate transitions across these boundaries, there is an urgent need for good methods to facilitate such delineations. This paper presents a machine learning CA model (termed MachCA) with nonlinear transition rules based on least squares support vector machines (LS-SVM) to simulate such urban growth. By projecting the input dataset into a high dimensional space using the LS-SVM method, an optimal hyper-plane is constructed to separate the complex boundaries between urban and nonurban land, thus enabling the retrieval of nonlinear CA transition rules. In the MachCA model, the transition rules are yes–no decisions on whether a cell changes its state or not, the rules being dynamically updated for each iteration of the model implementation. The application of the MachCA for simulating urban growth in the Shanghai Qingpu–Songjiang area in China reveals that the spatial configurations of rural–urban patterns can be modeled. A comparison of the MachCA model with a conventional CA model fitted by logarithmic regression (termed LogCA) shows that the MachCA model produces more hits and less misses and false alarms due to its capability for capturing the spatial complexity of urban dynamics. This results in improved simulation accuracies, although with only less than 1 % deviation between the overall errors produced by the MachCA and LogCA models. Nevertheless, the way MachCA model use in retrieving the transition rules provides a new method for simulating the dynamic process of urban growth.     

Spatially explicit assessment of land ecological security with spatial variables and logistic regression modeling in Shanghai,China

Ecological security is a fundamental component of regional security that has drawn increased attention worldwide over the past two decades. This paper presents a novel approach to assess the status of land ecological security (LES) in Shanghai, China from 1992 to 2011 using spatial variables and a logistic regression model. The LES status of 1745 points within the study area in 1992, 2001 and 2011 was sampled systematically using a 2 × 2 km grid sample frame and evaluated based on an expert method with ten experts from five fields. A five-point Likert scale was used to score the LES status as very insecure, insecure, neutral, secure or very secure. We identified several explanatory factors to the LES status, including distance-based variables describing the proximities to urban center, developed areas and sources of pollution, as well as variables regarding the density of built-up areas and the mean value of normalized difference vegetation index. A logistic regression model was used to quantify the relationship between LES scores and the spatial variables at each of the three time points, resulting in a series of maps illustrating the LES patterns of Shanghai in 1992, 2001 and 2011. The results show that LES is either very insecure or insecure at the center of Shanghai and at its district centers, and the LES of the entire Shanghai municipality has deteriorated significantly from 1992 to 2011. This research contributes to an enhanced understanding of LES changes resulting from rapid urbanization and industrialization of the Shanghai municipality and provides a methodological framework to study LES elsewhere.     

Multi-agent based modeling of spatiotemporal dynamical urban growth in developing countries: simulating future scenarios of Lianyungang city,China

Urbanization is the most typical form of land use/cover change, and exploration of the driving mechanism of urban growth and the prediction of its future changes are very important for achieving urban sustainable development. In view of the ability of a multi-agent system to simulate a complex spatial system and from the perspective of combining macroscopic and microscopic decision-making behaviors of agents, a spatiotemporal dynamical urban growth simulation model based on the multi-agent systems has been developed. In this model, macroscopic land use planning behaviors implemented by macroagents and microscopic land use selection behaviors autonomously generated by microagents interact within two-dimensional spatial cells. Furthermore, the urbanization process is promoted through joint decision-making by macroagents and microagents. Considering the central region of the coastal industrial city Lianyungang as the study area, we developed three target scenarios on the basis of current trends, economic development priorities, and environmental protection priorities. Moreover, the corresponding urban growth scenarios were simulated and analyzed. The simulation results show that by combining the macroscopic and microscopic decision-making behaviors of agents to simulate spatiotemporal dynamical urban growth based on the multi-agent systems, the proposed model can provide a useful spatial exploratory tool for explaining the driving mechanism of urbanization and providing decision-making support for urban management.     

Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression

In the context of climate change and rapid urbanization, urban pluvial floods pose an increasing threat to human wellbeing and security in the cities of China. A valuable aid to managing this problem lies in understanding the roles of environmental factors in influencing the occurrence of pluvial floods. This study presents a spatial analysis of records of inundated streets in the inner city of Shanghai during 1997–2013. A geographically weighted regression (GWR) is employed to examine the spatially explicit relationships between inundation frequency and spatial explanatory factors, and an ordinary least squares regression (OLS) is used to validate the GWR results. Results from the GWR model show that the inundation frequency is negatively related to elevation, pipeline density, and river density, and is positively related to road/square ratio and shantytown ratio. The green ratio is another significant explanatory factor for inundation frequency, and its coefficients range from ?1.11 to 0.81. In comparison with the OLS model, the GWR model has better performance as it has higher R², and lower corrected Akaike information criterion and mean square error values, as well as insignificant spatial autocorrelation of the model residuals. Additionally, the GWR model reveals detailed site-specific roles of the related factors in influencing street inundation. These findings demonstrate that the GWR model is a useful tool for investigating spatially explicit causes of disasters. The results also provide guidance for policy makers aiming to mitigate urban pluvial flood risks.     

Multiscale effects and drivers of landscape heterogeneity for water-related ecosystem services in urban agglomerations

Understanding the multiscale impacts and drivers of urban agglomeration landscape patterns for ecosystem services (ESs), especially water-related ecosystem services (WESs), is essential for the development of regional ecological management. However, the multiscale impacts and driving mechanisms of urban agglomeration landscape patterns for ESs have not been adequately explained. In this study, multivariate data were employed, and the InVEST model, trend test method, coupled GeoDetector and geographically and temporally weighted regression (GTWR) method were utilized to comprehensively explore the spatial and temporal changes in landscape patterns and WESs in the Pearl River Delta urban agglomeration (PRDUA) at various grid and administrative scales from 1990 to 2020 and to determine the driving mechanisms affecting WESs. The results indicated that the variation characteristics of landscape patterns and WESs in the PRDUA were consistent, forming a binary spatial structure of core and peripheral areas in an inverted “U” shape around the estuary of the Pearl River. The relationship between landscape patterns and WESs weakened with the increase of scale, and the correlation coefficient decreased by approximately 0.10 from 5 km to 10 km grid scale. Additionally, precipitation (PRE) was the main factor controlling WESs changes in the PRDUA, explaining more than 50% of the changes in WESs, and the regression coefficients ranged from 0.0825 to 0.1584. Changes in WESs were the result of the combined effects of natural factors, including PRE, landscape pattern, elevation, slope, and socioeconomic factors, such as population and gross domestic product (GDP). Overall, these findings could contribute to optimizing regional landscape patterns and fostering sustainable development of the ecological environment in urban agglomerations.     

Using geographically weighted regression to explore the spatially heterogeneous spread of bovine tuberculosis in England and Wales

An understanding of the factors that affect the spread of endemic bovine tuberculosis (bTB) is critical for the development of measures to stop and reverse this spread. Analyses of spatial data need to account for the inherent spatial heterogeneity within the data, or else spatial autocorrelation can lead to an overestimate of the significance of variables. This study used three methods of analysis—least-squares linear regression with a spatial autocorrelation term, geographically weighted regression (GWR) and boosted regression tree (BRT) analysis—to identify the factors that influence the spread of endemic bTB at a local level in England and Wales. The linear regression and GWR methods demonstrated the importance of accounting for spatial differences in risk factors for bTB, and showed some consistency in the identification of certain factors related to flooding, disease history and the presence of multiple genotypes of bTB. This is the first attempt to explore the factors associated with the spread of endemic bTB in England and Wales using GWR. This technique improves on least-squares linear regression approaches by identifying regional differences in the factors associated with bTB spread. However, interpretation of these complex regional differences is difficult and the approach does not lend itself to predictive models which are likely to be of more value to policy makers. Methods such as BRT may be more suited to such a task. Here we have demonstrated that GWR and BRT can produce comparable outputs.     

Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea

The aim of this study was to apply, verify and compare a multiple logistic regression model for landslide susceptibility analysis in three Korean study areas using a geographic information system (GIS). Landslide locations were identified by interpreting aerial photographs, satellite images and a field survey. Maps of the topography, soil type, forest cover, lineaments and land cover were constructed from the spatial data sets. The 14 factors that influence landslide occurrence were extracted from the database and the logistic regression coefficient of each factor was computed. Landslide susceptibility maps were drawn for these three areas using logistic regression coefficients derived not only from the data for that area but also using those for each of the other two areas (nine maps in all) as a cross‐check of method validity. For verification, the results of the analyses were compared with actual landslide locations. Among the nine cases, the Janghung exercise using the logistic formula and the coefficient for Janghung had the greatest accuracy (88·44%), whereas Janghung results, when considered by the logistic formula and the coefficient for Boeun, had the least accuracy (74·16%). Copyright © 2007 John Wiley & Sons, Ltd.     

Multi-city sustainable regional urban growth simulation—MSRUGS: a case study along the mid-section of Silk Road of China

Urban growth along the middle section of the ancient silk-road of China (so called West Yellow River Corridor—He-Xi Corridor) has taken a unique path deviating from what is commonly seen in the coastal China. Urban growth here has been driven by historical heritage, transportation connection between East and West China, and mineral exploitation. However, it has been constrained by water shortage and harsh natural environment because this region is located in arid and semi-arid climate zones. This paper attempts to construct a multi-city agent-based model to explore possible trajectories of regional urban growth along the entire He-Xi Corridor under a severe environment risk, over urban growth under an extreme threat of water shortage. In contrast with current ABM approaches, our model will simulate urban growth in a large administrative region consisting of a system of cities. It simultaneously considers the spatial variations of these cities in terms of population size, development history, water resource endowment and sustainable development potential. It also explores potential impacts of exogenous inter-city interactions on future urban growth on the basis of urban gravity model. The algorithmic foundations of three types of agents, developers, conservationists and regional-planners, are discussed. Simulations with regard to three different development scenarios are presented and analyzed.     

微重力探测城市地下空间试验及正演模拟分析

随着国家城市快速发展,城市地下空间探测与开发利用需求加大,传统物探方法在人文活动区域受干扰因素较多,无法获取真实准确的探测数据。微重力方法相对而言受干扰因素较小,对于城市建筑物和人类活动遗迹干扰可以通过模型正演校正的方法消除,从而获取高精度重力数据,进而通过有效的反演方法,可以获得城市地下空间的隧道、采空区、空洞、塌陷区、管廊等空间位置信息。本文用地面移动式高精度重力测量仪器,进行城市探测中受影响的因素进行试验性探测与分析,并结合正演模型校正研究,微重力方法在城市地下空间探测上有不错的效果。     

我国城市湖泊空间分布格局特征分析及影响因素探讨

城市湖泊作为城市与自然之间进行水气交换的蓝色空间,具有供水、防洪、休闲、气候调节以及改善城市生态环境等诸多生态服务功能。中国地域辽阔、城市众多,不同区域的城市湖泊受自然地理环境和社会经济发展等因素的影响而具有显著的空间差异特征。目前已有研究对我国省会城市和个别大型城市的湖泊空间分布及变化特征等开展研究,但全国范围内各行政等级单元内城市湖泊分布的空间格局及其影响因素仍缺乏综合分析。本研究基于中国城市湖泊数据集,从城市分布的地域单元、行政等级、城市规模3个方面对城市湖泊分布特征进行统计分析和比较,并结合自然和人类活动要素,初步探讨影响城市湖泊分布规模和丰度的主控因子。结果表明,2020年全国共有约11万个面积大于0.001km²城市湖泊(不包括太湖、滇池等大型湖泊),总面积约2112 km²,约占全国城市(遥感城市不透水层区域)面积的1.1%。城市湖泊的分布具有显著的集聚和分异特征,数量超过70%的城市湖泊分布在约20%的县(区)级行政单元,约21%的县(区)级行政单元基本没有(<10 m遥感影像分辨率下10个像元)城市湖泊分布。城市湖泊数...     

A comparative analysis of three habitat suitability models for commercial yield estimation of Tapes philippinarum in a North Adriatic coastal lagoon (Sacca di Goro,Italy)

Habitat Suitability (HS) models have been extensively used by conservation planners to estimate the spatial distribution of threatened species and of species of commercial interest. In this work we compare three HS models for the estimation of commercial yield potential and the identification of suitable sites for Tapes philippinarum rearing in the Sacca di Goro lagoon (Italy) on the basis of six environmental factors. The habitat suitability index (HSI) is based on expert opinion while the habitat suitability conditional (HSC) is calibrated on observational data. The habitat suitability mixed (HSM) model is a two-part model combining expert knowledge and regression analysis: the first component of the model uses logistic regression to identify the areas in which clams are likely to be present; the second part applies the same parameter-specific suitability functions of the HSI model only in the areas previously identified as productive by the logistic component.The HS models were validated on an independent data set and estimates of potential yield of the Goro lagoon were compared. The effectiveness of the three approaches is then discussed in terms of predicted yield and identification of suitable sites for farming.     

A comparative analysis of three habitat suitability models for commercial yield estimation of Tapes philippinarum in a North Adriatic coastal lagoon (Sacca di Goro, Italy)

Habitat Suitability (HS) models have been extensively used by conservation planners to estimate the spatial distribution of threatened species and of species of commercial interest. In this work we compare three HS models for the estimation of commercial yield potential and the identification of suitable sites for Tapes philippinarum rearing in the Sacca di Goro lagoon (Italy) on the basis of six environmental factors. The habitat suitability index (HSI) is based on expert opinion while the habitat suitability conditional (HSC) is calibrated on observational data. The habitat suitability mixed (HSM) model is a two-part model combining expert knowledge and regression analysis: the first component of the model uses logistic regression to identify the areas in which clams are likely to be present; the second part applies the same parameter-specific suitability functions of the HSI model only in the areas previously identified as productive by the logistic component. The HS models were validated on an independent data set and estimates of potential yield of the Goro lagoon were compared. The effectiveness of the three approaches is then discussed in terms of predicted yield and identification of suitable sites for farming.     

Probabilistic modeling and uncertainty estimation of urban water consumption under an incompletely informational circumstance

With a booming development characterized by new urbanization in current China, urban water consumption attracts growing concerns. An efficient and probabilistic prediction of urban water consumption plays a vital role for urban planning toward sustainable development, especially in megacities limited by water resources. However, the data insufficiency issue commonly exists nowadays and seriously restricts further development of urban water simulation. In this article, we proposed a consolidated framework for probabilistic prediction of water consumption under an incompletely informational circumstance to deal with the challenge. The model was constructed based on a state-of-the-art Bayesian neural networks (BNNs) technique. Three dominated influencing factors were identified and included into the BNN model. Future impact factors were generated by using a variety of methods including a quadratic polynomial model, a regression and auto-regressive moving average combination model and a Grey Verhulst model. Thereafter, water consumption projection (2013–2020) and uncertainty estimates was done. Results showed that the model matched well with observations. Through reducing the dependence on large amount of information and constructing a probabilistic means incorporating uncertainty estimation, the new approach can work better than conventional means in support of water resources planning and management under an incompletely informational circumstance.     

A comparative analysis of three habitat suitability models for commercial yield estimation of Tapes philippinarum in a North Adriatic coastal lagoon (Sacca di Goro, Italy)

Habitat Suitability (HS) models have been extensively used by conservation planners to estimate the spatial distribution of threatened species and of species of commercial interest. In this work we compare three HS models for the estimation of commercial yield potential and the identification of suitable sites for Tapes philippinarum rearing in the Sacca di Goro lagoon (Italy) on the basis of six environmental factors. The habitat suitability index (HSI) is based on expert opinion while the habitat suitability conditional (HSC) is calibrated on observational data. The habitat suitability mixed (HSM) model is a two-part model combining expert knowledge and regression analysis: the first component of the model uses logistic regression to identify the areas in which clams are likely to be present; the second part applies the same parameter-specific suitability functions of the HSI model only in the areas previously identified as productive by the logistic component.The HS models were validated on an independent data set and estimates of potential yield of the Goro lagoon were compared. The effectiveness of the three approaches is then discussed in terms of predicted yield and identification of suitable sites for farming.     

Latin hypercube approach to estimate uncertainty in ground water vulnerability

A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regression predictions of ground water vulnerability. Central to the proposed method is the assumption that prediction uncertainty in ground water vulnerability models is a function of input error propagation from uncertainty in the estimated logistic regression model coefficients (model error) and the values of explanatory variables represented in the GIS (data error). Input probability distributions that represent both model and data error sources of uncertainty were simultaneously sampled using a Latin hypercube approach with logistic regression calculations of probability of elevated nonpoint source contaminants in ground water. The resulting probability distribution represents the prediction intervals and associated uncertainty of the ground water vulnerability predictions. The method is illustrated through a ground water vulnerability assessment of the High Plains regional aquifer. Results of the LHS simulations reveal significant prediction uncertainties that vary spatially across the regional aquifer. Additionally, the proposed method enables a spatial deconstruction of the prediction uncertainty that can lead to improved prediction of ground water vulnerability.