Advanced reliability analysis of slopes in spatially variable soils using multivariate adaptive regression splines

This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the influences of the multiscale spatial variability of soil properties on the probability of failure(P_f) of the slopes. In the proposed approach, the relationship between the factor of safety and the soil strength parameters characterized with spatial variability is approximated by the MARS, with the aid of Karhunen-Loeve expansion. MCS is subsequently performed on the established MARS model to evaluate Pf.Finally, a nominally homogeneous cohesive-frictional slope and a heterogeneous cohesive slope, which are both characterized with different spatial variabilities, are utilized to illustrate the proposed approach.Results showed that the proposed approach can estimate the P_f of the slopes efficiently in spatially variable soils with sufficient accuracy. Moreover, the approach is relatively robust to the influence of different statistics of soil properties, thereby making it an effective and practical tool for addressing slope reliability problems concerning time-consuming deterministic stability models with low levels of P_f.Furthermore, disregarding the multiscale spatial variability of soil properties can overestimate or underestimate the P_f. Although the difference is small in general, the multiscale spatial variability of the soil properties must still be considered in the reliability analysis of heterogeneous slopes, especially for those highly related to cost effective and accurate designs.

Spatial Regression Analysis of Pedestrian Crashes Based on Point-of-Interest Data

Pedestrian safety has recently been considered as one of the most serious issues in the research of traffic safety. This study aims at analyzing the spatial correlation between the frequency of pedestrian crashes and various predictor variables based on open source point-of-interest (POI) data which can provide specific land use features and user characteristics. Spatial regression models were developed at Traffic Analysis Zone (TAZ) level using 10,333 pedestrian crash records within the Fifth Ring of Beijing in 2015. Several spatial econometrics approaches were used to examine the spatial autocorrelation in crash count per TAZ, and the spatial heterogeneity was investigated by a geographically weighted regression model. The results showed that spatial error model performed better than other two spatial models and a traditional ordinary least squares model. Specifically, bus stops, hospitals, pharmacies, restaurants, and office buildings had positive impacts on pedestrian crashes, while hotels were negatively associated with the occurrence of pedestrian crashes. In addition, it was proven that there was a significant sign of localization effects for different POIs. Depending on these findings, lots of recommendations and countermeasures can be proposed to better improve the traffic safety for pedestrians.

Spatial autocorrelation analysis of 13 leading malignant neoplasms in Taiwan: a comparison between the 1995-1998 and 2005-2008 periods

Spatial autocorrelation methodologies, including Global Moran’s I and Local Indicators of Spatial Association statistic (LISA), were used to describe and map spatial clusters of 13 leading malignant neoplasms in Taiwan. A logistic regression fit model was also used to identify similar characteristics over time. Two time periods (1995-1998 and 2005-2008) were compared in an attempt to formulate common spatio-temporal risks. Spatial cluster patterns were identified using local spatial autocorrelation analysis. We found a significant spatio-temporal variation between the leading malignant neoplasms and well-documented spatial risk factors. For instance, in Taiwan, cancer of the oral cavity in males was found to be clustered in locations in central Taiwan, with distinct differences between the two time periods. Stomach cancer morbidity clustered in aboriginal townships, where the prevalence of Helicobacter pylori is high and even quite marked differences between the two time periods were found. A method which combines LISA statistics and logistic regression is an effective tool for the detection of space-time patterns with discontinuous data. Spatio-temporal mapping comparison helps to clarify issues such as the spatial aspects of both two time periods for leading malignant neoplasms. This helps planners to assess spatio-temporal risk factors, and to ascertain what would be the most advantageous types of health care policies for the planning and implementation of health care services. These issues can greatly affect the performance and effectiveness of health care services and also provide a clear outline for helping us to better understand the results in depth.

Regional Integrated Meteorological Forecasting and Warning Model for Geological Hazards Based on Logistic Regression

Information model is adopted to integrate factors of various geosciences to estimate the susceptibility of geological hazards. Further combining the dynamic rainfall observations, Logistic regression is used for modeling the probabilities of geological hazard occurrences, upon which hierarchical warnings for rainfall-induced geological hazards are produced. The forecasting and warning model takes numerical precipitation forecasts on grid points as its dynamic input, forecasts the probabilities of geological hazard occurrences on the same grid, and translates the results into likelihoods in the form of a 5-level hierarchy. Validation of the model with observational data for the year 2004 shows that 80% of the geological hazards of the year have been identified as ＂likely enough to release warning messages＂. The model can satisfy the requirements of an operational warning system, thus is an effective way to improve the meteorological warnings for geological hazards.

GIS-based logistic regression method for landslide susceptibility mapping in regional scale

Landslide susceptibility map is one of the study fields portraying the spatial distribution of future slope failure sus- ceptibility. This paper deals with past methods for producing landslide susceptibility map and divides these methods into 3 types. The logistic linear regression approach is further elaborated on by crosstabs method, which is used to analyze the relationship between the categorical or binary response variable and one or more continuous or categorical or binary explanatory variables derived from samples. It is an objective assignment of coefficients serving as weights of various factors under considerations while expert opinions make great difference in heuristic approaches. Different from deterministic approach, it is very applicable to regional scale. In this study, double logistic regression is applied in the study area. The entire study area is first analyzed. The logistic regression equation showed that elevation, proximity to road, river and residential area are main factors triggering land- slide occurrence in this area. The prediction accuracy of the first landslide susceptibility map was showed to be 80%. Along the road and residential area, almost all areas are in high landslide susceptibility zone. Some non-landslide areas are incorrectly divided into high and medium landslide susceptibility zone. In order to improve the status, a second logistic regression was done in high landslide susceptibility zone using landslide cells and non-landslide sample cells in this area. In the second logistic regression analysis, only engineering and geological conditions are important in these areas and are entered in the new logistic regression equation indicating that only areas with unstable engineering and geological conditions are prone to landslide during large scale engineering activity. Taking these two logistic regression results into account yields a new landslide susceptibility map. Double logistic regression analysis improved the non-landslide prediction accuracy. During calculation of parameters for logistic regres- sion, landslide density is used to transform nominal variable to numeric variable and this avoids the creation of an excessively high number of dummy variables.

Hierarchical Geographically Weighted Regression Model

In spatial analysis, two problems of the scale effect and the spatial dependencehave been plagued scholars, the first law of geography presented to solve the spatialdependence has played a good role in the guidelines, forming the Geographical WeightedRegression (GWR). Based on classic statistical techniques, GWR model has ascertainsignificance in solving spatial dependence and spatial non-uniform problems, but it hasno impact on the integration of the scale effect. It does not consider the interactionbetween the various factors of the sampling scale observations and the numerous factorsof possible scale effects, so there is a loss of information. Crossing a two-stage analysisof “return of regression” to establish the model of Hierarchical Geographically WeightedRegression (HGWR), the first layer of regression analysis reflects the spatial dependenceof space samples and the second layer of the regression reflects the spatial relationshipsscaling. The combination of both solves the spatial scale effect analysis, spatialdependence and spatial heterogeneity of the combined effects.

Geographical Analysis of Lung Cancer Mortality Rate and PM2.5 Using Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth

Exposure to particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) may increase risk of lung cancer. The repetitive and broad-area coverage of satellites may allow atmospheric remote sensing to offer a unique opportunity to monitor air quality and help fill air pollution data gaps that hinder efforts to study air pollution and protect public health. This geographical study explores if there is an association between PM2.5 and lung cancer mortality rate in the conterminous USA. Lung cancer (ICD-10 codes C34- C34) death count and population at risk by county were extracted for the period from 2001 to 2010 from the U.S. CDC WONDER online database. The 2001-2010 Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth dataset was used to calculate a 10 year average PM2.5 pollution. Exploratory spatial data analyses, spatial regression (a spatial lag and a spatial error model), and spatially extended Bayesian Monte Carlo Markov Chain simulation found that there is a significant positive association between lung cancer mortality rate and PM2.5. The association would justify the need of further toxicological investigation of the biological mechanism of the adverse effect of the PM2.5 pollution on lung cancer. The Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth dataset provides a continuous surface of concentrations of PM2.5 and is a useful data source for environmental health research.

Covid-19 in West &East Africa, a Geographical Weighted Regression Exploration with http://mygeoffice.org/

Understanding the dynamics that affect the spread of Covid-19 is critical for the development of government measures to stop and reverse this nowadays disease propagation. Like in any epidemiological study, it is essential to analyze the spatial data to account for the inherent spatial heterogeneity within the data (spatial autocorrelation). This paper uses Geographically Weighted Regression (GWR) to identify the factors that influence the outbreak of Covid-19 in Western and Eastern countries of Africa. The analyses include traditional linear regression (including descriptive statistics, hierarchical clustering and correlations were not forgotten either) to reveal the importance of eight risk factors (population density, median age, aged over 65 years, GDP per capita, cardiovascular death rates, diabetes prevalence, female and male smokers) regarding Covid-19 dissemination. It is believed that this is the first attempt to explore possible causes associated with the spread of the Covid-19 pandemic in these disadvantage countries, where some intriguing clues are presented for further research such as the positive relationship between the financial purchase power of nations and the total number of infected people or the smoker’s gender impact on Covid-19.

Temporal and spatial responses of ecological resilience to climate change and human activities in the economic belt on the northern slope of the Tianshan Mountains, China

In the Anthropocene era,human activities have become increasingly complex and diversified.The natural ecosystems need higher ecological resilience to ensure regional sustainable development due to rapid urbanization and industrialization as well as other intensified human activities,especially in arid and semi-arid areas.In the study,we chose the economic belt on the northern slope of the Tianshan Mountains(EBNSTM)in Xinjiang Uygur Autonomous Region of China as a case study.By collecting geographic data and statistical data from 2010 and 2020,we constructed an ecological resilience assessment model based on the ecosystem habitat quality(EHQ),ecosystem landscape stability(ELS),and ecosystem service value(ESV).Further,we analyzed the temporal and spatial variation characteristics of ecological resilience in the EBNSTM from 2010 to 2020 by spatial autocorrelation analysis,and explored its responses to climate change and human activities using the geographically weighted regression(GWR)model.The results showed that the ecological resilience of the EBNSTM was at a low level and increased from 0.2732 to 0.2773 during 2010–2020.The spatial autocorrelation analysis of ecological resilience exhibited a spatial heterogeneity characteristic of"high in the western region and low in the eastern region",and the spatial clustering trend was enhanced during the study period.Desert,Gobi and rapidly urbanized areas showed low level of ecological resilience,and oasis and mountain areas exhibited high level of ecological resilience.Climate factors had an important impact on ecological resilience.Specifically,average annual temperature and annual precipitation were the key climate factors that improved ecological resilience,while average annual evapotranspiration was the main factor that blocked ecological resilience.Among the human activity factors,the distance from the main road showed a negative correlation with ecological resilience.Both night light index and PM2.5 concentration were negatively correlated with ecological resilience in the areas with better ecological conditions,whereas in the areas with poorer ecological conditions,the correlations were positive.The research findings could provide a scientific reference for protecting the ecological environment and promoting the harmony and stability of the human-land relationship in arid and semi-arid areas.

A Spatial Epidemiology Case Study of Coronavirus (COVID-19) Disease and Geospatial Technologies

Spatiotemporal pattern analysis provides a new dimension for data interpretation due to new trends in computer vision and big data analysis. The main aim of this study was to explore the recent advances in geospatial technologies to examine the spatiotemporal pattern of COVID-19 at the Public Health Unit (PHU) level in Ontario, Canada. The spatial autocorrelation results showed that the incidence rate (no. of confirmed cases per 100,000 population–IR/100K) was clustered at the PHU level and found a tendency of clustering high values. Some PHUs in Southern Ontario were identified as hot spots, while Northern PHUs were cold spots. The space-time cube showed an overall trend with a 99% confidence level. Considerable spatial variability in incidence intensity at different times suggested that risk factors were unevenly distributed in space and time. The study also created a regression model that explains the correlation between IR/100K values and potential socioeconomic factors.

石家庄暴雨时空分布特征及灾情评估

基于石家庄市2015—2021年暴雨洪涝灾情资料数据,以及17个国家站及268个区域自动气象站数据,采用气候统计诊断方法分析了石家庄暴雨时空分布的气候特征,并利用灰色关联分析及逐步回归方法,建立了石家庄市暴雨灾情评估及预评估模型.结果表明:①石家庄暴雨频次及强度随时间呈递增趋势,暴雨强度年际变化增大且极端性增强.②石家庄西北部暴雨频次多、强度大,西南部暴雨频次相对较少,但强度最大,其中平山、井陉为大暴雨、特大暴雨高发区,复杂的地理环境使该地区发生暴雨洪涝灾害的风险增加.③由灰色关联分析方法确定的暴雨灾情等级正确率83.33%,能够反映实际暴雨灾情等级,且有利于客观区分同一等级内暴雨灾情大小.④基于气象因子,利用逐步回归方法建立的暴雨灾情评估及预评估模型正确率可达68.75%.

电子商务发展的空间分布及其对实体经济的影响

近年来,电子商务的高速发展对我国的经济发展起到了重要的推动作用。探究其对不同区域经济以及实体经济的影响作用有利于其持续健康的发展。研究采用莫兰指数对我国电子商务的空间分布特征进行自相关分析,并采用回归模型分析法分析了电子商务对商用房价的影响,进而分析其对实体经济空间分布的影响。结果表明,我国电子商务从2011-2020年间的全局莫兰指数均大于0,且最高达到了0.036。商/住房价格比与电子商务水平的三种回归模型结果系数均小于0,即均为负相关关系。说明电子商务的发展对实体零售业的发展起到一定的抑制作用,城市商业传统的集聚态势在电子商务发展的冲击下会大大减弱。为推进电子商务与实体经济的共同发展提供了有效的参考依据。

Exploration of spatial and temporal characteristics of PM2.5 concentration in Guangzhou, China using wavelet analysis and modified land use regression model

This article attempts to detail time series characteristics of PM2.5 concentration in Guangzhou(China)from 1 June 2012 to 31 May 2013 based on wavelet analysis tools,and discuss its spatial distribution using geographic information system software and a modified land use regression model.In this modified model,an important variable(land use data)is substituted for impervious surface area,which can be obtained conveniently from remote sensing imagery through the linear spectral mixture analysis method.Impervious surface has higher precision than land use data because of its sub-pixel level.Seasonal concentration pattern and day-by-day change feature of PM2.5 in Guangzhou with a micro-perspective are discussed and understood.Results include:(1)the highest concentration of PM2.5 occurs in October and the lowest in July,respectively;(2)average concentration of PM2.5 in winter is higher than in other seasons;and(3)there are two high concentration zones in winter and one zone in spring.

考虑抵达时间成本的道路交通事故风险评估方法

道路交通事故频发,给生命财产造成重大损失,给社会生活带来重大影响。现有针对道路交通事故风险的研究未建立有效的道路网络模型,难以准确描述交通事故风险在道路上的传播特点,评估准确度不高。基于此,提出了一种基于抵达时间成本的网络地理加权回归方法,并利用某县级市2018—2020年的道路、交通违法、交通事故、城市POI等数据开展实验,结果表明,基于抵达时间成本的网络地理加权回归方法融合了交通事故风险在道路上的传播性质,显著降低了评估误差,能够有效评估道路交通事故风险及其影响因素;市中心区域道路交通事故高风险区域主要集中在车流量较大的道路交会处与部分交通设施尚不完备的道路;各类交通违法数量、城市POI对道路交通事故风险的影响程度不同,且具有很强的空间异质性。

Industrial Carbon Emission Distribution and Regional Joint Emission Reduction:A Case Study of Cities in the Pearl River Basin,China

China’s low-carbon development path will make significant contributions to achieving global sustainable development goals.Due to the diverse natural and economic conditions across different regions in China,there exists an imbalance in the distribution of car-bon emissions.Therefore,regional cooperation serves as an effective means to attain low-carbon development.This study examined the pattern of carbon emissions and proposed a potential joint emission reduction strategy by utilizing the industrial carbon emission intens-ity(ICEI)as a crucial factor.We utilized social network analysis and Local Indicators of Spatial Association(LISA)space-time trans-ition matrix to investigate the spatiotemporal connections and discrepancies of ICEI in the cities of the Pearl River Basin(PRB),China from 2010 to 2020.The primary drivers of the ICEI were determined through geographical detectors and multi-scale geographically weighted regression.The results were as follows:1)the overall ICEI in the Pearl River Basin is showing a downward trend,and there is a significant spatial imbalance.2)There are numerous network connections between cities regarding the ICEI,but the network structure is relatively fragile and unstable.3)Economically developed cities such as Guangzhou,Foshan,and Dongguan are in the center of the network while playing an intermediary role.4)Energy consumption,industrialization,per capita GDP,urbanization,science and techno-logy,and productivity are found to be the most influential variables in the spatial differentiation of ICEI,and their combination in-creased the explanatory power of the geographic variation of ICEI.Finally,through the analysis of differences and connections in urban carbon emissions under different economic levels and ICEI,the study suggests joint carbon reduction strategies,which are centered on carbon transfer,financial support,and technological assistance among cities.

山东烟田烟蚜种群动态与空间分布

烟蚜在山东烟田发生普遍,危害严重。研究山东烟田烟蚜种群动态与空间分布对于提高山东烟田烟蚜预测测报准确率以及有效防控该害虫具有重要意义。试验开展了烟蚜的田间种群消长动态调查,并应用5个聚集度指标(丛生指数I,聚块指标m^(*)/m,久野指标Ca,扩散系数C,负二项分布K值)和2种回归方法(Iwao回归分析法与Toylar幂法则)研究其空间分布特征。结果表明,2020—2021年烟田烟蚜的种群动态均为双峰型曲线:第1个高峰出现在5月下旬至6月上旬,第2个高峰出现在7月上中旬。在垂直分布上,烟草植株上部叶片的烟蚜数量占比(49.07%~70.29%)显著高于中部叶片(27.64%~33.71%)和下部叶片(1.64%~19.85%)。数据分析结果显示,I(1.733 2~42.703 0)>0,m^(*)/m(2.368 3~10.414 2)>1,Ca(1.368 3~9.414 2)>0,C(2.733 2~43.703 0)>1,0<K(0.106 2~0.730 8)<8;在Iwao回归方程中,β(4.578 65)>1,α(0.190 57)>0,在Toylar幂法则回归方程中,lga(0.691 65)>0,b(1.817 05)>1,说明调查期间烟蚜种群呈聚集分布,分布的基本成分是个体群。利用空间分布参数确定了烟蚜的理论抽样数,并提出合理的田间抽样技术,即在烟田内采用“Z”字形取样法,每点选取烟草5~10株、每株烟草调查上中部叶片2~4片。

近30年天山北坡改进型遥感生态指数时空变化及其驱动因素

天山北坡是西北地区重要的生态功能区和重要经济带,受气候变化和人类活动的影响,脆弱的生态环境面临着巨大挑战,但目前对该地区生态环境质量时空变化、特征及驱动力的认识较少。为此,本文利用改进型遥感生态指数(ERSEI)、空间自相关分析、多元线性回归分析和地理加权分析方法揭示天山北坡1990、2000、2010和2020年4个时期生态环境质量时空变化、特征及其驱动因素。结果表明:(1)1990-2020年天山北坡ERSEI先大幅下降后逐渐恢复并持续上升,生态环境质量逐渐改善并趋于稳定,总体呈“东优西劣”的空间格局;(2)天山北坡生态环境对气候变化较为敏感,温度是ERSEI变化的最重要驱动因子;(3)不同驱动因素对ERSEI的影响存在显著的空间异质性,自然因素对局部ERSEI空间分异起主导作用,人为因素则对年际ERSEI变化产生重要影响。这表明气候变化和人类活动显著影响ERSEI变化,未来应科学规划各类经济活动,减缓气候变化效应,提升天山北坡生态安全和环境可持续性。

淮河流域皖西大别山区地形因子与水系结构关联性分析

为深入研究淮河流域皖西大别山区地形因子与水系结构的量化关系,基于ArcGIS对研究区地形因子及水系结构特征参数进行计算与提取,采用多元线性回归方法分析了地形因子与水系结构特征参数之间的关系,得到了针对细度比、河道维系常数、分形维数、平均长度比和流域圆度的5个定量预测模型,并利用全局空间自相关及空间冷热点分析对水系格局及地形特征的空间分布进行了研究。结果表明:研究区地形因子与水系结构特征参数在空间上普遍存在相关关系,相关系数r最高达0.84;5个定量预测模型显著性水平P值均小于0.05,方差膨胀因子均小于10,模型拟合优度较高;研究区水系结构特征参数和地形因子存在“北冷南热”和“北热南冷”的显著空间分异特征。

徐州城市空间扩展特征及驱动因素分析

随着城市的快速发展,城市化进程也在不断加快,为了适应经济社会的飞速发展和人口的增长速度,随之而来的是城市用地增加,为更好地控制城市的空间扩展,研究城市空间扩展和驱动力尤为重要。本文以徐州市建成区1981年、1991年、2001年、2011年和2021年5个年份时相的遥感影像为数据源,利用遥感信息提取技术获取徐州市的城市扩展信息,从数量、形态、重心、方向4个角度分析城市空间扩展特征,并运用回归模型探究徐州城市扩展的主要驱动因素。结果表明:(1)1981—2021年共40年间城市整体扩张强度为26%,年均扩展面积为36.03 km2,其中2001—2011年是城市扩展强度最剧烈的一段时间;(2)城市空间扩展分形维数和紧凑度总体趋于稳定,使得城市空间更加规则,紧凑度得到提高;(3)城市空间扩展总体以东、东南、南方向进行发展,城市重心逐渐向东南方向移动;(4)城市空间扩展幅度明显增加的方位是在东部、南部、东南部,城市空间整体呈现条带状扩展延伸;(5)回归分析结果表明,人口、基础设施和政策规划因素对徐州市城市空间扩展影响较为显著,而经济因素也会对城市的发展产生一定的影响。

数字经济对居民消费潜力的影响效应研究——基于空间杜宾数学模型的实证分析

在全球经济增长放缓的形势下,为尽快恢复经济复苏增长并推动国内大循环,加快实现国内国际双循环发展就离不开对居民有效消费需求的引导及其消费潜力的释放。近年来,以大数据、5G、人工智能为依托的经济数字化转型已深刻影响社会的各个层面,作为拉动国民经济增长重要引擎的消费领域自然也受到其深刻影响。文章基于2017—2021年全国主要99个地级市的面板数据,通过实证分析探讨数字经济发展对居民消费潜力及其各维度的作用效果和溢出效应。结果表明:(1)数字经济发展对居民消费潜力及其消费支出维度、消费收入维度和消费环境维度均有积极作用;(2)数字经济对促进居民消费潜力释放具有正向的空间溢出效应;(3)相对居民消费潜力中的收入和支出维度,数字经济对居民消费环境维度的正向空间溢出效应更加明显。