地铁对服务业集聚的影响 ——以广州市为例

以广州市为例,基于POI数据,运用SAR模型识别城市地铁开通对服务业集聚的影响.研究发现:服务业集聚具有显著的空间依赖性特征,本地服务业集聚会受到周边地区服务业集聚的影响;开通地铁对广州市服务业空间集聚产生显著的正向影响,可提高城区的服务业集聚水平,但这种集聚效应因行业不同而有所差异;地铁开通对批发与零售业、住宿与餐饮...     

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Predicting socioeconomic conditions from satellite sensor data in rural developing countries: A case study using female literacy in Assam,India

Social data from census and household surveys provide key information for monitoring the status of populations, but the data utility can be limited by temporal gaps between surveys. Recent studies have pointed to the potential for remotely sensed satellite sensor data to be used as proxies for social data. Such an approach could provide valuable information for the monitoring of populations between enumeration periods. Field observations in Assam, north-east India suggested that socioeconomic conditions could be related to patterns in the type and abundance of local land cover dynamics prompting the development of a more formal approach. This research tested if environmental data derived from remotely sensed satellite sensor data could be used to predict a socioeconomic outcome using a generalised autoregressive error (GAR_err) model. The proportion of female literacy from the 2001 Indian National Census was used as an indicator of socioeconomic conditions. A significant positive correlation was found with woodland and a significant negative correlation with winter cropland (i.e., additional cropping beyond the normal cropping season). The dependence of female literacy on distance to nearest road was very small. The GAR_err model reduced residual spatial autocorrelation and revealed that the logistic regression model over-estimated the significance of the explanatory covariates. The results are promising, while also revealing the complexities of population–environment interactions in rural, developing world contexts. Further research should explore the prediction of socioeconomic conditions using fine spatial resolution satellite sensor data and methods that can account for such complexities.     

华山树木年轮年表的建立

本文以秦岭东部的华山为例，论述了建立常规标准化、差值、自回归标准化这三种树木年轮年表的途径，指出了在半湿润地区利用这种途径建立树木年轮气候学年表的必要性，并进一步分析了三种年表对气候要素的响应及其差异。在取样环境较复杂的地点，建立多种年表有利于深入探讨气候要素与树木生长之间的关系，有推广的价值。     

安塞县退耕还林工程与农业生态经济社会系统协同效应的分析——基于VAR模型的实证分析

针对黄土丘陵区退耕还林工程与农业生态经济社会系统协同性研究滞后的现实,运用向量自回归模型(VAR),通过对安塞县1995–2014年相关数据的分析,明确了其协同效应。退耕还林工程结果表明:退耕还林工程对农业生态经济社会系统产生作用的同时,农业生态经济社会系统的改变也反作用于退耕还林工程,明确了退耕还林工程与农业生态经济社会系统之间存在协同互馈的效应。在这一协同过程中,退耕还林工程对农业生态系统的贡献率达34%,明显高于退耕对农业经济系统、农业社会系统的贡献率;农业经济系统对退耕还林工程的影响程度最为明显,贡献率最高点达55.3%且常年稳定,这些都与现实状况相一致。基于此,为了推进退耕还林工程与农业生态经济社会系统的优化耦合,需要在增强生态功能基础上,通过发展碳汇产业促使生态功能的经济显化,构建以提高区域内资源的有效利用率为核心的农业产业–资源链。     

Pricing Weather Derivatives Index based on Temperature: The Case of Bahir Dar,Ethiopia

In this paper we present a stochastic model for daily average temperature to calculate the temperature indices upon which temperature-based derivatives are written. We propose a seasonal mean and volatility model that describes the daily average temperature behavior using the mean-reverting Ornstein-Uhlenbeck process. We also use higher order continuous-time autoregressive process with lag 3 for modeling the time evolution of the temperatures after removing trend and seasonality. Our model is fitted to 11 years of data recorded, in the period 1 January 2005 to 31 December 2015, Bahir Dar, Ethiopia, obtained from Ethiopia National Meteorological Services Agency. The analytical approximation formulas are used to price heating degree days (HDD) and cooling degree days (CDD) futures. The suggested model is analytically tractable for derivation of explicit prices for CDD and HDD futures and option. The price of the CDD future is calculated, using analytical approximation formulas. Numerical examples are presented to indicate the accuracy of the method. The results show that our model performs better to predict CDD indices.     

城市供水管网健康监测与诊断(Ⅰ)——用户流量的预测

供水管网运行状态的准确把握对供水管网的健康监测和健康诊断至关重要,而供水管网运行状态准确把握的关键则在于对用户流量的准确把握。本文提出了供水管网用户流量预测的两个方法,EMD分解与自回归模型相结合的方法和逐步回归方法与自回归模型相结合的方法,文中通过一实际供水管网中某用户流量的预测分析并与实际监测值的比较,验证了两个方法的有效性。研究使得城市供水管网健康的在线监测和在线诊断成为可能。     

IPCC AR6报告解读：未来的全球气候——基于情景的预估和近期信息

依据IPCC第六次评估报告（AR6）第一工作组报告第四章的内容,对未来全球气候的预估结果进行解读。报告对21世纪全球表面气温、降水、大尺度环流和变率模态、冰冻圈和海洋圈的可能变化进行了系统评估,并对2100年以后的气候变化做了合理估计。评估指出全球平均表面气温将在未来20年内达到或超过1.5℃,平均降水也将增加,但随季节和区域而异,同时变率将增大。大尺度环流和变率模态受内部变率影响较大。到21世纪末,北冰洋可能出现无冰期;全球海洋会继续酸化,平均海平面将持续上升,百年内上升幅度依赖不同排放情景,都在2100年后继续升高。在最新的评估中采用多种约束方法,减小了预估不确定性的范围。AR6对于低排放情景以及“小概率高增暖情节”的关注为应对气候变化提供了更多、更完整的信息。综合报告的评估结果指出,未来需要进一步减小区域,特别是季风区气候预估的不确定性,并从科学研究和模式发展两方面加强我国气候预估能力的建设。     

建筑物沉降规律的综合时序分析

建筑物的沉降监测数据序列具有趋势变化和随机变化的特点，本文针对两种变化项的特点分别建立相应的数学模型，再将其组合起来建立综合数学模型，从总体上把握沉降数据序列的变化规律。实例计算分析表明，此方法具有较高的拟合精度和预测能力，具有一定的应用参考价值。     

Crytic period analysis model of hydrological process and its application

It is difficult to analyse the crytic period of the hydrological process, because hydrological time series is probably characterized by heteroscedasticity. To find out the crytic period, a model is constructed as follows: (1) after using zero‐mean transformation for the data, to do Augmented Dickey–Fuller stationary test for the sequence, to build the corresponding AR(p) model and then to do ARCH effects test and white noise test for residual series; (2) for those time series that cannot pass through ARCH test, using logarithm transformation to reduce the heteroscedasticity, and then to redo step (1) until they pass through ARCH test and stationary test; (3) using periodogram analysis to determine all the possible the prime periods and further to put forward three kinds of tests to determine significance level of those prime periods. As examples, the hydrological processes of streamflow from 1784 to 1997 for the gauging stations of Alaer and Xinquman along Tarim River are analysed. After reducing their heteroscedasticity, AR(4) and AR(2) models are developed, respectively. Our results show that the streamflows from the two gauging stations have the same cryptic period of 42·7 years. Furthermore, the reliability for the crytic period model is testified by variance analysis, which shows that the crytic period model is useful and reliable. Copyright © 2009 John Wiley & Sons, Ltd.