基于TM影像属性和形态特征的土地覆被制图方法

本文以浙江省中南部地区不同时相30m分辨率的2景TM影像为基本数据,采用面向对象的方法实现了研究区的土地覆被制图。首先,在eCognition软件中采用多尺度分割算法,以光谱信息、纹理特征、几何特征等实现研究区的对象分割,使分割后的对象边界与实际地物边界尽量保持一致,通过建立多层次地物特征规则,进行最优分割尺度下的遥感多层次识别分类;然后,分析可用于分类的属性特征和形态特征,通过对这些特征的统计值对比分析,选取了对象的紧致度、长宽比、MNDWI、LBV等特征构建了决策树模型,实现了研究区1：25万的土地覆被分类;最后,采用目视解译和野外样本2种方式对分类结果进行精度验证,其中,目测随机样点评价得到的总体精度为87.66%,野外样本点评价得到的总体精度为83.38%。研究表明：面向对象的分类方法不仅具有较高的精度,而且图斑与实际地物边界能较好地吻合,很好地避免了混合像元误分的现象,同时能消除像元分类的“椒盐现象”。     

经济地理学新的思维范畴

全球经济一体化与信息时代的到来，给经济地理学带来了新的变化，加之实践的检验，经济地理学由过去那些“区位论”，“生产布局”相应地发展到空间理论和信息理论，重在分析机制因素，时空的变化也更加深刻了，本学科比较综合，具有广阔的区域空间，复杂的内涵 ，其学科的创新在于在时空耦合度上使经济和社会同人口，资源，生态环境之间保持和谐，高效优化有序的发展，逐步建立具有我国特色的资源节约型的社会经济消费体系。着重分析了经济地理学新的思维范畴与研究趋势，强调用科学方法来检验以前的结论，用统计方法（层面分析）来解释数量关系，用GIS的分析法来印证空间布局的合理性。     

一种基于机器学习算法的岩性填图方法

通过野外地质调查与机器学习方法的有机融合,提出了一种基于梯度提升决策树算法的岩性单元填图方法.研究以多龙矿集区为模型试验区,选择1:5万勘查地球化学数据为基础预测数据,以1:5万区域地质图为参考,进行基于梯度提升决策树算法的岩性预测填图模型试验.首先选择研究区内小范围空白区开展野外填图,建立原始数据集并初步构建岩性单元...     

近年来我国城市地理学主要研究领域的新进展

本文对改革开放以来中国城市地理学研究的新进展作了综述，尤其注重新领域的进展和主要观点的概括。本文涉及的主要领域包括：（１）城市化研究；（２）城市发展方针的研究；（３）城市体系研究；（４）城市内部空间结构研究。最后，在研究展望中作者指出，在社会主义市场经济体制下，应开展城市土地和住房以及国际大都市的研究。     

Utility Efficient Frontier: An Application in the Oil and Gas Industry

Current practice shows that the use of portfolio and utility theory is very low among petroleum companies. This article advocates the use of both portfolio theory and utility theory as decision-making tools to improve performance of oil and gas companies. We introduce a model that can be practically used and applied in the oil and gas industry. This model generates an optimized, efficient portfolio and, at the same time, enables the decision maker to incorporate his risk attitude and policy. This can only be done by combining both the portfolio theory and utility theory through an approach called the utility mean-variance model. A typical oil portfolio optimization problem is investigated by applying both portfolio and utility theories. Through the utility mean-variance model, an efficient frontier that captures decision maker risk attitude is achieved.     

Deriving rules from activity diary data: A learning algorithm and results of computer experiments

 Activity-based models consider travel as a derived demand from the activities households need to conduct in space and time. Over the last 15 years, computational or rule-based models of activity scheduling have gained increasing interest in time-geography and transportation research. This paper argues that a lack of techniques for deriving rules from empirical data hinders the further development of rule-based systems in this area. To overcome this problem, this paper develops and tests an algorithm for inductively deriving rules from activity-diary data. The decision table formalism is used to exhaustively represent the theoretically possible decision rules that individuals may use in sequencing a given set of activities. Actual activity patterns of individuals are supplied to the system as examples. In an incremental learning process, the system progressively improves on the selection of rules used for reproducing the examples. Computer experiments based on simulated data are performed to fine-tune rule selection and rule value update functions. The results suggest that the system is effective and fairly robust for parameter settings. It is concluded, therefore, that the proposed approach opens up possibilities to derive empirically tested rule-based models of activity scheduling. Follow-up research will be concerned with testing the system on empirical data. Received: 31 January 2001 / Accepted: 13 September 2001     

Calculation of Uncertainty in the Variogram

There are often limited data available in early stages of geostatistical modeling. This leads to considerable uncertainty in statistical parameters including the variogram. This article presents an approach to calculate the uncertainty in the variogram. A methodology to transfer this uncertainty through geostatistical simulation and decision making is also presented.The experimental variogram value for a separation lag vector h is a mean of squared differences. The variance of a mean can be calculated with a model of the correlation between the pairs of data used in the calculation. The data here are squared differences; therefore, we need a measure of a 4-point correlation. A theoretical multi-Gaussian approach is presented for this uncertainty assessment together with a number of examples. The theoretical results are validated by numerical simulation. The simulation approach permits generalization to non-Gaussian situations.Multiple plausible variograms may be fit knowing the uncertainty at each variogram point, . Multiple geostatistical realizations may then be constructed and subjected to process assessment to measure the impact of this uncertainty.     

Differences in trace metal concentrations among fluvial morphologic units and implications for sampling

 This study examines the segregation of trace metals within and between fluvial morphologic units in sand-sized and finer bed sediments in a cobble bed stream. The types of fluvial morphologic units sampled are low gradient riffles, high gradient riffles, glides, eddy drop zones, lateral scour pools, attached bars, and detached bars. Three to nine samples were collected from ten of each type of morphologic unit. All 12 metals show significantly different concentrations between some morphologic units in sediments smaller than 2 mm. Eddy drop zones and attached bars consistently have the highest metal concentrations, while low gradient riffles, high gradient riffles, and glides typically have the lowest concentrations. Metals showing the greatest between-unit variability are Al, Cr, Fe, Cu, and Ti, followed by Co, Mn, and Pb, while Mg, Mn, V, and Zn display relatively few differences between units. Lateral and longitudinal variations of metals within units are not significant, and there was no consistent, predictable variation in metal concentrations with distance downstream. Results indicate that metal studies in other gravel- and cobble-bed streams should include a reconnaissance survey to determine variations between morphologic units, stratify sampling by morphologic unit, and analyze spatial autocorrelation to determine sample spacing. Received: October 1997 · Accepted: 23 March 1998     

Using simulated data to investigate the spatial patterns of obesity prevalence at the census tract level in metropolitan Detroit

Obesity is a serious public health problem in the United States. It is important to estimate obesity prevalence at the local level to target programmatic and policy interventions. It is challenging, however, to obtain local estimates of obesity prevalence because national health surveys such as the Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS) are not designed to produce direct estimates at the local levels (e.g. census tracts) due to small population samples and the need to preserve individual confidentiality. In this study we address the problem of estimating local obesity prevalence rates by implementing a spatial microsimulation modeling technique to proportionally replicate the demographic characteristics of BRFSS respondents to census tract populations in metropolitan Detroit. Obesity prevalence rates are examined for high and low spatial clusters and studied in relation to the U.S. Department of Agriculture's (USDA) measures of low-income neighborhoods and local food deserts and CDC's measure of healthy and less healthy food environments currently used to target obesity reduction initiatives. This study found that obesity prevalence was largely clustered in the City of Detroit extending north into contiguous suburbs. The spatial patterns of highest obesity prevalence tracts were most similarly aligned with USDA-defined low-income tracts and CDC's less healthy food tracts. The locations of USDA's food desert tracts rarely overlapped with the highest obesity prevalence tracts. This study demonstrated a new methodology by which to assess local areas in need of future obesity interventions.     

Structural and nutritional heterogeneity of riparian vegetation in Patagonia (Argentina) in relation to seasonal grazing by sheep

Riparian meadows of southern Patagonia are temporally and spatially heterogeneous habitats. They are thought to play a key role in regulating the dynamics of arid grazed steppes of the region. We conducted a 2-year study with sheep to determine the influence of two grazing conditions (deferment and spring grazing) on structural and nutritional parameters of three vegetation types apparently associated with a soil moisture gradient in a riparian meadow in southern Santa Cruz (Argentina). Spring deferment allowed forage accumulation in very moist and intermediate vegetation types by the beginning of the summer and had no detrimental impact on forage quality. Deferment had no effect on biomass accumulation of the drier vegetation type at our study site. Structural attributes of the vegetation appeared to affect spatial grazing patterns of sheep. Soil-related patchiness, rather than sheep grazing, appeared to control vegetation structure and nutritional value. The proportion of more mesic plant communities in riparian habitats of Patagonia could determine the outcome of plant–animal interactions in these systems. Practical recommendations of grazing capacity in paddocks, or more theoretical considerations of ecosystem dynamics of the Patagonian steppes need to explicitly consider the internal patchiness of riparian habitats.