新时期区域综合规划中的“协调”问题初探——基于京津冀都市圈区域综合规划的思考

适应国家对区域规划日益重视的趋势,总结我国“十一五”规划区域规划项目——京津冀都市圈区域规划的编制经验,实证研究与理论研究相结合,认为“协调”是区域规划工作的精髓。阐明了区域规划应在三个阶段,即前期研究阶段、研究成果完成阶段以及规划初稿阶段进行协调;协调的对象包括各专业规划、高层次区域（空间）规划和地方规划等,即上、下、横向三个层次的规划;协调内容包括战略性内容如规划目标、规划内容尤其是探索性内容、具体项目与对策措施等方面,即从宏观战略到具体方案等不同层次的内容。     

Exploring urban taxi ridership and local associated factors using GPS data and geographically weighted regression

Taxi is a core component of urban transit systems. Since they can provide more time-saving and convenient service than many other transit options, taxis have a certain passenger base. The analysis of taxi ridership can be used to better understand the travel mobility of passengers and the traffic structure of urban areas. In previous studies, taxi trajectory data have been widely used, especially in exploring taxi ridership, and point-of-interest (POI) data are usually used to evaluate the land-use type of a certain sub-district. On the basis of preceding research, this paper uses taxi trajectory data within the long time scale of one week. Five traffic factors are taken into consideration: pick-ups, drop-offs, and the ratio of pick-ups to drop-offs, pick-up probability and drop-off probability. The research model is divided into weekdays and weekends. For the calculation of probabilities, an index termed the Area Crossing Index is proposed to reflect the taxi cardinality and accessibility of a region. At the same time, POI and demographic data are used as explanatory variables. In this study, we also take the business hours of POIs into consideration. In order to explore the ridership in each hour, hierarchical clustering is used to determine the similarity characteristics of hourly dependent variables. Then, stepwise linear regression is used to screen and evaluate coefficients without collinearity. Finally, geographically weighted regression is adopted to evaluate spatial variability, and the coefficients of common explanatory variables on weekdays and weekends are examined. At the end of this paper, the causes of common explanatory factors on weekdays and weekends for each traffic factor are discussed. This paper also analyzes ridership by combining all the results of dependent variables and proposes some suggestions for taxi scheduling.     

地理信息共享管理环境研究

地理信息共享是地理信息系统领域的研究热点之一。地理信息共享环境由管理环境、标准环境和技术环境所组成。自地理信息共享环境的概念提出以来,对地理信息共享技术环境和标准环境研究较多,而对地理信息共享管理环境仅有较少的关注。本文在对地理信息共享环境探讨的基础上,提出了地理信息共享管理环境的概念,较为详尽地论述了地理信息共享管理环境的构成及其构成要素之间的关系。     

Are new institutional economics enough? Promoting photovoltaics in India's agricultural sector

Solar photovoltaic (PV) technology can bring emissions-free electricity to rural areas without access to the grid. Currently, policymakers are seeking ways to expand markets for off-grid PV from solar home systems to productive uses in order to enhance income generation and contribute to social development. This paper explores the role of the state in promoting such market growth. As a start, the state must improve institutions, thus echoing popular recommendations based in new institutional economic theory (NIE) that are currently touted by the World Bank and other large development agencies. Yet, NIE's framework may be insufficient because it fails to offer viable solutions to overcoming political barriers that contribute to technological path dependency. A case study in Punjab, India, where an agricultural PV water pumping program showed promising signs of successful market penetration in its first 3 years of operation, illustrates how the NIE's shortcomings manifest themselves in practice. The program was beginning to penetrate market entry for PV pumps via competition between PV providers. However, the government of India achieved this feat by providing subsidies to wealthy farmers, a move that is frowned upon by the NIE school. Based on findings from the case study, states should look beyond NIE's framework to expand and improve PV markets in productive uses by accounting for political constraints, assessing how PV technology can improve development goals, and cultivating locally appropriate service delivery models.     

A data-augmentation method for infectious disease incidence data from close contact groups

A broad range of studies of preventive measures in infectious diseases gives rise to incidence data from close contact groups. Parameters of common interest in such studies include transmission probabilities and efficacies of preventive or therapeutic interventions. We estimate these parameters using discrete-time likelihood models. We augment the data with unobserved pairwise transmission outcomes and fit the model using the EM algorithm. A linear model derived from the likelihood based on the augmented data and fitted with the iteratively reweighted least squares method is also discussed. Using simulations, we demonstrate the comparable accuracy and lower sensitivity to initial estimates of the proposed methods with data augmentation relative to the likelihood model based solely on the observed data. Two randomized household-based trials of zanamivir, an influenza antiviral agent, are analyzed using the proposed methods.     

Urban planning indicators,morphology and climate indicators: A case study for a north-south transect of Beijing,China

Eleven sites, representing different urban morphologies across central Beijing, are used to simulate urban heat island effects and explore the relationship between urban planning indicators and climate indicators such as daily maximum and minimum surface temperatures. The results indicate that mesoscale urban planning indicators can explain the majority of the urban climate differences among the sites. For example, green cover ratio and floor area ratio can explain 94.47–98.57% of the variance for daily maximum surface temperature, green cover ratio and building height can explain 98.94–99.12% of the variance for daily minimum surface temperature, and floor area ratio, green cover ratio and building density together can explain 99.49–99.69% of the variance for time of peak surface temperature. Furthermore, green cover ratio is identified as the most significant urban planning indicator affecting the urban thermal environment.     

Distribution,abundance and spatial variability of microplastic pollution on the surface of Lake Superior

In 2014, 94 paired neuston net samples (0.5 mm mesh) were collected from the surface waters of Lake Superior. These samples comprise the most comprehensive surface water survey for microplastics of any of the Great Lakes to date, and the first to employ double net trawls. Microplastic abundance estimates showed wide variability, ranging between 4000 to more than 100,000 particles/km² with most locations having abundances between 20,000 to 50,000 particles/km². The average abundance in Lake Superior was ~30,000 particles/km² which was similar to previous estimates within this Laurentian Great Lake and suggests a total count of more than 2.4 billion (1.7 to 3.3 billion, 95% confidence interval) particles across the lake’s surface. Distributions of plastic particles, characterized by size fraction and type, differed between nearshore and offshore samples, and between samples collected in the eastern versus western portion of the lake. Most of the particles found were fibers (67%), and most (62%) were contained in the smallest classified size fraction (0.50–1 mm). The most common type of polymer found was polyethylene (51%), followed by polypropylene (19%). This is consistent with global plastics production and results obtained from other studies. No statistically significant difference was detected between the paired net samples, indicating that single net sampling should produce a representative estimate of microplastic particle abundance and distribution within a body of water.     

The role of spatial heterogeneity in detecting urban slums

Satellite images allow characterizing and monitoring urban slums. Yet the urban landscape as a complex geographic system is composed of hierarchical patterns and discrete objects in a spatial and temporal continuum with different scales and anisotropy which can only be estimated from image snapshots. Understanding the spatial heterogeneity of slums in terms of scale and anisotropy from discrete image pixels is nontrivial and has not been explicitly addressed by image-based studies detecting slums, where scale and direction in characterizing slum features are commonly done by trial and error. This study addresses this gap by analyzing the impact of scales and anisotropy detected in the scale space and frequency domain for the calculation of texture indices that ultimately govern the detection of slums. Employing case studies of three cities with a large portion of slum population and for which we have very high resolution satellite imagery, we identify the characteristic scales of slum and formal built-up areas. Results show that the characteristic scales correspond with the optimal grain size to obtain image texture features for detecting slums, while the directional spectral energy at the pixel level identifies characteristic directions. Thus texture indices calculated at the characteristic scale and along the characteristic directions of slum patterns improve the efficiency in feature extraction and classification of slums, where optimizing the scale has a higher impact on the detection of slums than choosing the optimal directions. This study provides a framework for scientifically selecting optimal scales and directions for slum mapping studies. The framework is recommended to be tested for more general applications in land surface characterization and classification especially by using high order texture indices.     

Calibrating spatial interaction models from GPS tracking data: An example of retail behaviour

Global Positioning System (GPS) technology has changed the world. We now depend on it for navigating vehicles, for route finding and we use it in our everyday lives to extract information about our locations and to track our movements. The latter use offers a potential alternative to more traditional sources of movement data through the construction of trip trajectories and, ultimately, the construction of origin-destination flow matrices. The advantage of being able to use GPS-derived movement data is that such data are potentially much richer than traditional sources of movement data both temporally and spatially. GPS-derived movement data potentially allow the calibration of spatial interaction models specific to very short time intervals, such as daily or even hourly, and for user-specified origins and destinations. Ultimately, it should be possible to calibrate continuously updated models in near real-time. However, the processing of GPS data into trajectories and then origin-destination flow matrices is not straightforward and is not well understood. This paper describes the process of transferring GPS tracking data into matrices that can be used to calibrate spatial interaction models. An example is given using retail behaviour in two towns in Scotland with an origin-constrained spatial interaction model calibrated for each day of the week and under different weather conditions (normal, rainy, windy). Although the study is small in terms of individuals and spatial context, it serves to demonstrate a future for spatial interaction modelling free from the tyranny of temporally static and spatially predefined data sets.     

Voting for Trump and the electoral mosaics of US metropolitan areas: Exploring changing patterns of party support by neighborhood

The socio-spatial structure of US metropolitan areas is the foundation of their electoral geographies: political parties and their candidates draw their support from separate groups within society whose spatial segregation is reproduced in voting patterns. As a consequence, when there are changes in a party's support base these should be reflected in its electoral landscape. The extent of such changes is explored here in analyses of precinct-scale voting patterns at the 2008, 2012 and 2016 US presidential elections across 373 SMSAs. Eight types of electoral mosaics are identified using a cluster analysis of those SMSAs and their distribution explored in the context of their socio-economic and -demographic characteristics. Of the eight types, the SMSAs in four showed little change in the voting profiles of their precincts between 2012 (Obama's second victory) and 2016 (when Trump won in the Electoral College but lost the popular vote); SMSAs in the other four types experienced considerable change, with many more rustbelt SMSA precincts delivering a Republican landslide at the latter contest.