“We Don’t Equal Even Just One Man”: Gender and Social Control in Conservation Adoption

Women own or co-own approximately half of the farmland in Iowa, United States, yet researchers are only beginning to study these landowners’ social relationships in relation to their land. This study analyzes qualitative data collected in Iowa through a series of meetings hosted by the Women, Food and Agriculture Network (WFAN). I find that social control through exclusion constrains women landowners’ access to information about and implementation of conservation. Specifically, I identify how women landowners experience the social processes of boundary maintenance and othering in land management. These processes create barriers to conservation adoption and maintain gendered agricultural landscapes. The women who participated in WFAN’s conservation programs express their experience of and resistance to dominant narratives as they attempt to create landscape change. These findings highlight the importance of further study of inequality processes and their relation to control of farmland if conservation goals are to be met.     

Gatekeepers,Shareholders, and Evangelists: Expanding Communication Networks of African American Forest Landowners in North Carolina

The Sustainable Forestry and African American Land Retention Program (SFLR) was launched in 2012 to increase adoption of sustainable forestry practices among African American landowners in the southeastern United States to prevent land loss, increase forest health, and build economic assets. One of its main goals was to build communication networks through which African American landowners could obtain and share information about forestry practices and landowner assistance programs independent of public agencies. To measure and examine the growth of these communication networks over a three-year period (2014-2017), we conducted 87 interviews with landowners (24 of whom were interviewed multiple times), SFLR personnel, and Federal and State staff members in North Carolina. We used complementary methods of data gathering and analysis, including social network analysis and qualitative analysis. Our results showed expanding communication networks will be sustained independently of the program over time, although there is still a heavy reliance on program personnel.     

A Road to Conflict: Stakeholder’s and Social Network Analysis of the Media Portrayals of a Social-Environmental Conflict in Bolivia

Society’s understanding of a conflict is mediated by information provided in mass media, for which researchers stress the importance of analyzing media portrays of stakeholders in a conflict. We analyze information from the Bolivian press regarding the construction of a road crossing the Isiboro-Sécure Indigenous Territory and National Park (TIPNIS). Using stakeholder’s and social network analyses, we explore stakeholder’s positions and alliances as represented in the media and contrast it with previous scholarly work. We found that some actors cited as central in scholar analyses of the conflict are largely absent in the media (e.g., private investors, conservationist sector) and that the media tend to present stakeholders as having more homogeneous positions than the academic literature does while also neglecting some important alliances in their account. The media also suggests that Indigenous communities are forging stronger alliances with urban sectors and civil society, alliances not stressed by researchers.     

Location choices of Chinese enterprises in Southeast Asia: The role of overseas Chinese networks

Journal of Geographical Sciences - With the implementation of the “Going out” strategy and the Belt and Road Initiative, China’s investments have become increasingly influential...     

Parallelization of the flow-path network model using a particle-set strategy

High-performance simulation of flow dynamics remains a major challenge in the use of physical-based, fully distributed hydrologic models. Parallel computing has been widely used to overcome efficiency limitation by partitioning a basin into sub-basins and executing calculations among multiple processors. However, existing partition-based parallelization strategies are still hampered by the dependency between inter-connected sub-basins. This study proposed a particle-set strategy to parallelize the flow-path network (FPN) model for achieving higher performance in the simulation of flow dynamics. The FPN model replaced the hydrological calculations on sub-basins with the movements of water packages along the upstream and downstream flow paths. Unlike previous partition-based task decomposition approaches, the proposed particle-set strategy decomposes the computational workload by randomly allocating runoff particles to concurrent computing processors. Simulation experiments of the flow routing process were undertaken to validate the developed particle-set FPN model. The outcomes of hourly outlet discharges were compared with field gauged records, and up to 128 computing processors were tested to explore its speedup capability in parallel computing. The experimental results showed that the proposed framework can achieve similar prediction accuracy and parallel efficiency to that of the Triangulated Irregular Network (TIN)-based Real-Time Integrated Basin Simulator (tRIBS).     

Integration of nighttime light remote sensing images and taxi GPS tracking data for population surface enhancement

The population distribution grid at fine scales better reflects the distribution of residents and plays an important role in investigating urban systems. The recent years have witnessed a growing trend of applying the nighttime light data to the estimation of population at micro levels. However, using the nighttime light data alone to estimate population may cause the overestimation problem due to excessively high light radiance in specific types of areas such as commercial zones and transportation hubs. In dealing with this issue, this study used taxi trajectory data that delineate people’s movements, and explored the utility of integrating the nighttime light and taxi trajectory data in the estimation of population in Shanghai at the spatial resolution of 500 m. First, the initial population distribution grid was generated based on the NPP-VIIRS nighttime light data. Then, a calibration grid was created with taxi trajectory data, whereby the initial population grid was optimized. The accuracy of the resultant population grid was assessed by comparing it with the refined survey data. The result indicates that the final population distribution grid performed better than the initial population grid, which reflects the effectiveness of the proposed calibration process.     

结合云计算与大数据的出租房消防安全监管GIS

我国城镇化进程和经济发展效果卓著,但消防建设规划与城市扩张发展之间并不协调。虽然现有研究已经运用GIS技术到消防安全监管当中,但依然存在信息联动性不强,可视化程度低的问题。为此,本文基于地理信息系统技术对城市开发区内地理信息数据、多源多时态遥感及航空影像、巡查数据、出租房数据等进行有效整合完善,以建立时空地理信息大数据。笔者利用虚拟化、云计算等技术建立出租房消防安全动态监管平台。此平台为管理部门提供了基于地理信息的决策支持,提升了出租房管理的高效性和科学性。系统的建成和应用对我国的出租房管理信息化工作的开展具有重要意义。     

大面积硅微条探测器在轨数据压缩算法的设计与实现

硅微条探测器具有位置分辨高、响应快、低噪声、低功耗等优点,广泛应用在各大加速器试验中,测量粒子径迹.新世纪以来,逐渐应用于空间探测领域.计划中的"悟空"2号暗物质粒子探测卫星的硅微条探测器将至数十万计,将产生海量的原始数据.如何实现探测器快速实时的数据压缩,是其需要解决的一大难题.立足于面向空间应用的硅微条探测器在轨实时压缩算法,算法采用FPGA (Field-programmable Gate Array)搭建流水线结构的方式实现,在提高系统集成度、节省逻辑资源的同时,批量数据处理时最高可将数据压缩率提升至38.4 M通道/s.算法结构具有通用性,设计思想和方案将为"悟空"2号的径迹探测器的研制提供参考.     

Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data

When travelling, people are accustomed to taking and uploading photos on social media websites, which has led to the accumulation of huge numbers of geotagged photos. Combined with multisource information (e.g. weather, transportation, or textual information), these geotagged photos could help us in constructing user preference profiles at a high level of detail. Therefore, using these geotagged photos, we built a personalised recommendation system to provide attraction recommendations that match a user's preferences. Specifically, we retrieved a geotagged photo collection from the public API for Flickr (Flickr.com) and fetched a large amount of other contextual information to rebuild a user's travel history. We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation (the matching process) and candidate ranking. In the matching process, we used a support vector machine model that was modified for multiclass classification to generate the candidate list. In addition, we used a gradient boosting regression tree to score each candidate and rerank the list. Finally, we evaluated our recommendation results with respect to accuracy and ranking ability. Compared with widely used memory-based methods, our proposed method performs significantly better in the cold-start situation and when mining ‘long-tail’ data.