A Comparison of Public Lands and Farmlands for Grassland Bird Conservation

Midwestern states have invested extensively in grasslands for wildlife conservation, yet these public lands make up a minority of grassland habitat. How effective are public grasslands, relative to private lands, for conserving native songbird populations? I compare private and public lands in southern Minnesota using bird survey data from Conservation Reserve Program (CRP) fields and public lands and assessing fragmentation in a GIS. Bird abundance and diversity were greater on CRP lands. Vegetation composition, field isolation, and field size appear to explain differences in bird counts. Land cover data show that grassland habitat on public lands is scarce and widely scattered. The CRP provides more, and here better, habitat for grassland birds. Funding partly explains this disparity. Trends in farm set‐aside program rules and distribution, which can be vary greatly over time, will strongly influence the success or failure of biodiversity conservation in this region.     

灵丘黑鹳生境的保护与管理

山西省灵丘县具有很好的黑鹃生存环境,征我凼不仪是黑鹳主要的栖息、繁殖地,而且还是我国唯一的越冬地。分析了灵丘黑鹳的生活习性、生境成因,针对黑鹳牛境保护管理中存在的问题,提出了几点保护建议。     

A Geographic Approach for Teaching about Terrorism

Geography is in a unique position to address the topic of terrorism both inside and outside the classroom and has much to offer in terms of crafting effective measures to counter some of the most pressing threats to international security. As little consideration has been given to teaching about this important subject matter at the university level, this article presents an upper-level special topics case study course offered on terrorism. A capstone project that required students to apply course concepts to a specific terrorist organization is examined and the article concludes with a discussion of the limitations of this pedagogical approach.     

Spatial Differentiation Pattern of Habitat Quality and Mechanism of Factors Influencing in Resource-based Cities: A Case Study of Tangshan City,China

The degradation of the eco-environment has been a challenge for sustainability in resource-based cities around the world. Although much attention has been drawn to this issue, few insights have been acquired regarding the spatial differentiation and mechanism of the factors influencing habitat quality in resource-based cities from the perspective of the interactions of natural and human factors. Using Tangshan City as a case study, this paper evaluates habitat quality by integrating Ecosystem Service Value Assessment and the InVEST-HQ model, identifies the spatial distribution of Tangshan’s habitat quality with spatial auto-correlation, and explores the influencing factors and their mechanism of influence on the spatial differentiation with the geographical detector model and Space production theory. The results show that: (1) The total value of the habitat quality in Tangshan City in 2019 was 3.45×10 ¹⁰ yuan, and the habitat quality value was 24435.05 yuan ha ^?1. The habitat quality value presents a clustered distribution pattern of “hot in the north and the south, cold from the center to the west”. (2) On the county scale, Qianxi County had the best habitat quality and Lubei District had the worst habitat quality; Shangying Township had the highest average habitat quality and Kaiping Street had the lowest average habitat quality in the township unit. (3) The results of geographical detectors show that natural environmental conditions are the important basic factors affecting the spatial differentiation of habitat quality in Tangshan City, while urbanization and industrialization factors are the most important external forces driving the spatial differentiation of habitat quality. The contributions of average elevation, average slope, raw material industrial density, and population density to the spatial differentiation of habitat quality are all above 0.40. The interactions of any two factors on habitat quality are enhanced. Areas with concentrated populations, rich industrial resources, and convenient transportation become low-value habitat quality areas; while areas with beautiful landscape patterns, abundant precipitation, and a comfortable climate become high-value habitat quality areas. Space production theory can be used to explain the mechanism of the formation of the spatial differentiation of habitat quality.     

气候变化下基于物种与植物耦联机制的小白额雁(Anser erythropus)潜在生境及适宜性研究--以长江流域中下游为例

气候变化和人类活动通过改变物种生境而影响物种多样性。小白额雁是长江流域中下游的一种具有较高生态价值的食草型濒危候鸟,受气候变化和人类活动威胁。本文以小白额雁为代表性物种,定量分析了气候变化对长江流域中下游候鸟潜在生境及适宜性空间分布格局的影响。采用Maxent模型模拟了当前情景和全球环流模型(GCMs)气候场景下小白额雁潜在生境及其适宜性分布。研究结果表明,小白额雁分布特征与其栖息地周边植物分布呈显著相关关系;运用Maxent模型模拟小白额雁六种主要食源植物的分布特征,并将其结果作为环境变量,将显著改善小白额雁潜在生境及其适宜性模型的模拟性能;在两种典型浓度情景(RCP 2.6和RCP8.5)下,2070年小白额雁潜在生境适宜性面积将下降。为应对气候变化对小白额雁的影响,应采取更加合理的管理措施和保护政策,包括调整保护区的大小、形状和用途。     

Giant Maps as Pedagogical Tools for Teaching Geography and Mathematics

Giant maps offer a potentially useful pedagogical tool for teaching mathematics and map skills. In this pilot study, giant maps were distributed to elementary schools in Colorado and New Hampshire and teachers were provided with guided activities designed to integrate mathematics and geography skills. In an assessment of student skills, it was found that there were improvements in map-use skills and that students improved in some skills more than others. Additionally, there were differences in gender; girls improved more in their scores for some skills and less than boys in others. The teaching benefits of giant maps merit additional investigation.     

Wireless Mapping,GIS, and Learning about the Digital Divide: A Classroom Experience

Abstract

The purpose of this article is to describe a capstone course in undergraduate student geographical research in which GIS and other geospatial tools were used to teach undergraduate students basic geographical principles. The course uses the “cooperative learning” pedagogical approach to address one of a number of client-supplied research projects, chosen on the basis of logistical difficulty, time, student ability, and project importance. In the connection of primary data with existing data, students confronted a number of important research issues such as mapping ethics, database design and management, time management, group dynamics, and research limitations.     

Developing and Sequencing Community Engagement and Experiential Education: A Case Study of Urban Geography Teaching and Research

Community engagement curricula and course design can provide substantial experiences for both community members and participating students. Using a case study approach, this research focuses on four steps in this process: initial community relationship forming, engaging in community service, transitioning to civic engagement, and developing a community-based research program. Narrative examples from student course evaluations position these community-based experiences as transformative for multiple parties. Institutional structures are presented as helpful entrees to engagement for students, while noting that community relationships provide contextualized, powerful, and meaningful relationships, supporting recommendations for emerging and existing community engagement programs.     

A Framework for Teaching,Learning, and Assessing Environmental Issues

Abstract

A twelve-question framework for teaching, learning, and assessing environmental issues was created through analysis of environmental textbooks and interviews with professors and researchers who specialize in the environment. Concepts in the framework include: causes, scale, spatial distribution, longevity, consequences, risks, economic implications, solutions, obstacles, social values, stakeholders, political status, behavior changes, and personal actions. The twelve-question framework was submitted to a validity panel of geographers. Statistical analysis of responses from the panel indicates that the framework is valid.     

A locally-constrained YOLO framework for detecting small and densely-distributed building footprints

ABSTRACT

Building footprints are among the most predominant features in urban areas, and provide valuable information for urban planning, solar energy suitability analysis, etc. We aim to automatically and rapidly identify building footprints by leveraging deep learning techniques and the increased availability of remote sensing datasets at high spatial resolution. The task is computationally challenging due to the use of large training datasets and large number of parameters. In related work, You-Only-Look-Once (YOLO) is a state-of-the-art deep learning framework for object detection. However, YOLO is limited in its capacity to identify small objects that appear in groups, which is the case for building footprints. We propose a LOcally-COnstrained (LOCO) You-Only-Look-Once framework to detect small and densely-distributed building footprints. LOCO is a variant of YOLO. Its layer architecture is determined by the spatial characteristics of building footprints and it uses a constrained regression modeling to improve the robustness of building size predictions. We also present an invariant augmentation based voting scheme to further improve the precision in the prediction phase. Experiments show that LOCO can greatly improve the solution quality of building detection compared to related work.     

简论对基地班学生创新思维和科研能力的培养——以《城市地理学》的教学为例

为培养具有创新意识、创新能力的创新型人才，必须把创新思维教育和培养贯穿于课程教学之中。本文就《城市地理学》教学过程中，如何根据教材知识内容结构、特征，采用创新思维方法，对基地班学生进行创造性思维和科研能力培养，以提高教学质量和效果。     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

Conflation and aggregation of spatial data improve predictive models for species with limited habitats: A case of the threatened yellow-billed cuckoo in Arizona,USA

Riparian vegetation provides important wildlife habitat in the southwestern United States, but limited distributions and spatial complexity often leads to inaccurate representation in maps used to guide conservation. We test the use of data conflation and aggregation on multiple vegetation/land-cover maps to improve the accuracy of habitat models for the threatened western yellow-billed cuckoo (Coccyzus americanus occidentalis). We used species observations (n = 479) from a state-wide survey to develop habitat models from 1) three vegetation/land-cover maps produced at different geographic scales ranging from state to national, and 2) new aggregate maps defined by the spatial agreement of cover types, which were defined as high (agreement = all data sets), moderate (agreement ≥ 2), and low (no agreement required). Model accuracies, predicted habitat locations, and total area of predicted habitat varied considerably, illustrating the effects of input data quality on habitat predictions and resulting potential impacts on conservation planning. Habitat models based on aggregated and conflated data were more accurate and had higher model sensitivity than original vegetation/land-cover, but this accuracy came at the cost of reduced geographic extent of predicted habitat. Using the highest performing models, we assessed cuckoo habitat preference and distribution in Arizona and found that major watersheds containing high-probably habitat are fragmented by a wide swath of low-probability habitat. Focus on riparian restoration in these areas could provide more breeding habitat for the threatened cuckoo, offset potential future habitat losses in adjacent watershed, and increase regional connectivity for other threatened vertebrates that also use riparian corridors.     

Why did a vehicle stop? A methodology for detection and classification of stops in vehicle trajectories

ABSTRACT

Trajectory data mining is a lively research field in the domain of spatio-temporal data mining. Trajectory pattern mining comprises a set of specific pattern mining methods, which are applied as consecutive steps on a trajectory with the goal to extract and classify re-occurring spatio-temporal patterns. Despite the common nature and frequent usage of such methods by the GIScience community, a methodological approach is missing so far, especially when it comes to the use of machine learning-based classification methods. The current work closes this gap by proposing and evaluating a machine learning-based 3-steps trajectory data mining methodology using the detection and classification of stop points in vehicle trajectories as example. The work describes in detail the applied methodologies with respect to the three mining steps ‘stop detection’, ‘feature extraction’ and ‘classification in traffic-relevant and non-traffic-relevant stops’ and evaluates six machine learning-based classification algorithms using a real-world dataset of 15,498 vehicle trajectories with 5,899 detected stops (thereof 2,032 manually classified). Due to its exemplary nature, the presented methodology is suited to act as blueprint for similar trajectory data mining problems.     

白刺属植物的川西新记录和澳大利亚间断分布途径的探讨

四川西部干旱河谷新记录两种白刺属植物(柴达木白刺和西伯利亚白刺),从它们的地理分布可以反射出干旱河谷现代植物区系起源古老,具有历史植物区系“活化石”意义,该区系与古地中海在时间上、与现代中亚(草原和荒漠)在空间上有着渊源联系。这两种白刺在川西是中生而不是旱生植物,生于干旱河谷灌丛(近似于中亚草原灌丛)而不是草原或荒漠中。根据植物地理学和地质学研究的一些例证,探讨了远离白刺属发生地和分布中心的比拉底白刺(或其祖型),从非洲北部-阿拉伯地区经由印度和缅甸至马来西亚直达澳大利亚形成现代洲际间断分布的迁移途径。     

职住分离的度量方法与空间组织特征——以北京市轨道交通对职住分离的影响为例

基于问卷调查获取的万份调查数据,构建了以通勤时间/距离为基础数据的职住分离度量模型,即基于频数分布法的通勤时间/距离和基于不同时段/区段范围内居民分布结构的"职住分离强度指数".根据上述判别模型与计算结果,发现:①各样本区居民通勤时间普遍增加,城市外围区轨道交通沿线居民通勤时间多数集中在40分钟以上,自购居民的通勤时间...