Spatial optimization for land acquisition problems: A review of models,solution methods,and GIS support

This article reviews the interdisciplinary research field of spatial optimization for land acquisition problems. We start with a theoretical framework to identify three categories of spatial optimization models: problems with aspatial constraints, location models, and problems with topological constraints. Exact, heuristic, and metaheuristic approaches to solving these problems are critically discussed. Tools that are available in commercial and open‐source GIS packages are reviewed from four aspects. We first survey the off‐the‐shelf support and then the development environments in these packages. A case study of the one‐center problem is used to illustrate the computational performance of different solution methods. Finally the advantages and disadvantages of current GIS data models are discussed. The article concludes with challenges and future directions for solving spatial optimization problems for land acquisition.     

Modeling and Communicating the Conceptual Intent of Geo‐Analytical Tasks for Human‐GIS Interaction

One of the fundamental issues of geographical information science is to design GIS interfaces and functionalities in a way that is easy to understand, teach, and use. Unfortunately, current geographical information systems (including ArcGIS) remains very difficult to use as spatial analysis tools, because they organize and expose functionalities according to GIS data structures and processing algorithms. As a result, GIS interfaces are conceptually confusing, cognitively complex, and semantically disconnected from the way human reason about spatial analytical activities. In this article, we propose an approach that structures GIS analytical functions based on the notion of “analytical intent”. We describe an experiment that replaces ArcGIS desktop interface with a conversational interface, to enable mixed‐initiative user‐system interactions at the level of analytical intentions. We initially focus on the subset of GIS functions that are relevant to “finding what's inside” as described by Mitchell, but the general principles apply to other types of spatial analysis. This work demonstrates the feasibility of delegating some spatial thinking tasks to computational agents, and also raises future research questions that are key to building a better theory of spatial thinking with GIS.     

Integrating GRASS GIS and Jupyter Notebooks to facilitate advanced geospatial modeling education

Open education materials are critical for the advancement of open science and the development of open-source software. These accessible and transparent materials provide an important pathway for sharing both standard geospatial analysis workflows and advanced research methods. Computational notebooks allow users to share live code with in-line visualizations and narrative text, making them a powerful interactive teaching tool for geospatial analytics. Specifically, Jupyter Notebooks are quickly becoming a standard format in open education. In this article, we introduce a new GRASS GIS package, grass.jupyter , that enhances the existing GRASS Python API to allow Jupyter Notebook users to easily manage and visualize GRASS data including spatiotemporal datasets. While there are many Python-based geospatial libraries available for use in Jupyter Notebooks, GRASS GIS has extensive geospatial functionality including support for multi-temporal analysis and dynamic simulations, making it a powerful teaching tool for advanced geospatial analytics. We discuss the development of grass.jupyter and demonstrate how the package facilitates teaching open-source geospatial modeling with a collection of Jupyter Notebooks designed for a graduate-level geospatial modeling course. The open education notebooks feature spatiotemporal data visualizations, hydrologic modeling, and spread simulations such as the spread of invasive species and urban growth.     

GIS本科教育:开放、开源与开发

随着地理信息知识结构的不断发展,社会人才需求呈现多样性,传统的本科教学方式不足以应对高水平的科研和工作要求,更科学高效的本科培养方案亟待提出。为响应以“成人”统领“成才”的教学指导方针,将GIS的基础理念、专业知识和方法技术贯彻到本科生的教学之中,本文提出GIS本科教育3个创新思想——开放、开源与开发,创建了一套“多学科、多方法、多技巧”的立体式培养方案,结合地理信息科学的专业特点在现有教学方式的基础上进行了突破性的设想和改革,将GIS的本科教学变成一种知识领域更加全面、方法技术更加前沿、实践方式更加多样的专业人才培养模式。该教学理念有利于本科学生充分认识所学知识,在理论基础的支持上能更快速地提升空间思维素养和增强实践开发能力,是GIS本科教育方法上的有益探索。     

A Review of Sampling Effects and Response Bias in Internet Participatory Mapping (PPGIS/PGIS/VGI)

Global interest in participatory mapping described as public participation GIS (PPGIS), participatory GIS (PGIS), and volunteered geographic information (VGI) continues to grow, but systematic study of spatial data quality and sampling effects is limited. This article provides a review and meta‐analysis of Internet‐based PPGIS studies conducted during the period 2006–2015 (n=26) to answer the following research questions: (1) How does mapping effort, as a proxy measure for spatial data quality, differ by sampling group? (2) Does the purpose and context of PPGIS influence mapping results? (3) What is the potential for mapping bias through sampling design? (4) Given the results, what should be the focus of future PPGIS research? Mapping effort was highest in sampling groups whose livelihoods were closely related to the purpose of the study, there was greater mapping effort in household sampling groups compared to volunteer groups, and participant domicile had strong effects on mapped results through spatial discounting. The use of online Internet panels provides higher response rates but lower spatial data quality. Future research should focus on increasing sampling response rates, assessing social trade‐offs using alternative spatial weighting schemes, and examining the capacity of the public to select land use alternatives as a complement to traditional expert‐driven planning systems.     

The Development of the Chagas' Online Data Entry System (CODES-GIS)

This article describes a web‐based data entry and GIS‐driven mapping system designed for an ethnographic and entomological survey of Chagas’ disease, an emerging zoonotic disease, and Triatoma dimidiata, a primary vector, in the Los Tuxtlas region of Veracruz, Mexico. To better understand this disease in the region, a collaborative, multi‐disciplinary study was initiated to conduct a spatial investigation of T. dimidiata and a community‐by‐community survey of local perceptions of the disease. In order to facilitate such a collaborative effort the CODES‐GIS was developed. This system allows for (near) real‐time mapping, analyses, disease reporting, and results sharing. CODES‐GIS provides a framework for a research team working in a remote area with limited technology, software, or GIS expertise to benefit from (near) real‐time spatial analyses performed at collaborating institutions. The system is bi‐directional, where field personnel can upload data to the system for field‐based map production. Likewise, laboratory personnel can upload diagnostics data for viewing by field personnel. In this way, the system provides a virtual link between the field and the laboratory to increase the speed at which results are returned to the local community. The CODES‐GIS is described along with a selection of study results.     

基于面向对象知识表达的空间推理决策及应用

提出了一种基于面向对象知识表达的空间推理决策方法 ,该方法扩展了常规的GIS空间分析功能 ,使其建立在GIS空间分析与领域知识基础上。认为GIS空间分析与领域知识利用的结合需要建立统一的知识表达概念模型 ,通过将常规决策知识设计为决策知识类 ,将决策事实作为事实知识类处理 ,并由知识类的消息机制进行内部通讯 ,实现空间推理决策的智能化。在进行领域知识分析与知识分类的基础上 ,给出了领域知识类的层次结构组织图和知识类定义以及决策知识及事实知识的面向对象表达 ,最后给出了空间知识类的形成及空间推理决策过程。方法的实用性在实践中得到验证     

Analysis of human spatial behavior in a GIS environment: Recent developments and future prospects

In recent years, comprehensive geographic data sets of metropolitan areas and individual-level, georeferenced data are becoming more available to social scientists. At the same time, tools for performing spatial analysis in a GIS environment have also become more available. These developments provide many new opportunities for the analysis and theoretical understanding of disaggregate human spatial behavior. This paper examines how these developments may enable the researcher to represent complex urban and cognitive environments more realistically, and to overcome the limitations of aggregate spatial data framework. It explores their implications for the theoretical and methodological development in geography and other social science disciplines.     

A Review and Evaluation of Uncertainty Classification and the Error‐Band Geometry Model

Advances in computer technologies have improved the quality of maps, making map comparison and analysis easier, but uncertainty and error still exist in GIS when overlaying geographic data with multiple or unknown confidence levels. The goals of this research are to review current geospatial uncertainty literature, present the Error‐Band Geometry Model (EBGM) for classifying the size and shape of spatial confidence intervals for vector GIS data, and to analyze the interpretability of the model by looking at how people use metadata to classify the uncertainty of geographic objects. The results from this research are positive and provide important insight into how people interpret maps and geographic data. They suggest that uncertainty is more easily interpreted for well defined point data and GPS data. When data is poorly defined, people are unable to determine an approach to model uncertainty and generate error‐bands. There is potential for using the EBGM to aid in the development of a GIS tool that can help individuals parameterize and model spatial confidence intervals, but more research is needed to refine the process by which people use the decision tree. A series of guiding questions or an “uncertainty wizard” tool that helps one select an uncertainty modeling approach might improve the way people apply this model to real‐world applications.     

Seamless integration of spatial statistics and GIS: The S-PLUS for ArcView and the S+Grassland Links

The extension of the functional capacity of geographic information systems (GIS) with tools for statistical analysis in general and exploratory spatial data analysis (ESDA) in particular has been an increasingly active area of research in recent years. In this paper, two operational implementations that combine the functionality of spatial data analysis software with a GIS are considered more closely. They consist of linkages between the S-PLUS software for data analysis and two different GIS implementations, the ArcView desktop system, which is mostly vector-oriented, and the primarily raster-based Grassland open GIS environment. We emphasize conceptual and technical issues related to the software implementation of these approaches and suggest future directions for linking spatial statistics and GIS. Received: 14 January 1999 / Accepted: 11 May 1999     

Deeply integrating Linked Data with Geographic Information Systems

The realization that knowledge often forms a densely interconnected graph has fueled the development of graph databases, Web‐scale knowledge graphs and query languages for them, novel visualization and query paradigms, as well as new machine learning methods tailored to graphs as data structures. One such example is the densely connected and global Linked Data cloud that contains billions of statements about numerous domains, including life science and geography. While Linked Data has found its way into everyday applications such as search engines and question answering systems, there is a growing disconnect between the classical ways in which Geographic Information Systems (GIS) are still used today and the open‐ended, exploratory approaches used to retrieve and consume data from knowledge graphs such as Linked Data. In this work, we conceptualize and prototypically implement a Linked Data connector framework as a set of toolboxes for Esri's ArcGIS to close this gap and enable the retrieval, integration, and analysis of Linked Data from within GIS. We discuss how to connect to Linked Data endpoints, how to use ontologies to probe data and derive appropriate GIS representations on the fly, how to make use of reasoning, how to derive data that are ready for spatial analysis out of RDF triples, and, most importantly, how to utilize the link structure of Linked Data to enable analysis. The proposed Linked Data connector framework can also be regarded as the first step toward a guided geographic question answering system over geographic knowledge graphs.     

How Reliable are Citizen‐Derived Scientific Data? Assessing the Quality of Contrail Observations Made by the General Public

Citizen science projects encourage the general public to participate in scientific research. Participants can contribute large volumes of data over broad spatial and temporal frames; however, the challenge is to generate data of sufficient quality to be useable in scientific research. Most observations made by citizen‐scientists can be independently verified by “experts”. However, verification is more problematic when the phenomena being recorded are short‐lived. This article uses a GIS methodology to verify the quality of contrail observations made by the general public as part of the OPAL Climate Survey. We verify observations using datasets derived from a variety of different sources (experts, models and amateur enthusiasts) with different spatial and temporal properties which reflect the complex 3D nature of the atmosphere. Our results suggest that ～70% of citizen observations are plausible, based on favorable atmospheric conditions and the presence or absence of aircraft; a finding which is in keeping with other, more conventional citizen science projects. However, questions remain as to why the quality of the citizen‐based observations was so high. Given the lack of supporting data on observers, it is impossible to determine whether the dataset was generated by the activities of many participants or a small but dedicated number of individual observers.     

The Potential of Maps APIs for Internet GIS Applications

Since the launching of Maps Application Programming Interfaces (APIs) in 2005, many web developers, including geographers and non‐geographers, applauded the freely adaptable tools and used them to spawn numerous Internet applications. The success of the Maps APIs is largely attributable to its no‐cost policy, the availability of global data coverage, dynamic navigation, query capability, and ease of implementation. Despite its versatility in dynamic exploration of geographic data online, the existing Maps APIs lack Geographic Information System (GIS) functionalities compared to other Internet Mapping Services. The goal of this research was to review the potential of the Maps APIs for Internet GIS applications. This research employed the Google Maps API and developed a web prototype that disseminates spatial information of urban sprawl in Mundy Township, Michigan. The results revealed that both vector and raster data could be effectively represented by using the Maps API. Moreover, the Geographic Markup Language (GML) approach illustrated great potential for developing Internet GIS solutions around open specifications. This research suggested several potential solutions to expand the spectrum of GIS operations of the Maps APIs by incorporating the XML‐related technology and extending the JavaScript library.     

GIS Course Planning: A Comparison of Syllabi at US College and Universities

Despite the enormous growth of college courses dealing with spatial information, curriculum planning involving geographic information science (GIS) courses and programs has received little attention within the GIS literature. As the number and variety of GIS courses expands, so too does the importance of both systematic and inclusive planning and campus‐wide coordination. In this article we explore course planning through an exploration of 312 GIS course syllabi used at US colleges and universities with the goal of characterizing the degree to which learning resources, student evaluation methods, and course topics have become standardized across institution types, academic levels, and disciplines. Our findings reveal a consensus in the use of GIS software across courses but no similar agreement in the use of textbooks. Hands‐on activities were used as evaluation methods in nearly every course in the sample regardless of institution type or academic level. However, quizzes, tests and final exams were used more often in community colleges while papers and projects were used more frequently in four‐year and comprehensive research universities. The frequency of topic categories listed on syllabi was relatively consistent across institutions, academic levels and disciplines with a few topics such as vector analytic operations, data models, and data creation/acquisition/editing included on more than 50% of syllabi.     

Inference and analysis across spatial supports in the big data era: Uncertain point observations and geographic contexts

The ways in which geographic information are produced have expanded rapidly over recent decades. These advances have provided new opportunities for geographical information science and spatial analysis—allowing the tools and theories to be expanded to new domain areas and providing the impetus for theory and methodological development. In this light, old problems of inference and analysis are rediscovered and need to be reinterpreted, and new ones are made apparent. This article describes a new typology of geographical analysis problems that relates to uncertainties in the relationship between individual‐level data, represented as point features, and the geographic context(s) that they are associated with. We describe how uncertainty in context linkage (uncertain geographic context problem) is also related to, but distinct from, uncertainty in point‐event locations (uncertain point observation problem) and how these issues can impact spatial analysis. A case study analysis of a geosocial dataset demonstrates how alternative conclusions can result from failure to account for these sources of uncertainty. Sources of point observation uncertainties common in many forms of user‐generated and big spatial data are outlined and methods for dealing with them are reviewed and discussed.     

Virtual Coast Guard: An Open and Crowdsourced GIS Portal for Stewardship of India's Coast

India's coastline is under tremendous pressure. The current processes of development are becoming a threat to the ecology and livelihoods housed on the coast. If left unchecked, there will not be much of a coast left for our future generations. GIS provides tremendous opportunities to map spatial information for better decision‐making. Government agencies generate maps, but they are localized and inaccessible, preventing an actual assessment of the damage that is already happening to our coast. The Coastal Area Mapping Project (CAMP) envisions creating a collaborative GIS platform using open source technologies. CAMP's vision is to use Free and Open Source Software (FOSS) for collaboration and cocreation and collective problem solving. CAMP foresees a vision for India's coastline where power is equalized among all constituencies (Government, corporations, media, communities, civil societies, etc.), by capturing the big picture and patterns, thus enabling the creation of strategies for stewardship of India's coastline for human wellbeing, coastal protection and sustainable coastal development.     

多学科交叉中的大测绘科学

值《测绘学报》创刊50周年之际,扼要回顾测绘科学从几何科学发展为信息科学,目前已进入信息化测绘新阶段的历史演变。文中重点讨论处于多学科交叉中的大测绘科学,这种交叉集成不仅反映在测绘学科内各分支学科的相互融合与集成,而且也反映在测绘学科与其他多学科的交叉集成,形成广义空间信息网格,从而大大地促进了测绘学的自动化和智能化水平,使得地球空间信息大众化,实现按需测量,为各行各业服务。这种发展趋势将为《测绘学报》未来50年的发展提供更加广阔的天地。     

A Spatial Analysis System for Integrating Data,Methods and Models on Environmental Risks and Health Outcomes

Integrating data on health outcomes with methods of disease mapping and spatially explicit models of environmental contaminants are important aspects of environmental health surveillance. In this article, we describe a modular, web‐based spatial analysis system that uses GIS, spatial analysis methods and software services delivered over computer networks to achieve this end. The Environmental Health Surveillance System (EHSS) is a prototype system that is designed to serve three purposes: a secure environment for producing maps of disease outcomes from individual‐level data while preserving privacy; an automated process of linking environmental data, environmental models, and GIS tasks like geocoding for the purposes of estimating individual exposures to environmental contaminants; and mechanisms to visualize the spatial patterns of disease outcomes via Web‐based mapping interfaces and interactive tools like Google Earth.     

Integrating spatial data analysis functionalities in a GIS environment: Spatial Analysis using ArcGIS Engine and R (SAAR)

Spatial data analysis (SDA) tools to efficiently handle and explore spatial data have become readily available. Although these SDA tools have their own strengths and purposes, they suffer from limited support in terms of a development environment offering easy customization and high extensibility, a strength of open source software. This article presents a standalone software package for SDA in a geographic information system (GIS) environment, called Spatial Analysis using ArcGIS Engine and R (SAAR), which provides an integrated GIS and SDA environment. A set of SDA tools in SAAR utilizes functions in R using R.NET, while other tools were developed in .NET independent of R. SAAR provides an efficient working environment for both general and advanced GIS users. For general GIS users with limited programming skills, SAAR furnishes advanced SDA tools in a popular ArcGIS environment with graphical user interfaces. For advanced GIS users, SAAR offers an extensible GIS platform to help them customize and implement SDA functions with relatively little development effort. This article demonstrates some functionalities of SAAR using census data for Texas counties.