Design and Usability of an Enhanced Geographic Information System for Exploration of Multivariate Health Statistics*

The multidimensional nature of many types of data in modern geography calls for creative and innovative approaches to their analysis. Statisticians have recently developed methods for exploring and visualizing large, multivariate datasets, but cartographers and geographers in general have only recently begun to integrate these methods for use with spatial and spatiotemporal datasets that are multivariate in character. This article will present an example of such an integration—an environment for visualization of health statistics—as a case study to demonstrate the philosophical and practical advantages of geovisualization systems for the exploration of complex spatiotemporal information. Emphasis is placed on the encouragement of creative thinking about geographic phenomena through the use of such data‐rich graphical tools.     

A land use/land cover change geospatial cyberinfrastructure to integrate big data and temporal topology

ABSTRACT

Big data have shifted spatial optimization from a purely computational-intensive problem to a data-intensive challenge. This is especially the case for spatiotemporal (ST) land use/land cover change (LUCC) research. In addition to greater variety, for example, from sensing platforms, big data offer datasets at higher spatial and temporal resolutions; these new offerings require new methods to optimize data handling and analysis. We propose a LUCC-based geospatial cyberinfrastructure (GCI) that optimizes big data handling and analysis, in this case with raster data. The GCI provides three levels of optimization. First, we employ spatial optimization with graph-based image segmentation. Second, we propose ST Atom Model to temporally optimize the image segments for LUCC. At last, the first two domain ST optimizations are supported by the computational optimization for big data analysis. The evaluation is conducted using DMTI (DMTI Spatial Inc.) Satellite StreetView imagery datasets acquired for the Greater Montreal area, Canada in 2006, 2009, and 2012 (534 GB, 60 cm spatial resolution, RGB image). Our LUCC-based GCI builds an optimization bridge among LUCC, ST modelling, and big data.     

Mining spatiotemporal association patterns from complex geographic phenomena

ABSTRACT

Spatiotemporal association pattern mining can discover interesting interdependent relationships among various types of geospatial data. However, existing mining methods for spatiotemporal association patterns usually model geographic phenomena as simple spatiotemporal point events. Therefore, they cannot be applied to complex geographic phenomena, which continuously change their properties, shapes or locations, such as storms and air pollution. The most salient feature of such complex geographic phenomena is the geographic dynamic. To fully reveal dynamic characteristics of complex geographic phenomena and discover their associated factors, this research proposes a novel complex event-based spatiotemporal association pattern mining framework. First, a complex geographic event was hierarchically modeled and represented by a new data structure named directed spatiotemporal routes. Then, sequence mining technique was applied to discover the spatiotemporal spread pattern of the complex geographic events. An adaptive spatiotemporal episode pattern mining algorithm was proposed to discover the candidate driving factors for the occurrence of complex geographic events. Finally, the proposed approach was evaluated by analyzing the air pollution in the region of Beijing-Tianjin-Hebei. The experimental results showed that the proposed approach can well address the geographic dynamic of complex geographic phenomena, such as the spatial spreading pattern and spatiotemporal interaction with candidate driving factors.     

A lightweight ensemble spatiotemporal interpolation model for geospatial data

ABSTRACT

Missing data is a common problem in the analysis of geospatial information. Existing methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease of use in practice. Classical interpolation models are easy to build and apply; however, their imputation accuracy is limited due to their inability to capture spatiotemporal characteristics of geospatial data. Consequently, a lightweight ensemble model was constructed by modelling the spatiotemporal dependencies in a classical interpolation model. Temporally, the average correlation coefficients were introduced into a simple exponential smoothing model to automatically select the time window which ensured that the sample data had the strongest correlation to missing data. Spatially, the Gaussian equivalent and correlation distances were introduced in an inverse distance-weighting model, to assign weights to each spatial neighbor and sufficiently reflect changes in the spatiotemporal pattern. Finally, estimations of the missing values from temporal and spatial were aggregated into the final results with an extreme learning machine. Compared to existing models, the proposed model achieves higher imputation accuracy by lowering the mean absolute error by 10.93 to 52.48% in the road network dataset and by 23.35 to 72.18% in the air quality station dataset and exhibits robust performance in spatiotemporal mutations.     

Some Aspects of the Boundaries and Frontiers of Israel

Abstract

An intensive and comprehensively planned program of mineral exploration and discovery is the most important single factor in maintaining an adequate supply of available minerals.

This program requires the services of a large staff of geologists and geophysicists adequately equipped with the essential instruments of exploration.

Exploration, even tho it results in substantial discoveries, will not be adequate without the aid of the miners, metallurgists, engineers, architects, and contributions from deposits in foreign countries.

Developments in mining—such as diamond drilling, block caving, and removal of overburden—have contributed toward making ore deposits of low grade economically exploitable, thereby increasing the supply of metal that can be made available.     

Editorial review

Abstract

This paper comprises the thoughts and perceptions of an interested outsider, an accountant and economist by training, who has recently chaired a Committee of Enquiry into the handling of geographical information. It focuses on the real barriers to getting more practical use from geographical information systems (GIS), most of which have research implications—but not of a technical nature. To an outsider GIS appears as a solution, or more strictly a tool, in search of a problem. Of course, there are numerous problems for GIS to tackle, but the difficulty is that those who have the problems are often either unaware of GIS and the questions they can deal with or, more seriously, are unaware that they have problems and of the questions they should be asking. GIS as a tool is about aiding managers to carry out their jobs more efficiently and effectively, and, more particularly, about better decision-making     

Book Reviews

Abstract

Educational Foundations of Trade and Industry.—Fabian Ware. 7 × 4 1/2, in. New York, D. Appleton & Co., 1901. Pp. xiv + 300. Price, $1.20.

Observations and Exercises on the Weather.—Laboratory Work in Physical Geography and Meteorology by James A. Price, A.M., Instructor in Physiography in the Fort Wayne High School, Indiana. 10 × 8 in. New York, American Book Co., 1902. Pp. 60. Price, 30 cents.

Harriman Alaska Expedition.—Two volumes. 10 × 7 in. New York. Doubledar, Page & Co., 1901. Pp. xlii + 184-200; 15 landscapes in color lithography, 25 color lithographs of birds, mammals and flowers, 85 photogravures, all full-page; 240 pen drawings in the text, and 5 maps. Text by John Burroughs and eleven others. Price, $15.

A Laboratory Manual of Botany.—Outlines and Directions for Laboratory and Field Work in Botany in Secondary Schools. Otis W. Caldwell, Ph.D., Professor of Botany in the Eastern Illinois State Normal School. 7 × 5 in. New York, D. Appleton & Co., 1902.

Grammar School Geography.—Alexis Everett Frye, recently Superintendent of Schools of Cuba. Large quarto, cloth. Boston, Ginn & Company, 1902. Pp. viii + 195 and 8. List price, $1.25.     

Artificial neural networks and geostatistical models for housing valuations in urban residential areas

Property valuation studies often use classical statistics techniques. Among these techniques, the Artificial Neural Networks are the most applied, overcoming the inflexibility and the linearity of the hedonic models. Other researchers have used Geostatistics techniques, specifically the Kriging Method, for interpreting spatial-temporal variability and to predict housing unit prices. The innovation of this study is to highlight how the Kriging Method can help to better understand the urban environment, improving the results obtained by classical statistics. This study presents two different methods that share the general objective of extracting information regarding a city’s housing from datasets. The procedures applied are Ordinary Kriging (Geostatistics) and Multi-Layer Perceptron algorithm (Artificial Neural Networks). These methods were used to predict housing unit prices in the municipality of Pozuelo de Alarcon (Madrid). The implementation of both methods provides us with the urban characteristics of the study area and the most significant variables related to price. The main conclusion is that the Ordinary Kriging models and the Neural Networks models, applied to predicting housing unit prices are necessary methodologies to improve the information obtained in classical statistical techniques.

Abbreviations: ANN: Artificial Neural Networks; OK: ordinary Kriging; MLP: multi-layer perceptron     

Applications of multivariate statistical analysis in remote sensing of agriculture

Rasmussen, Kjeld & Olesen, Henrik Hagen: Applications of multivariate statistical analysis in remote sensing of agriculture. Geografisk Tidsskrift 88:100–107. Copenhagen 1988.

Applications of satellite remote sensing to agriculture involve two main objectives, the identification and mapping of crops, including estimation of acreages, and monitoring of plant growth or production factors, aiming at estimation/prediction of yields.

Deterministic models of the interaction of electromagnetic radiation and plant canopies are used to relate the measured reflected or emitted radiation to crop type and agronomically relevant parameters. The great natural variation of reflectance properties of crops does, however, call for use of a statistical approach. The high dimensionality of the data-sets involved, very often more than ten, requires the use of multivariate techniques.

This paper will deal with the use of multivariate statistical techniques for both crop identification and crop monitoring based on high-resolution satellite remote sensing data, such as those produced by Landsat MSS and -TM and SPOT. Emphasis will be placed upon use of statistical methods in classification and on removal of redundancy in multi-dimensional data-sets. The relative merits of deterministic and statistical methods will be discussed as will the possibilities of incorporating spatial information into statistical methods.     

A human-machine adversarial scoring framework for urban perception assessment using street-view images

ABSTRACT

Though global-coverage urban perception datasets have been recently created using machine learning, their efficacy in accurately assessing local urban perceptions for other countries and regions remains a problem. Here we describe a human-machine adversarial scoring framework using a methodology that incorporates deep learning and iterative feedback with recommendation scores, which allows for the rapid and cost-effective assessment of the local urban perceptions for Chinese cities. Using the state-of-the-art Fully Convolutional Network (FCN) and Random Forest (RF) algorithms, the proposed method provides perception estimations with errors less than 10%. The driving factor analysis from both the visual and urban functional aspects demonstrated its feasibility in facilitating local urban perception derivations. With high-throughput and high-accuracy scorings, the proposed human-machine adversarial framework offers an affordable and rapid solution for urban planners and researchers to conduct local urban perception assessments.     

Geographic Graphics

Abstract

The diagrammatic climate key presented here was developed in an attempt to simplify the teaching of climate classification at the secondary and college levels. The chart emphasizes four critical temperatures involved in the Koppen classification, and shows their relationships to the various climate types. It is hoped that use of this key will give students a better understanding of the role of critical temperatures such as 64.4° or 71.6°, symbols such as Aw or BSk, and the overall scheme of climate classification. The chart has been developed for the Köppen climatic classification as modified by Ackerman and Van Royen and popularized by Trewartha.¹ Because the modified Koppen system is widely used and accepted, that system—along with Fahrenheit temperatures—will be utilized in the following discussion. Figure 1 represents the working skeleton, which may be drawn upon the chalkboard, presented by use of transparent overlay, or handed out on individual laboratory exercise sheets.     

Community resources for small city creativity? Rethinking creative economy narratives at the Blue Mountains Music Festival

ABSTRACT

Limitations in scale, infrastructure and business opportunities in combination with a greater sense of community in small centres require a more diverse understanding of the small city creative economy. Entrepreneurship and business development builds on non-capitalist resources, creative community initiatives, volunteers and community networks. Fostering the creative economy in small cities requires a rethinking of creative narratives that iterate the key role of non-capitalist community resources. I illustrate this argument via a case study of the Blue Mountains Music Festival, in New South Wales, Australia. It examines the gap between creative economy aspirations of a small city government and the reality of creativity ‘on the ground’. The City of the Blue Mountains is a small urban centre on the fringes of metropolitan Sydney and regional New South Wales. Its council has recently adopted a creative industries approach to enhance local employment in line with national and metropolitan creative agendas. The Blue Mountains Music Festival was launched in response to creative economy policy more than 20 years ago, but has now lost this connection as it fails to fit current policy aspirations. The festival is, however, an effective example of how creativity in a small city unfolds, demonstrating that beyond jobs and growth are key matters of non-capitalist community networks and resources.     

Remotely-sensed data as an information source for geographical information systems in natural resource management a review

Abstract

Remotely-sensed image data have long served as a primary source of information for. geographical information systems (GIS), with the transformation of data to information provided by visual image interpretation. Digital image data, in combination with recent advances in computer technology, currently allow visual image interpretation to be realised in a completely digital processing environment. However, it remains desirable to capitalize even further on the potential of digital image data for GIS, through the use of schemes involving automated data processing. The opportunities for, and impediments to, such schemes are considered here with particular reference to the provision of information for natural resource management. It is concluded that further—although foreseeable—developments in both research and technology will be required before fully operational procedures for the automated acquisition of GIS information from image data can be established.     

Fuzzy representation of geographical boundaries in GIS

Abstract

Polygon boundaries on thematic maps are conventionally considered to be sharp lines representing abrupt changes of phenomena. However, in reality changes of environmental phenomena may also be partial or gradual. Indiscriminate use of sharp lines to represent different types of change creates a problem of boundary inaccuracy. Specifically, in the context of vector-based GIS, use of sharp lines to represent gradual or partial changes may cause misunderstanding of geographical information and reduce analysis accuracy.

In this paper, the expressive inadequacy of the conventional vector boundary representation is examined. A more informative technique—the fuzzy representation of geographical boundaries—is proposed, in which boundaries describe not only the location but also the rate of change of environmental phenomena. Four methods of determining fuzzy boundary membership grades from different kinds of geographical data are described. An example of applying the fuzzy boundary technique to data analysis is presented and the advantages of the technique are discussed.     

Upland Adjustment to Regional Drainage in Central Pennsylvania: An Application of Trend Surface Analysis

Abstract

The use of quantitative measurements in geography has increased rapidly throughout the 1960s, and will probably continue to accelerate. It is unfortunate that those with limited mathematical backgrounds have felt intimidated by this new methodology. Even more regrettable is the fact that few quantifiers have attempted to make their work more palatable to a wider audience. The method of trend surface analysis employed here to explore an old problem—peneplanation—has many other potential applications in geographic research. The author believes that no geographer should feel intimidated by mathematical symbolism. An attempt is made to “talk-through” the technique of spatial trend analysis—one of many measurement tools already or shortly available to geography teachers and high school students via remote terminals linked to university computing centers. In addition, the concepts of the Erosion Cycle and upland accordance are reviewed and evaluated in the light of new evidence trend surface analysis now makes possible.     

Data-driven approach to characterize urban vitality: how spatiotemporal context dynamically defines Seoul’s nighttime

ABSTRACT

This study takes a data-driven approach to define urban nighttime by examining the spatiotemporal dynamics of urban vitality. Using micro-scale spatiotemporal analysis, this paper empirically provides a comprehensive, yet granular, picture of collective human behaviors in cities. Using Seoul, South Korea as a case study site, it prioritizes the spatiotemporal context in order to mitigate uncertain contextual effects inherent in such forms of data-driven analysis. Instead of leaving the data re-grouping up to researcher’s arbitrary decision, this paper employs a functional principal component analysis (FPCA) to systematically transform a set of discrete data to a continuous functional form. This paper applies FPCA on 24-hour-based dataset of pedestrian traffic in Seoul in order to make a data-driven extraction of principal components that characterize the city’s unique patterns of urban vitality. Extracting principal components allows for less statistically obvious phenomena to be measured that would have otherwise been hidden within the data. This approach proved successful in capturing nighttime vitality patterns that are eclipsed by the overwhelming trend of daytime patterns. Additionally, this paper compares differences between regions and seasons to examine what the differences can tell about the definition of nighttime.     

THE PRODUCTION AND EMPLOYMENT OF GEOGRAPHERS IN CANADA

This paper considers the appropriateness of typologies of urban retailing areas based on multivariate functional data. The findings of a case study in which statistical and graphical methods were employed within the framework of multivariate ordination are presented. It is shown that the functional attributes of retail areas are closely associated with their location, accessibility, quality, morphology, size, period of development, and socioeconomic character.     

Ventures into Viral Cartography: Waffle House,Educational Attainment,and the Social Life of Maps

Viral maps—ones that are shared widely on social media and media outlets—have become an increasingly common part of online conversations about a range of issues. Despite the increasing prevalence of these viral maps, only a few academic researchers have examined the factors leading to their popularity or their social use and effect. In this article, we analyze two case studies of viral maps, a viral tweet about the August 2017 total eclipse and an interactive tool for exploring educational attainment by neighborhood in the United States. By reflecting on our experience as authors of these maps and analyzing the reactions they elicited, we identify several key elements of these maps and their circulation. First, viral maps act as a form of phatic communication, allowing users to restate and react to shared social identities. Second, maps are read from specific times and places, and this spatiotemporal context significantly shapes the reactions of map readers. Finally, viral maps illustrate gaps or improvements in trust between mapmakers and map readers, including questions about map accuracy or the intentions behind the map. We close by considering implications for future research and viral cartography. Key Words: online maps, social networks, viral cartography.     

GIS and remote sensing integration for environmental applications

Abstract

The combined use of remotely-sensed images and vector GIS data has received considerable interest in recent years. This review article discusses the historical move towards closer integration of remote sensing and GIS technologies and the requirements of integrated software systems to enable remotely-sensed data to be combined with vector datasets for maximum effect. The benefits of integration to users of both GIS and remote sensing for environmental applications are reviewed and some thoughts are given on terminology and future directions in this field.