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.     

GIS in Participatory Research: Assessing the Impact of Landmines on Communities in North-west Cambodia

The view of GIS, adopted by many, as an undemocratic and divisive technology is perhaps most poignant in settings where financial and skills‐based resources are limited, notably in lower‐income countries. Where those countries are also recovering from a period of military conflict there would at first sight seem to be little opportunity for employing GIS as part of a socially‐sensitive approach to ‘development’. This paper explores the potential for using GIS in participatory approaches to gathering and analysing geographical information on human‐environment interactions in post‐conflict settings. We discuss empirical work from northwest Cambodia where GIS and participatory methods have been integrated to assess risk to local communities from landmines and to develop priorities for landmine clearance. In juxtaposing ‘official’, spatially‐referenced data with indigenous geographical knowledge in this study we seek to privilege meaningful geographical understandings over conventional notions of spatial ‘accuracy’. In so doing we carry out a Participatory Mine Impact Assessment (PMIA) to explore spatial activities among local populations in mine‐contaminated communities and to help identify improved strategies for returning refugees in re‐establishing livelihoods.     

Buildings in GI: How to deal with building models in the GIS domain

Digital building information is important during a building's lifecycle, it is needed from first design until demolition. The two domains that mainly contribute are AEC (architecture, engineering, and construction) during design, construction, and operation, and GIS as a supporting discipline for analysis and further integration of the building's environment. However, there is a challenge in information exchange between the two domains, resulting in the remodeling of digital building information in GIS. In this article, we identify three major data sources from the AEC domain and show transformation processes to enable the integration of such models into the geographical environment in the form of one transition model. Furthermore, we show that this model can either be used directly or exported in the form of de facto standards that allow for further analysis.     

Network Density Estimation: A GIS Approach for Analysing Point Patterns in a Network Space

Human activities and more generally the phenomena related to human behaviour take place in a network‐constrained subset of the geographical space. These phenomena can be expressed as locations having their positions configured by a road network, as address points with street numbers. Although these events are considered as points on a network, point pattern analysis and the techniques implemented in a GIS environment generally consider events as taking place in a uniform space, with distance expressed as Euclidean and over a homogeneous and isotropic space. Network‐spatial analysis has developed as a research agenda where the attention is drawn towards point pattern analytical techniques applied to a space constrained by a road network. Little attention has been put on first order properties of a point pattern (i.e. density) in a network space, while mainly second order analysis such as nearest neighbour and K‐functions have been implemented for network configurations of the geographical space. In this article, a method for examining clusters of human‐related events on a network, called Network Density Estimation (NDE), is implemented using spatial statistical tools and GIS packages. The method is presented and compared to conventional first order spatial analytical techniques such as Kernel Density Estimation (KDE). Network Density Estimation is tested using the locations of a sample of central, urban activities associated with bank and insurance company branches in the central areas of two midsize European cities, Trieste (Italy) and Swindon (UK).     

A Location Aware Mobile Tool for Direct and Indirect Climate Data Sensors

Access to GIS data from mobile platforms continues to be a challenge and there is a wide range of fields where it is extremely useful. In this work, we combined three key aspects: climate data sensors, mobile platforms and spatial proximity operations. We published and made use of a web 2.0 network of climate data, where content is user‐collected, by means of their meteorological stations, and exposed as available information for the virtual community. Moreover, we enriched this data by giving the users the opportunity to directly inform the system with different climate measures. In general, management of this type of information from a mobile application could result in an important decision tool, as it enables us to provide climate‐related data according to a context and a geographical location. Therefore, we implemented a native mobile application for iPhone and iPad platforms by using ArcGIS SDK for iOS and by integrating a series of ArcGIS webmaps, which allows us to perform geospatial queries based on the user's location, offering, at the same time, access to all the data provided by the climate data sensor network and from direct users.     

Human conceptions of spaces: Implications for GIS

The way people conceptualize space is an important consideration for the design of GIS, because a better match with people's thinking is expected to lead to easier-to-use information systems. Everyday space, the basis to GIS, has been characterized in the literature as being either small-scale (from table-top to room-size spaces) or large-scale (inside-of-building spaces to city-size spaces). While this dichotomy of space is grounded in the view from psychology that people's perception of space, spatial cognition, and spatial behaviour are experience-based, it is in contrast to current GIS, which enable us to interact with large-scale spaces as though they were small-scale or manipulable. We analyse different approaches to characterizing spaces and propose a unified view in which space is based on the physical properties of manipulability, locomotion, and size of space. Within the structure of our framework, we distinguish six types of spaces: manipulable object space (smaller than the human body), non-manipulable object space (greater than the human body, but less than the size of a building), environmental space (from inside-of-building spaces to city-size spaces), geographic space (state, country, and continent-size spaces), panoramic space (spaces perceived via scanning the landscape), and map space. Such a categorization is an important part of Naive Geography, a set of theories on how people intuitively or spontaneously conceptualize geographic space and time, because it has implications for various theoretical and methodological questions concerning the design and use of spatial information tools. Of particular concern is the design of effective spatial information tools that lead to better communication.     

Making direction a first‐class citizen of Tobler's first law of geography

Waldo Tobler frequently reminded us that the law named after him was nothing more than calling for exceptions. This article discusses one of these exceptions. Spatial relations between points are frequently modeled as vectors in which both distance and direction are of equal prominence. However, in Tobler's first law of geography, such a relation is described only from the perspective of distance by relating the decreasing similarity of observations in some attribute space to their increasing distance in geographic space. Although anisotropic versions of many geographic analysis techniques, such as directional semivariograms, anisotropy clustering, and anisotropic point pattern analysis, have been developed over the years, direction remains on the level of an afterthought. We argue that, compared to distance, directional information is still under‐explored and anisotropic techniques are substantially less frequently applied in everyday GIS analysis. Commonly, when classical spatial autocorrelation indicators, such as Moran's I, are used to understand a spatial pattern, the weight matrix is only built from distance, without direction being considered. Similarly, GIS operations, such as buffering, do not take direction into account either, with distance in all directions being treated equally. In reality, meanwhile, particularly in urban structures and when processes are driven by the underlying physical geography, direction plays an essential role. In this article we ask whether the development of early GIS, data (sample) sparsity, and Tobler's law lead to a theory‐induced blindness for the role of direction. If so, is it possible to envision direction becoming a first‐class citizen of equal importance to distance instead of being an afterthought only considered when the deviation from a perfect circle becomes too obvious to be ignored?     

Description of a New Method for Retrieving the Aerosol Optical Thickness from Satellite Remotely Sensed Imagery Using the Maximum Contrast Value and Darkest Pixel Approach

Satellite sensors have provided new datasets for monitoring regional and urban air quality. Satellite sensors provide comprehensive geospatial information on air quality with both qualitative remotely sensed imagery and quantitative data, such as aerosol optical depth which is the basic unknown parameter for any atmospheric correction method in the pre‐processing of satellite imagery. This article presents a new method for retrieving aerosol optical thickness directly from satellite remotely sensed imagery for short wavelength bands in which atmospheric scattering is the dominant contribution to the at‐satellite recorded signal. The method is based on the determination of the aerosol optical thickness through the application of the contrast tool (maximum contrast value), the radiative transfer calculations and the ‘tracking’ of the suitable darkest pixel in the scene. The proposed method that needs no a‐priori information has been applied to LANDSAT‐5 TM, LANDSAT‐7 ETM+, SPOT‐5 and IKONOS data of two different geographical areas: West London and Cyprus. The retrieved aerosol optical thickness values show high correlations with in‐situ visibility data acquired during the satellite overpass. Indeed, for the West London area a logarithmic regression was fitted for relating the determined aerosol optical thickness with the in‐situ visibility values. A high correlation coefficient (r²= 0.82; p= 0.2) was found. Plots obtained from Tanre et al. (1979, 1990) and Forster (1984 ) were reproduced and estimates for these areas were generated with the proposed method so as to compare the results. The author's results show good agreement with Forster's aerosol optical thickness vs. visibility results and a small deviation from Tanre's model estimates.     

The Performance of Vector Oriented Data Storage Strategies in ESRI's ArcGIS

The emergence of Geographical Information Systems (GIS) as an important tool in the analysis of spatial phenomena has been mirrored by the evolution of the data models underpinning such systems. When considering vector‐based solutions, such developments have seen a migration from single‐user, file‐based, topological hybrid models to multi‐user database management system (DBMS) based integrated formats, often with no inherent topology. With all these solutions still being readily available, the decision of which to employ for a given application is a complicated one. This study analyses the performance of a number of vector data storage formats for use with ESRI's ArcGIS, with the aim to facilitate the ‘intelligent selection’ of an appropriate solution. Such a solution will depend upon the application domain and both single‐user and multi‐user (corporate) scenarios are considered. Findings indicate that single‐user ESRI coverages and multi‐user ArSDE/Oracle strategies perform better when considering the range of GIS operations used to evaluate data store performance.     

3D Visualization for the Analysis of Forest Cover Change

Abstract

Visualization techniques have been developed to recreate natural landscapes, but little has been done to investigate their potential for illustrating land cover change using spatio‐temporal data. In this work, remote sensing, geographic information systems (GIS) and visualization techniques were applied to generate realistic computer visualizations depicting the dynamic nature of forested environments. High resolution digital imagery and aerial photography were classified using object‐oriented methods. The resulting classifications, along with preexisting land cover datasets, were used to drive the correct placement of vegetation in the visualized landscape, providing an accurate representation of reality at various points in time. 3D Nature's Visual Nature Studio was used to construct a variety of realistic images and animations depicting forest cover change in two distinct ecological settings. Visualizations from Yellowstone National Park focused on the dramatic impact of the 1988 fire upon the lodgepole pine forest. For a study area in Kansas, visualization techniques were used to explore the continuous human‐land interactions impacting the eastern deciduous forest and tallgrass prairie ecotone between 1941 and 2002. The resulting products demonstrate the flexibility and effectiveness of visualizations for representing spatio‐temporal patterns such as changing forest cover. These geographic visualizations allow users to communicate findings and explore new hypotheses in a clear, concise and effective manner.     

Automation and Democratization of Cartography: An Example of a Mapping System at CEM,University of Durham

Abstract

Over the last three decades, the information revolution and technological advances have led to a paradigm shift in cartography. Recently, the major emphasis has been on 'democratization of cartography', where individuals can be empowered with electronic tools to analyse and visualize spatially referenced data (Morrison, 1994). But, an ordinary user can not effectively use the available GIS tools to visualize geographical data. Therefore, cartographers and/or GIS experts need to develop user-friendly map making systems to facilitate the democratization of cartography. A mapping system was designed using the advanced macro language (AML) and menus of ARC/INFO at the Curriculum for Evaluation and Management Centre (CEM) in Durham University to automate the mapping process (extending from spatial and non-spatial data extraction to the printing of a final map), so that any user can map pupils' locations of any school in the United Kingdom simply by entering some key information. The procedure followed to automate pupils' mapping is described in this paper. A similar programming approach can be helpful to automate mapping systems in a local network and on web.     

Development of a Speech-Based Augmented Reality System to Support Exploration of Cityscape

When people explore new environments they often use landmarks as reference points to help navigate and orientate themselves. This research paper examines how spatial datasets can be used to build a system for use in an urban environment which functions as a city guide, announcing Features of Interest (FoI) as they become visible to the user (not just proximal), as the user moves freely around the city. Visibility calculations for the FoIs were pre‐calculated based on a digital surface model derived from LIDAR (Light Detection and Ranging) data. The results were stored in a text‐based relational database management system (RDBMS) for rapid retrieval. All interaction between the user and the system was via a speech‐based interface, allowing the user to record and request further information on any of the announced FoI. A prototype system, called Edinburgh Augmented Reality System (EARS), was designed, implemented and field tested in order to assess the effectiveness of these ideas. The application proved to be an innovative, ‘non‐invasive’ approach to augmenting the user's reality.     

Earth observations during space shuttle right STS‐29: Discovery's Voyage to the earth

Abstract

Today's Geography graduates are employed by a diverse set of employers, and the jobs these individuals fill are as varied as the field of Geography itself. Geographers who join corporate workplace teams may need to find common grounds for communication with their business school‐trained colleagues. This cross‐disciplinary study examines the curricula of introductory courses in Geographic Information Systems (GIS) and one of its business counterparts, Accounting Information Systems (AIS), through an analysis of GIS and AIS textbooks. The findings suggest that there are areas in which shared background exists. Introductory information about systems, and an understanding of database architectures are the most likely areas of commonality; depending on which GIS text was used during the geographer's training, the geographer may also share background in such areas as systems analysis, database management systems, and data modeling.     

Spatial Analysis of 1991 Gulf War Troop Locations in Relationship with Postwar Health Symptom Reports Using GIS Techniques

Spatial autocorrelation analysis was used to identify spatial patterns of 1991 Gulf War (GW) troop locations in relationship to subsequent postwar diagnosis of chronic multisymptom illness (CMI). Criteria for the diagnosis of CMI include reporting from at least two of three symptom clusters: fatigue, musculoskeletal pain, and mood and cognition. A GIS‐based methodology was used to examine associations between potential hazardous exposures or deployment situations and postwar health outcomes using troop location data as a surrogate. GW veterans from the Devens Cohort Study were queried about specific symptoms approximately four years after the 1991 deployment to the Persian Gulf. Global and local statistics were calculated using the Moran's I and G statistics for six selected date periods chosen a priori to mark important GW‐service events or exposure scenarios among 173 members of the cohort. Global Moran's I statistics did not detect global spatial patterns at any of the six specified data periods, thus, indicating there is no significant spatial autocorrelation of locations over the entire Gulf region for veterans meeting criteria for severe postwar CMI. However, when applying local G* and local Moran's I statistics, significant spatial clusters (primarily in the coastal Dammam/Dharhan and the central inland areas of Saudi Arabia) were identified for several of the selected time periods. Further study using GIS techniques, coupled with epidemiological methods, to examine spatial and temporal patterns with larger sample sizes of GW veterans is warranted to ascertain if the observed spatial patterns can be confirmed.     

A Hybrid Spatio-Temporal Data Model and Structure (HST-DMS) for Efficient Storage and Retrieval of Land Use Information

While the incorporation of geographical and environmental modeling with GIS requires software support for storage and retrieval of spatial information that changes over time, it continues to be an unresolved issue with modern GIS software. Two complementary approaches have been used to manage the spatial and temporal heterogeneity within datasets that use a field‐based representation of the world. Some researchers have proposed new data models that partition space into discrete elements on an as‐needed basis following each temporal event, while others have focused on eliminating duplication of repeated data elements present in spatio‐temporal information. It is proposed in this paper that both approaches have merit and can be combined to create a Hybrid Spatio‐Temporal Data Model and Structure (HST‐DMS) that efficiently supports spatio‐temporal data storage and querying. Specifically, Peuquet and Duan's (1995) Event‐based Spatio‐Temporal Data Model (ESTDM) and the Overlapping R‐tree (Guttman 1984, Tzourmanis et al. 2000) are utilized to create a prototype used to store information about urban expansion for the town of Carbondale, Illinois.     

The Utility of Very High Spatial Resolution Images to Identify Urban Objects

Abstract

Landsat MSS, TM and SPOT XS imageries were used in conjunction with unsupervised, supervised and hybrid classilication techniques to classify land cover types in semi‐arid savannas of Mathison Pastoral Station in the Katherine region of northern Australia. Accuracy assessment was based on field data from 246 ground survey sites over a 745‐km² study area. Of 14 land cover classes identified by traditional mapping means, all combinations of imageries and classification techniques differentiated at least seven land cover types. The overall accuracy for these classifications ranged between 43% and 67%. SPOT XS image delivered the best accuracy followed by TM and MSS; unsupervised classification performed better than supervised and hybrid methods. User's and producer's accuracy of individual land units ranged from 0% to 100%. Riparian woodlands, woodland on limestone slopes, shrubland on clay plains, woodland on limestone plains and shadows were the best‐mapped classes. The land units that were associated with undulating hills were not mapped accurately. However, incorporation of a digital elevation model (DEM) in a GIS improved the overall accuracy. The user's and producer's accuracy of dominant land cover types were also enhanced. The classification results and the efficacy of the techniques at Mathison were similar to those found for a nearby semi‐arid area (Kidman Springs) about 200 km from Mathison. However, the overall accuracy was lower at Mathison than at Kidman Springs. Spectral classification masks were developed from the SPOT XS and TM imageries at Kidman Springs, and were applied to classify SPOT XS and TM imageries at Mathison. Initial results showed that the classification mask could be successfully extrapolated to map dominant land cover types but only with moderate accuracy (50%).     

Ontology‐supported Querying of Geographical Databases

Querying geographical information systems has been recognized as a difficult task for non‐expert users. Furthermore, user queries are often characterized by semantic aspects not directly managed by traditional spatial databases or GIS. Examples of such semantic geospatial queries are the use of implicit spatial relations between objects, or the reference of domain concepts not explicitly represented in data. To handle such queries, we envisage a system that translates natural language queries into spatial SQL statements on a database, thus improving standard GIS with new semantic capabilities. Within this general objective, the contribution of this article is to introduce a methodology to handle semantic geospatial queries issued over a spatial database. This approach captures semantics from an ontology built upon the spatial database and enriched by domain concepts and properties specifically defined to represent the localization of objects. Some examples of the use of the methodology in the urban domain are presented.     

Understanding geographical patterns of a city’s diurnal rhythm from aggregate data of location‐aware services

Do collective behaviors of the daily routine of a city's inhabitants form the periodical cycling of human activity at the city level (here termed the “city's diurnal rhythm”)? If the answer is yes, do there exist geographical patterns in the city's diurnal rhythm? Using a nationwide dataset of observed uses of location‐aware services in the largest Chinese social media platform, we first confirm the significant periodicity in city‐level human activity from the perspective of the aggregate degree of social media uses over a day. We then investigate geographical changes in the diurnal rhythm of human activity and its local variations in different parts of the city, and between weekdays and weekend days, over 340 Chinese cities. Our results show that a city's diurnal rhythm across the whole country exhibits both regular, nationally conspicuous shifts along geographical gradients and locally distinct spatiotemporal changes within the city. Our findings could provide insights into the characterization of the daily routine of city‐level human activity and its geographical patterns, and have potential for several issues in terms of planning, management, and decision‐making related to human population dynamics.     

Mapping spiny aster infestations with QuickBird imagery

QuickBird satellite imagery acquired in June 2003 and September 2004 was evaluated for detecting the noxious weed spiny aster [Leucosyris spinosa (Benth.) Greene] on a south Texas, USA rangeland area. A subset of each of the satellite images representing a diversity of cover types was extracted and used as a study site. The satellite imagery had a spatial resolution of 2.8 m and contained 11-bit data. Unsupervised and supervised classification techniques were used to classify false colour composite (green, red, and near-infrared bands) images of the study site. Imagery acquired in June was superior to that obtained in September for distinguishing spiny aster infestations. This was attributed to differences in spiny aster phenology between the two dates. An unsupervised classification of the June image showed that spiny aster had producer's and user's accuracies of 90% and 93.1%, respectively, whereas a supervised classification of the June image had producer's and user's accuracies of 90% and 81.8%, respectively. These results indicate that high resolution satellite imagery coupled with image analysis techniques can be used successfully for detecting spiny aster infestations on rangelands.     

Ontological Engineering for Interpreting Geospatial Queries

Despite advancements in geographic information system (GIS) technology, the efficient and effective utilization of GIS to solve geospatial problems is a daunting process requiring specialized knowledge and skills. Two of the most important and burdensome tasks in this process are interpretation of geospatial queries and mapping the interpreted results into geospatial data models and geoprocessing operations. With the current state of GIS, there exists a gap between the knowledge user's possess and the knowledge and skills they need to utilize GIS for solving problems. Currently, users resort to training and practice on GIS technology or involving GIS experts. Neither of these options is optimal and there is a need for a new approach that automates geoprocessing tasks using GIS technology. This paper presents an ontological engineering methodology that uses multiple ontologies and the mappings among them to automate certain tasks related to interpretation of geospatial queries and mapping the interpreted results into geospatial data models and geoprocessing operations. The presented methodology includes conceptualization of geospatial queries, knowledge representation for queries, techniques for relating elements in different ontologies, and an algorithm that uses ontologies to map queries to geoprocessing operations.