E. Lynn Usery

Using Geometrical, Textural, and Contextual Information of Land Parcels for Classification of Detailed Urban Land Use

Detailed urban land use data are important to government officials, researchers, and businesspeople for a variety of purposes. This article presents an approach to classifying detailed urban land use based on geometrical, textural, and contextual information of land parcels. An area of 6 by 14 km in Austin, Texas, with land parcel boundaries delineated by the Travis Central Appraisal District of Travis County, Texas, is tested for the approach. We derive fifty parcel attributes from relevant geographic information system (GIS) and remote sensing data and use them to discriminate among nine urban land uses: single family, multifamily, commercial, office, industrial, civic, open space, transportation, and undeveloped. Half of the 33,025 parcels in the study area are used as training data for land use classification and the other half are used as testing data for accuracy assessment. The best result with a decision tree classification algorithm has an overall accuracy of 96 percent and a kappa coefficient of 0.78, and two naive, baseline models based on the majority rule and the spatial autocorrelation rule have overall accuracy of 89 percent and 79 percent, respectively. The algorithm is relatively good at classifying single-family, multifamily, commercial, open space, and undeveloped land uses and relatively poor at classifying office, industrial, civic, and transportation land uses. The most important attributes for land use classification are the geometrical attributes, particularly those related to building areas. Next are the contextual attributes, particularly those relevant to the spatial relationship between buildings, then the textural attributes, particularly the semivariance texture statistic from 0.61-m resolution images.

Geospatial data resampling and resolution effects on watershed modeling: A case study using the agricultural non-point source pollution model

Abstract.Researchers have been coupling geographic information systems (GIS) data handling and processing capability to watershed and water-quality models for many years. This capability is suited for the development of databases appropriate for water modeling. However, it is rare for GIS to provide direct inputs to the models. To demonstrate the logical procedure of coupling GIS for model parameter extraction, we selected the Agricultural Non-Point Source (AGNPS) pollution model. Investigators can generate data layers at various resolutions and resample to pixel sizes to support models at particular scales. We developed databases of elevation, land cover, and soils at various resolutions in four watersheds. The ability to use multiresolution databases for the generation of model parameters is problematic for grid-based models. We used database development procedures and observed the effects of resolution and resampling on GIS input datasets and parameters generated from those inputs for AGNPS. Results indicate that elevation values at specific points compare favorably between 3- and 30-m raster datasets. Categorical data analysis indicates that land cover classes vary significantly. Derived parameters parallel the results of the base GIS datasets. Analysis of data resampled from 30-m to 60-, 120-, 210-, 240-, 480-, 960-, and 1920-m pixels indicates a general degradation of both elevation and land cover correlations as resolution decreases. Initial evaluation of model output values for soluble nitrogen and phosphorous indicates similar degradation with resolution.

Category Theory and the Structure of Features in Geographic Information Systems

This paper explores feature-based approaches to geographic information systems (GISs) and develops a conceptual framework that can be used as a base for structuring geographic entities as features. The concepts are drawn from set theory, cognitive category theory, and cartographic principles of abstraction and generalization. Set theory provides an analytical approach to determining geographic features, an example of which is multispectral classification. This approach is limited when applied to geomorphologic phenomena such as hills and valleys, but cognitive category theory with the development of prototype concepts is applicable in this instance. Cartographic abstraction principles recognize the dependence of map features on purpose and scale. Combining set theory, cognitive category theory, and cartographic abstraction principles yields a base for the development of application-specific and resolution-dependent geographic features for use in GISs. The conclusion that features must subscribe to cogniti...

Implications of Web Mercator and Its Use in Online Mapping

Online interactive maps have become a popular means of communicating with spatial data. In most online mapping systems, Web Mercator has become the dominant projection. While the Mercator projection has a long history of discussion about its inappropriateness for general-purpose mapping, particularly at the global scale, and seems to have been virtually phased out for general-purpose global-scale print maps, it has seen a resurgence in popularity in Web Mercator form. This article theorizes on how Web Mercator came to be widely used for online maps and what this might mean in terms of data display, technical aspects of map generation and distribution, design, and cognition of spatial patterns. The authors emphasize details of where the projection excels and where it does not, as well as some of its advantages and disadvantages for cartographic communication, and conclude with some research directions that may help to develop better solutions to the problem of projections for general-purpose, multi-scale Web mapping. Les cartes interactives en ligne sont devenues un moyen populaire de communiquer au moyen de données spatiales. Dans la plupart des systèmes de cartographie en ligne, la projection de Mercator sur le Web est devenue la projection dominante. La projection de Mercator soulève depuis longtemps des discussions sur son caractère inapproprié en cartographie générale, particulièrement à l’échelle de la planète, et elle semble avoir à peu près disparu des cartes imprimées à l’échelle mondiale d’usage général, mais on a constaté un regain de popularité de la projection de Mercator sur le Web. Cet article présente une théorie sur la façon dont la projection de Mercator sur le Web s’est généralisée pour les cartes en ligne et sur ce que cela pourrait signifier pour l’affichage des données, les aspects techniques de la production et de la distribution de cartes, la conception et la cognition des tendances spatiales. Les auteurs mettent en évidence des détails sur les aspects où la projection excelle et sur ceux où elle n’excelle pas, ainsi que certains de ses avantages et inconvénients pour la communication cartographique. Ils concluent par des pistes de recherche qui peuvent aider à trouver une meilleure solution au problème des projections destinées à la cartographie générale à échelles multiples sur le Web.

Projecting Global Datasets to Achieve Equal Areas

Scientists routinely accomplish global modeling in the raster domain, but recent research has indicated that the transformation of large areas through map projection equations leads to errors. This research attempts to gauge the extent of map projection and resampling effects on the tabulation of categorical areas by comparing the results of three datasets for seven common projections. The datasets, Global Land Cover, Holdridge Life Zones, and Global Vegetation, were compiled at resolutions of 30 arc-second, ½ degree, and 1 degree, respectively. These datasets were projected globally from spherical coordinates to plane representations. Results indicate significant problems in the implementation of global projection transformations in commercial software, as well as differences in areal accuracy across projections. The level of raster resolution directly affects the accuracy of areal tabulations, with higher resolution yielding higher accuracy. If the raster resolution is high enough for individual pixels to approximate points, the areal error tends to zero. The 30-arc-second cells appear to approximate this condition.

Design and development of linked data from The National Map

The development of linked data on the World-Wide Web provides the opportunity for the U.S. Geological Survey USGS to supply its extensive volumes of geospatial data, information, and knowledge in a machine interpretable form and reach users and applications that heretofore have been unavailable. To pilot a process to take advantage of this opportunity, the USGS is developing an ontology for The National Map and converting selected data from nine research test areas to a Semantic Web format to support machine processing and linked data access. In a case study, the USGS has developed initial methods for legacy vector and raster formatted geometry, attributes, and spatial relationships to be accessed in a linked data environment maintaining the capability to generate graphic or image output from semantic queries. The description of an initial USGS approach to developing ontology, linked data, and initial query capability from The National Map databases is presented.

An ontology design pattern for surface water features

Surface water is a primary concept of human experience but concepts are captured in cultures and languages in many different ways. Still, many commonalities exist due to the physical basis of many of the properties and categories. An abstract ontology of surface water features based only on those physical properties of landscape features has the best potential for serving as a foundational domain ontology for other more context-dependent ontologies. The Surface Water ontology design pattern was developed both for domain knowledge distillation and to serve as a conceptual building-block for more complex or specialized surface water ontologies. A fundamental distinction is made in this ontology between landscape features that act as containers (e.g., stream channels, basins) and the bodies of water (e.g., rivers, lakes) that occupy those containers. Concave (container) landforms semantics are specified in a Dry module and the semantics of contained bodies of water in a Wet module. The pattern is implemented in OWL, but Description Logic axioms and a detailed explanation is provided in this paper. The OWL ontology will be an important contribution to Semantic Web vocabulary for annotating surface water feature datasets. Also provided is a discussion of why there is a need to complement the pattern with other ontologies, especially the previously developed Surface Network pattern. Finally, the practical value of the pattern in semantic querying of surface water datasets is illustrated through an annotated geospatial dataset and sample queries using the classes of the Surface Water pattern.

Topographic Mapping Data Semantics Through Data Conversion and Enhancement

This paper presents research on the semantics of topographic data for triples and ontologies to blend the capabilities of the Semantic Web and The National Map of the U.S. Geological Survey. Automated conversion of relational topographic data of several geographic sample areas to the triple data model standard resulted in relatively poor semantic associations. Further research employed vocabularies of feature type and spatial relation terms. A user interface was designed to model the capture of non-standard terms relevant to public users and to map those terms to existing data models of The National Map through the use of ontology. Server access for the study area triple stores was made publicly available, illustrating how the development of linked data may transform institutional policies to open government data resources to the public. This paper presents these data conversion and research techniques that were tested as open linked data concepts leveraged through a user-centered interface and open USGS server access to the public.

The Digital Transition in Cartography: USGS Data Innovations, 1970s

The 1970s were a period of data innovation in the evolution of geographic information system technology and geographic information science. Each new form of data innovated new data structures, which in a way is a new model of geographic information, providing a base for new applications of topographic map data. U.S. Geological Survey scientists were actively engaged in this data model development with at least four different objectives: the conversion of existing cartographic data from topographic maps into a dataset built from a data model that incorporated topological structure in the form of a Digital Line Graph; the building of a nationwide database of land cover, also topologically structured, but available in raster format, in the Land Use Data Analysis Program; the automation of producing graphic products including topographic maps with the Digital Cartographic Software System and the Graphic Map Production System; and terrain representation as a Digital Elevation Model, a byproduct of the orthophoto generation process and new digital production techniques. These innovations in data-model development were followed by sustained programs of nationwide data production from these efforts providing digital geographic data that catalyzed the GIScience industry in the 1980s and 1990s. The U.S. Geological Survey efforts in this area continue with the evolution of these 1970s data models to the seamless nationwide databases of today of The National Map and the new graphic product called US Topo.

Data Layer Integration for The National Map of the United States

The integration of geographic data layers in multiple raster and vector formats, from many different organizations and at a variety of resolutions and scales, is a significant problem for The National Map of the United States being developed by the U.S. Geological Survey. Our research has examined data integration from a layer-based approach for five of The National Map data layers: digital orthoimages, elevation, land cover, hydrography, and transportation. An empirical approach has included visual assessment by a set of respondents with statistical analysis to establish the meaning of various types of integration. A separate theoretical approach with established hypotheses tested against actual data sets has resulted in an automated procedure for integration of specific layers and is being tested. The empirical analysis has established resolution bounds on meanings of integration with raster datasets and distance bounds for vector data. The theoretical approach has used a combination of theories on cartographic transformation and generalization, such as Topfer’s radical law, and additional research concerning optimum viewing scales for digital images to establish a set of guiding principles for integrating data of different resolutions.