Researchers Turn to Technology to Discover a Novel Way of Mapping Landscapes

University of Cincinnati

Using computer technology to map patterns of land cover reveals types of landscapes and holds applications for numerous fields in research and planning.

University of Cincinnati researchers are blending technology with tradition, as they discover new and improved methods for mapping landscapes.

The research is newly published in the Journal of Applied Geography (Vol. 45, December 2013) by UC authors Jacek Niesterowicz, a doctoral student in the geography department, and Professor Tomasz Stepinski, the Thomas Jefferson Chair of Space Exploration in the McMicken College of Arts and Sciences (A&S).

The researchers say the analysis is the first to use a technology from a field of machine vision to build a new map of landscape types – a generalization of a popular land cover/land use map. Whereas land cover/land use pertains to physical material at, or utilization of, the local piece of Earth’s surface, a landscape type pertains to a pattern or a mosaic of different land covers over a larger neighborhood.

Machine vision is a subfield of computer science devoted to analyzing and understanding the content of images. A role of a machine vision algorithm is to “see” and interpret images as close to human vision interpretation as possible. Previous uses of the technology have focused on medicine, industry and government, ranging from robotics to face detection.

The UC research focused on a very large map of land cover/land use, called the National Land Cover Database 2006, developed by the U.S. Geological Survey.

Niesterowicz says he developed and applied machine vision-based algorithms to map landscape types in an area of northern Georgia that he selected because of the diverse patterns of land cover. The result allowed the researchers to discover and differentiate 15 distinctive landscape types, including separating forests by their domination of different plant species.

“Before now, people would do this mapping by hand, but if you had 10 maps drawn by 10 people, they would all be different,” says Stepinski.

Niesterowicz says the information uncovered by auto-mapping of landscape types would be useful for a number of fields, ranging from geographic research to land management, urban planning and conservation.

“The good thing about this method is that it doesn’t need to be restricted to land cover or other physical variables – it can be applied as well to socio-economic data, such as U.S. Census data, for example,” says Niesterowicz.

“It’s an entirely new way to conduct geographic research,” says Stepinski. “By leveraging technology developed in the field of computer science, it’s possible to make geography searchable by content. Using this technique, for example, we can quickly discover (using Web-based applications on our website) that farms in Minnesota are on average larger than farms in Ohio, and ask why that is.”

The researchers say future research will involve using the method to identify characteristic landscape types (from waterways to forests to regions influenced by human habitation) over the entire United States.

Stepinski adds that longer-term applications could involve comparisons of landscape types of other countries with those of the United States and to identify characteristic patterns of different geographical entities, such as terrain, or human patterns including socioeconomics and race.

The research out of UC’s Space Informatics Lab was supported by funding from a grant from the National Science Foundation (NSF BCS-1147702), the Polish National Science Centre (DEC-2012/07/B/ST6/012206) and by the UC Space Exploration Institute.

The UC Department of Geography’s Space Informatics Lab – created by Stepinski – develops intelligent algorithms for fast and intuitive exploration of large spatial datasets. UC’s Space Exploration Institute, funded by a $20 million gift to the university by an anonymous donor in 2007, supports numerous areas of space exploration research, including research out of the Space Informatics Lab.

[Source: University of Cincinnati press release]

Using Participatory GIS to Measure Physical Activity and Urban Park Benefits

Landscape and Urban PlanningLandscape and Urban Planning, Volume 121, January 2014, Pages 34–44

By Greg Brown, Morgan Faith Schebella, and Delene Weber

“Highlights:

  • Uses participatory GIS methods to measure physical activities and benefits of urban parks.
  • Examines relationship between park activities and benefits with park type, size, and location.
  • Park type and size are significantly related to the type and amount of physical activities and community benefits received from urban parks.
  • Participatory GIS research methods have limitations but appear useful for examining spatial relationships to inform urban parks planning.

“Previous urban park research has used self-reported surveys and physical activity logs to examine associations between physical activity and park features, size, and distance to participants’ homes. In this study, we used participatory geographic information systems (GIS) methods to explore potential correlates of physical activity and other health benefits in urban parks. Using an internet-based public participation geographic information system (PPGIS) system, study participants identified the spatial locations where they engaged in various types of physical activity and where they received other park benefits—environmental, social, and psychological health benefits. Using an urban park typology, we found that different urban park types provide different opportunities for physical activity with linear parks providing the greatest overall physical benefit while other park types provided important non-physical community benefits. Distance to park was not a significant predictor of physical activity but park size was correlated with physical activity and other park benefits. We discuss the strengths and limitations of using PPGIS methods for understanding the benefits of urban park systems.”

Participatory Development of a New Interactive Tool for Capturing Social and Ecological Dynamism in Conservation Prioritization

Landscape and Urban PlanningLandscape and Urban Planning, Volume 114, June 2013, Pages 80–91

By Petina L. Pert, Scott N. Lieske, and Rosemary Hill

“Highlights

  • The Collaborative Habitat Investment Atlas is an interactive spatial tool.
  • Allows display and rapid adjustment to stakeholder and habitat values.
  • Enables on-the-fly changes to “optimal” landscape designs values.
  • Models “levels of protection” of multiple habitat laws at many scales.
  • Outputs include maps of habitat prioritization for multi-scalar planning.

“Conservation tools have historically been oriented toward optimization for singular decision-makers. A new generation of participatory tools is now appearing and have begun to recognize multiple human values and decision-makers. However, very few tools accommodate a fully interactive process that can account for both ecological and social dynamism and complexity. The Collaborative Habitat Investment Atlas (CHIA) is a participatory tool for conservation prioritization with a strong visual and dynamic capability. The CHIA promotes interaction among stakeholders through two aspects: stakeholders’ ability to alter variable weights to reflect different biodiversity protection requirements; and formula-based dynamic attributes that immediately update results visually.

The overall CHIA modeling process showing engagements and stakeholder values incorporated by slider-bar functionality, data attributes, dynamic updating of attributes (as values adjusted by slider-bars), biodiversity model, level of protection model and threat model and an example of conservation prioritization output map.

The overall CHIA modeling process showing engagements and stakeholder values incorporated by slider-bar functionality, data attributes, dynamic updating of attributes (as values adjusted by slider-bars), biodiversity model, level of protection model and threat model and an example of conservation prioritization output map.

“This paper documents the development of the CHIA within its role as a part of an overall adaptive community-based natural resource management pilot project in Australia’s globally significant humid tropical forests. There are two primary innovations of this approach. The first innovation is the dynamic updating of values and other data, allowing rapid feedback on “what-if?” type questions and enhances the public engagement processes. The second innovation is the recognition and spatial description of different levels of protection across the landscape. Results include parcel-based maps that display the three models: biodiversity importance, level of protection and threat. Additionally, the three models were combined and two examples of suitability maps to aid conservation decision-making are included. When integrated into a conservation planning process the CHIA opens lines of communication, allows exploration of alternatives and enables prioritization of investment that captures the diversity of stakeholder preferences in multiple social decision making contexts.”

A Novel Electronic Data Collection System for Large-Scale Surveys of Neglected Tropical Diseases

PLoS ONE 8(9): e74570, 2013

By Jonathan D. King, Joy Buolamwini, Elizabeth A. Cromwell, Andrew Panfel, Tesfaye Teferi, Mulat Zerihun, Berhanu Melak, Jessica Watson, Zerihun Tadesse, Danielle Vienneau, Jeremiah Ngondi, Jürg Utzinger, Peter Odermatt, and Paul M. Emerson

Background: Large cross-sectional household surveys are common for measuring indicators of neglected tropical disease control programs. As an alternative to standard paper-based data collection, we utilized novel paperless technology to collect data electronically from over 12,000 households in Ethiopia.

Capturing the identification number from a barcode-labeled stool specimen

Capturing the identification number from a barcode-labeled stool specimen

Methodology: We conducted a needs assessment to design an Android-based electronic data collection and management system. We then evaluated the system by reporting results of a pilot trial and from comparisons of two, large-scale surveys; one with traditional paper questionnaires and the other with tablet computers, including accuracy, person-time days, and costs incurred.

Principle Findings: The electronic data collection system met core functions in household surveys and overcame constraints identified in the needs assessment. Pilot data recorders took 264 (standard deviation (SD) 152 sec) and 260 sec (SD 122 sec) per person registered to complete household surveys using paper and tablets, respectively (P = 0.77). Data recorders felt a lack of connection with the interviewee during the first days using electronic devices, but preferred to collect data electronically in future surveys. Electronic data collection saved time by giving results immediately, obviating the need for double data entry and cross-correcting. The proportion of identified data entry errors in disease classification did not differ between the two data collection methods. Geographic coordinates collected using the tablets were more accurate than coordinates transcribed on a paper form. Costs of the equipment required for electronic data collection was approximately the same cost incurred for data entry of questionnaires, whereas repeated use of the electronic equipment may increase cost savings.

Distance between the recorded location of a surveyed household and the cluster centroid

Distance between the recorded location of a surveyed household and the cluster centroid

Conclusions/Significance: Conducting a needs assessment and pilot testing allowed the design to specifically match the functionality required for surveys. Electronic data collection using an Android-based technology was suitable for a large-scale health survey, saved time, provided more accurate geo-coordinates, and was preferred by recorders over standard paper-based questionnaires.”

Population Ecology of Free-Roaming Cats and Interference Competition by Coyotes in Urban Parks

PLOS_ONEPLoS ONE 8(9): e75718, 2013

By Stanley D. Gehrt, Evan C. Wilson, Justin L. Brown, and Chris Anchor

“Free-roaming cats are a common element of urban landscapes worldwide, often causing controversy regarding their impacts on ecological systems and public health. We monitored cats within natural habitat fragments in the Chicago metropolitan area to characterize population demographics, disease prevalence, movement patterns and habitat selection, in addition to assessing the possible influence of coyotes on cats. The population was dominated by adults of both sexes, and 24% of adults were in reproductive condition. Annual survival rate was relatively high (S=0.70, SE=0.10), with vehicles and predation the primary causes of death.

Spatial overlaps between cats and coyotes using the same urban park

Spatial overlaps between cats and coyotes using the same urban park

“Size of annual home range varied by sex, but not reproductive status or body weight. We observed partitioning of the landscape by cats and coyotes, with little interspecific overlap between core areas of activity. Coyotes selected for natural habitats whereas cats selected for developed areas such as residences. Free-roaming cats were in better condition than we predicted, but their use of natural habitat fragments, and presumably their ecological impact, appeared to be limited by coyotes through intraguild competition.”

Geospatial Services in the Cloud

Computers & GeosciencesComputers & Geosciences, Published Online 23 October 2013

By Konstantinos Evangelidis, Konstantinos Ntouros, Stathis Makridis, and Constantine Papatheodorou

“Highlights:

  • Geospatial services in the Cloud for both spatial data acquisition and processing
  • Interaction UML diagram representing requests for and responses from OGC services
  • Multi tier architecture with open source software and OGC standards implementations
  • Challenge: to specify potential geospatial processes for future WPS implementations
  • From desktop and proprietary web applications to open GIS systems in the Cloud

“Data semantics play an extremely significant role in spatial data infrastructures by providing semantic specifications to geospatial data and enabling in this way data sharing and interoperability. By applying, on the fly, composite geospatial processes on the above data it is possible to produce valuable geoinformation over the web directly available and applicable to a wide range of geo-activities of significant importance for the research and industry community. Cloud computing may enable geospatial processing since it refers to, among other things, efficient computing resources providing on demand processing services. In this context, we attempt to provide a design and architectural framework for web applications based on open geospatial standards. Our approach includes, in addition to geospatial processing, data acquisition services that are essential especially when dealing with satellite images and applications in the area of remote sensing and similar fields. As a result, by putting in a common framework all data and geoprocesses available in the cloud, it is possible to combine the appropriate services in order to produce a solution for a specific need.”

Call for Papers — Geodesign: Changing the World, Changing Design

Landscape and Urban Planning (LAND)Guest Editors: Frederick R. Steiner and Allan W. Shearer, School of Architecture, The University of Texas at Austin

Geodesign is an emerging, interdisciplinary field that has evolved from Geographic Information Systems (GIS) and encompasses digital, two-, three-, and four-dimensional representation tools developed in the environmental design disciplines. Over a relatively short span of time, Geodesign has gone from a neologism to the topic of international professional conferences to the focus of research centers to the premise for new classes at many institutions of higher learning and degrees at leading universities. Yet, despite so much activity—or, perhaps, because of it—there is no commonly agreed upon definition for the word.
The purpose of the special issue of Landscape and Urban Planning (LAND) is to provide a basis for common understanding of what Geodesign is by asking what Geodesign does. We seek papers that examine how questions of environmental change have been posed in Geodesign and that demonstrate how the answers allow for, or demand, new models of design practice and education.
We welcome such investigations in the forms of review articles, research articles, case studies, and discussions about research needs and pedagogy. We anticipate submissions that draw upon the disciplines of geography, computer science, and the environmental sciences, as well as landscape architecture, community and regional planning, and architecture.

Abstract and Manuscript Submission
An abstract of 800 words or less, specifying title, author(s), affiliation and e-mail address, should be sent to Dr. Allan W. Shearer (ashearer@austin.utexas.edu) by 15 February 2014. Abstracts will be shortlisted by the editorial panel against the criteria of originality, methodological quality, and relevance. Authors of abstracts demonstrating clear scholarly merits will be invited to submit a full manuscript.
Invited manuscripts should be should be between 4,000–8,000 words and submitted through the LAND website by 15 June 2014. All papers submitted for this Special Issue will undergo the usual LAND peer review process. Details on article type and format are available from the LAND journal website at: http://www.journals.elsevier.com/landscape-and-urban-planning