I am trying to get the Feature Class name (from Iterate Feature Classes) into a new field. I have been following the models described in the following two threads.
Using Value from Iterate Feature Selection to Calculate Field in ArcMap 10.1 ModelBuilder
However, whenever I run the model I get the following error message
ERROR 000539: Error running expression: CF0140r01 Traceback (most recent call last): File "", line 1, in NameError: name 'CF0140r01' is not defined Failed to execute (Calculate Field)
I have tried it using Parse Path using %Value% in the expression field (here is my model) (http://i.imgur.com/aOFgCgJ.jpg">
and the Calculate Field
I have tried to simplify this model (not even sure why I need the Feature Class to Feature Class tool, I just have it there because an earlier thread I was using to build this had that in it). Here is the updated, and simplified model, with the Error Message I am getting. FYI: the Expression in the Calculate Field tool is %Name%
The ultimate in stupidity.
All I was missing (in any of the ways I approached it) was "QUOTATION MARKS" around the expression.
ie. Expression = "%Value%" if using Parse Path, or "%Name%" if not using Parse Path
I would imagine the problem is with the fact you named the output of FeatureClass to FeatureClass tool using the inline substitution syntax which makes no sense. Rename that to something like "output" (do not include the quotes!). Then your field calculate tool expression stays as %Name% which is the inline substitution coming from the variable Name which is one of the outputs of the iterator.
Physical geography (or physiogeography) focuses on geography as an Earth science. It aims to understand the physical lithosphere, hydrosphere, atmosphere, pedosphere, and global flora and fauna patterns (biosphere). Physical geography can be divided into the following broad categories:
- Biogeography Climatology & paleoclimatology Coastal geography Env. geog. & management
- Geodesy Geomorphology Glaciology Hydrology & Hydrography
Human geography is a branch of geography that focuses on the study of patterns and processes that shape human interaction with various environments. It encompasses human, political, cultural, social, and economic aspects. While the major focus of human geography is not the physical landscape of the Earth (see physical geography), it is hardly possible to discuss human geography without referring to the physical landscape on which human activities are being played out, and environmental geography is emerging as a link between the two. Human geography can be divided into many broad categories, such as:
- Cultural geography Development geography Economic geography Health geography
- Historical & Time geog. Political geog. & Geopolitics Pop. geog. or Demography Religion geography
- Social geography Transportation geography Tourism geography Urban geography
Various approaches to the study of human geography have also arisen through time and include:
- * Behavioral geography
- * Feminist geography
- * Culture theory
- * Geosophy
Environmental geography is the branch of geography that describes the spatial aspects of interactions between humans and the natural world. It requires an understanding of the traditional aspects of physical and human geography, as well as the ways in which human societies conceptualize the environment.
Environmental geography has emerged as a bridge between human and physical geography as a result of the increasing specialisation of the two sub-fields. Furthermore, as human relationship with the environment has changed as a result of globalization and technological change a new approach was needed to understand the changing and dynamic relationship. Examples of areas of research in environmental geography include emergency management, environmental management, sustainability, and political ecology.
Digital Elevation Model (DEM)
Geomatics is a branch of geography that has emerged since the quantitative revolution in geography in the mid 1950s. Geomatics involves the use of traditional spatial techniques used in cartography and topography and their application to computers. Geomatics has become a widespread field with many other disciplines using techniques such as GIS and remote sensing. Geomatics has also led to a revitalization of some geography departments especially in Northern America where the subject had a declining status during the 1950s.
Geomatics encompasses a large area of fields involved with spatial analysis, such as Cartography, Geographic information systems (GIS), Remote sensing, and Global positioning systems (GPS).
Regional geography is a branch of geography that studies the regions of all sizes across the Earth. It has a prevailing descriptive character. The main aim is to understand or define the uniqueness or character of a particular region which consists of natural as well as human elements. Attention is paid also to regionalization which covers the proper techniques of space delimitation into regions.
Regional geography is also considered as a certain approach to study in geographical sciences (similar to quantitative or critical geographies, for more information see History of geography).
* Urban planning, regional planning and spatial planning: use the science of geography to assist in determining how to develop (or not develop) the land to meet particular criteria, such as safety, beauty, economic opportunities, the preservation of the built or natural heritage, and so on. The planning of towns, cities, and rural areas may be seen as applied geography.
* Regional science: In the 1950s the regional science movement led by Walter Isard arose, to provide a more quantitative and analytical base to geographical questions, in contrast to the descriptive tendencies of traditional geography programs. Regional science comprises the body of knowledge in which the spatial dimension plays a fundamental role, such as regional economics, resource management, location theory, urban and regional planning, transport and communication, human geography, population distribution, landscape ecology, and environmental quality.
* Interplanetary Sciences: While the discipline of geography is normally concerned with the Earth, the term can also be informally used to describe the study of other worlds, such as the planets of the Solar System and even beyond. The study of systems larger than the earth itself usually forms part of Astronomy or Cosmology. The study of other planets is usually called planetary science. Alternative terms such as Areology (the study of Mars) have been proposed, but are not widely used.
As spatial interrelationships are key to this synoptic science, maps are a key tool. Classical cartography has been joined by a more modern approach to geographical analysis, computer-based geographic information systems (GIS).
In their study, geographers use four interrelated approaches:
* Systematic – Groups geographical knowledge into categories that can be explored globally.
* Regional – Examines systematic relationships between categories for a specific region or location on the planet.
* Descriptive – Simply specifies the locations of features and populations.
* Analytical – Asks why we find features and populations in a specific geographic area.
Cartography studies the representation of the Earth’s surface with abstract symbols (map making). Although other subdisciplines of geography rely on maps for presenting their analyses, the actual making of maps is abstract enough to be regarded separately. Cartography has grown from a collection of drafting techniques into an actual science.
Cartographers must learn cognitive psychology and ergonomics to understand which symbols convey information about the Earth most effectively, and behavioral psychology to induce the readers of their maps to act on the information. They must learn geodesy and fairly advanced mathematics to understand how the shape of the Earth affects the distortion of map symbols projected onto a flat surface for viewing. It can be said, without much controversy, that cartography is the seed from which the larger field of geography grew. Most geographers will cite a childhood fascination with maps as an early sign they would end up in the field.
Geographic information systems
Geographic information systems (GIS) deal with the storage of information about the Earth for automatic retrieval by a computer, in an accurate manner appropriate to the information’s purpose. In addition to all of the other subdisciplines of geography, GIS specialists must understand computer science and database systems. GIS has revolutionized the field of cartography nearly all mapmaking is now done with the assistance of some form of GIS software. GIS also refers to the science of using GIS software and GIS techniques to represent, analyze and predict spatial relationships. In this context, GIS stands for Geographic Information Science.
Remote sensing can be defined as the art and science of obtaining information about Earth features from measurements made at a distance. Remotely sensed data comes in many forms such as satellite imagery, aerial photography and data obtained from hand-held sensors. Geographers increasingly use remotely sensed data to obtain information about the Earth’s land surface, ocean and atmosphere because it: a) supplies objective information at a variety of spatial scales (local to global), b) provides a synoptic view of the area of interest, c) allows access to distant and/or inaccessible sites, d) provides spectral information outside the visible portion of the electromagnetic spectrum, and e) facilitates studies of how features/areas change over time. Remotely sensed data may be analyzed either independently of, or in conjunction with, other digital data layers (e.g., in a Geographic Information System).
Geographic quantitative methods
Geostatistics deal with quantitative data analysis, specifically the application of statistical methodology to the exploration of geographic phenomena. Geostatistics is used extensively in a variety of fields including: hydrology, geology, petroleum exploration, weather analysis, urban planning, logistics, and epidemiology. The mathematical basis for geostatistics derives from cluster analysis, linear discriminant analysis and non-parametric statistical tests, and a variety of other subjects. Applications of geostatistics rely heavily on geographic information systems, particularly for the interpolation (estimate) of unmeasured points. Geographers are making notable contributions to the method of quantitative techniques.
Geographic qualitative methods
Geographic qualitative methods, or ethnographical research techniques, are used by human geographers. In cultural geography there is a tradition of employing qualitative research techniques also used in anthropology and sociology. Participant observation and in-depth interviews provide human geographers with qualitative data.
Some Geographic Information Systems Types You Need to Know
ArcGIS: This is a cloud-based mapping solution. It offers robust yet simple online mapping tools that can be utilized even by lay users to create and share attractive maps. Besides maps, you can also use ArcGIS Online for collaboration, administration, and analytics. On top of that, this platform offers unique capabilities for using location-based analysis to improve your business.
The most notable benefit of ArcGIS is the use of a geographical information system (GIS). The GIS helps organizations of all sizes to question, analyze, visualize, and interpret data to gain an understanding of relationships, trends, and patterns. The system provides improved communication, better record-keeping, cost savings, and better decision-making.
SuperGIS Desktop: A robust desktop GIS mapping software that gives you the capabilities to analyze, visualize, manage, and edit geographic data. Then, you can exhibit the results on progressional-quality maps. It is easy to customize the application’s various functions for your needs. Plus, the platform supports various data types and offers sophisticated data processing tools.
With SuperGIS Desktop’s Process Designer, an urban plan can create and automate customized geospatial workflows. It is also capable of batch processing so that complex projects and reports become less tedious to do and can be completed much faster. Users can also publish workflows online in order to make them accessible anywhere that there is a web-enabled device.
Simple GIS Client: A feature-packed GIS software platform that runs on MS Windows. Besides desktop, it is lightweight enough to be used on Windows tablets and laptops for tasks such as trip planning and field data collection. This app is recommended for editing and viewing shapefiles as it supports multi-user write and read access to shapefiles. The vendor offers a 30-day free trial.
With Simple GIS Client, city planning companies have a powerfully-built software that runs on Windows machines but still light enough to run on Windows-powered laptops and tablets for field use. This allows users to be mobile while still working with the desktop client.
Bentley Map: This is a 2D and 3D desktop mapping and GIS software. You can create, maintain, share, and analyze your business, engineering, and geospatial data in a MicroStation environment. Offers a flexible yet strong application program interface (API) to help you build custom GIS applications. Use the mobile app to enhance the productivity of your field staff.
With Bentley Map, urban planners can access multiple online spatial databases while they are working. This feature enables organizations in storing and managing a large amount of spatial data. Plus, the application connects with Oracle Spatial to allow users to modify in 2D and 3D directly. This way, rasterized and vectored data are sent straight to a central repository and then made accessible from the desktop for ease of operations and enhanced productivity.
Maptitude: A mapping software product from Caliper Corporation that allows you to view, integrate, and edit maps. It is an easy-to-use package that helps you enjoy the benefits of spatial analysis and desktop mapping. Maptitude tells you where your consumers are and find out where sales are maximum. On top of that, you can discover hidden opportunities and get answers to geographic questions that affect your operations.
Maptitude is a GIS mapping software that helps the urban developers to discover prime locations for their research based on foot traffic, research zoning standards, property value, surrounding amenities, demographic analysis, and more. With this feature, you are able to identify locations where your business can thrive and provide high-quality service to your target demographic.
MapViewer: This is a GIS mapping software that offers powerful tools that help urban planners to create professional-grade thematic maps and allows users to take complete control over their spatial data. The mapping features along with flexible map-display, advanced analytics and instant communication make the software an attractive proposition for business professionals, GIS analysts and individuals who work with spatially distributed data.
Several map types are supported by the software, including base, pin, choropleth, contour, density, symbol, territory, vector, line graph, gradient, flow, bar, pie, prism, multi-graph and cartogram.
The geoprocessing tools of the software allow making more informed decisions and highlight areas of interest, which helps narrow down data for further analysis.
MapViewer ensures immediate access to maps and online data and is natively compatible with various file formats, including DXF, SHP and XLSX. It also makes it easier to share information and collaborate more effectively. The streamlined workflows reduce the time it takes to go from raw data to actual maps and helps achieve desired results within minutes.
All the Key Features of GIS Software Listed Above
Routing tools: These give the ability to design accurate stops and to manage, compare, and manipulate routes.
Territory tools: Urban planners need territory creation tools that help to handle data tables and to visually represent and manipulate service areas.
Reports: Top GIS software applications enable you to intuitively and quickly create top quality reports in PDF and MS Excel file formats.
Interoperability: Quality GIS platforms support the most recent data standards and a range of file formats including those for MS Excel, MS MapPoint, and Google Maps.
Data: On top of that, they provide new data sets for different countries with access to the latest available geographic information.
Logistics and Operations: A distance and drive-time feature that creates an Excel table that shows the costs of travel between any numbers of locations. You can quickly find out the nearest and backup locations ranked by distance or travel time.
Location analysis: This technique is used to identify ideal locations for new retail outlets.
Geographic Information Systems (GIS) Benefits Benefits
- Decision making is improved because you get detailed as well as specific information about locations.
- You can increase efficiency and reduce expenses especially regarding scheduling timetables, fleet movements, and maintenance schedules.
- The visual format is easy to understand which helps to enhance communication between involved departments or organizations.
- GIS software enables effortless recordkeeping as it easily records geographical changes.
- Helps in geographic management as you can get to know what is happening in a geographic location and space, and use it to plan actions.
- Cloud GIS software solutions facilitate instantaneous collaboration.
- Offers enhanced transparency for citizen engagement.
- Enables identification of underserved and at-risk populations in a community.
- Helps in planning and allocation of resources.
- Improves management of natural resources.
- Enhances communication during a crisis.
- Lastly, GIS software can be used to plan for the impact of demographic changes in a community.
In a recent survey by GIS Professional throws light on the issues faced by users of GIS software. In the survey, 32% identified data accuracy and 31% named efficient data management as the main challenges.
In addition, 12% named the open source and open data as both a trend and a challenge. 8% named location privacy as an issue to be wary of. The availability of open-source data and software is creating new users who understand the advantages of GIS but lack the knowledge to use the system properly.
Digital Maps Are Giving Scholars the Historical Lay of the Land
Few battles in history have been more scrutinized than Gettysburg’s three blood-soaked days in July 1863, the turning point in the Civil War. Still, there were questions that all the diaries, official reports and correspondence couldn’t answer precisely. What, for example, could Gen. Robert E. Lee actually see when he issued a series of fateful orders that turned the tide against the Confederate Army nearly 150 years ago?
Now historians have a new tool that can help. Advanced technology similar to Google Earth, MapQuest and the GPS systems used in millions of cars has made it possible to recreate a vanished landscape. This new generation of digital maps has given rise to an academic field known as spatial humanities. Historians, literary theorists, archaeologists and others are using Geographic Information Systems — software that displays and analyzes information related to a physical location — to re-examine real and fictional places like the villages around Salem, Mass., at the time of the witch trials the Dust Bowl region devastated during the Great Depression and the Eastcheap taverns where Shakespeare’s Falstaff and Prince Hal caroused.
Like the crew on the starship Enterprise, humanists are exploring a new frontier of the scholarly universe: space.
“Mapping spatial information reveals part of human history that otherwise we couldn’t possibly know,” said Anne Kelly Knowles, a geographer at Middlebury College in Vermont. “It enables you to see patterns and information that are literally invisible.” It adds layers of information to a map that can be added or taken off at will in various combinations the same location can also be viewed back and forth over time at the click of a mouse.
Today visitors to Gettysburg can climb to the cupola of the Lutheran seminary, where Lee stationed himself on July 2, the second day of fighting or stand on Seminary Ridge, where the next day Lee watched from behind the Confederate lines as thousands of his men advanced across the open farmland to their deaths in the notorious Pickett’s Charge. But they won’t see what the general saw because the intervening years have altered the topography. Over the decades a quarry, a reservoir, different plants and trees have been added, and elevations have changed as a result of mechanical plowing and erosion.
Geographic Information Systems, known as GIS, allowed Ms. Knowles and her colleagues to recreate a digital version of the original Gettysburg battlefield from historical maps, documented descriptions of troop positions and scenery, and renderings of historic roads, fences, buildings and vegetation. “The only way I knew how to answer the question,” about what Lee saw, Ms. Knowles said, “was to recreate the ground digitally using GIS and then ask the GIS program: What can you see from a certain position on the digital landscape, and what can you not see?”
She said her work helps “make Lee’s dilemma more vivid and personal.” Nineteenth-century military leaders relied primarily on their own eyes, and small differences in elevation were strategically important. “Lee probably could not have possibly seen the massive federal forces building up on the eastern side of the battlefield on Day 2 during the famous attack on Little Round Top,” Ms. Knowles said. “He had to make decisions with really inadequate information.”
So did Lt. Gen. James Longstreet, who was vilified in the Confederacy partly because of his decision on July 2 to take his troops on a long countermarch to avoid detection rather than attack Little Round Top directly. The march “made Longstreet the goat of Gettysburg,” Ms. Knowles said. But there was no way that Longstreet could have seen that Little Round Top was undefended at the time. “The analysis says Longstreet made the best decision he could,” added Ms. Knowles, who is currently working on a digital map of the Nazis’ territorial conquests and forced labor camps in Europe.
New methods of computer-assisted geographic analysis can also offer new interpretations of familiar topics. Geoff Cunfer, a historian at the University of Saskatchewan, revisited causes of the 1930s Dust Bowl by analyzing data from all 208 counties in Texas, New Mexico, Colorado, Oklahoma and Kansas that were affected, an impossible undertaking without this system. He found that the traditional explanation of farmers’ extensively plowing the land without care for environmental limits was only true in some places. Barely plowed Southern counties also suffered from the plague of dust. Using reports of annual precipitation, unplowed grassland, wind direction, droughts, agricultural censuses, historical studies and previous reports on dust storms — “a messy shoebox full of newspaper clippings” — Mr. Cunfer created data sets that could be plotted on maps.
He discovered that dust storms regularly occurred in the 19th century and were a natural part of plains ecology before any plowing occurred, but were “unreported and unpublicized,” he said.
Advanced mapping tools, around since the 1960s, were initially used primarily for environmental analysis and urban planning. In the late 1980s and 1990s geographic historical information systems enabled scholars to take census information and other quantifiable data and plot changes in a location over time. By the late 1990s professional networks and organizations began to form, but this sort of mapmaking remained on the margins.
This system insists on precision, explained David Bodenhamer, a historian at Indiana University who is editing a series of books on the spatial humanities. Every bit of data is represented by a point, a closed polygon or a pixel on a map. Critics complained this exactitude did not allow for multiple viewpoints.
By the mid-2000s technological developments enabled scholars to break out of the strict map format and add photographs and texts to create what Mr. Bodenhamer calls “deep maps,” which can capture more than one perspective.
In 2005 Mr. Bodenhamer, collaborating with colleagues at Florida State University and West Virginia University, helped create the Polis Center in Indianapolis, which calls itself the first virtual spatial humanities center. One of their early projects was financed by the National Endowment for the Humanities: a detailed digital atlas of religion in North America that broke down denominations by county. Geographic Information Systems make it possible to analyze complex and changing patterns of political preferences, religious affiliation, migration and cultural influence in fresh ways by linking them to geography, Mr. Bodenhamer said.
Benjamin Ray, the director of the Salem Witch Trials Documentary Archive at the University of Virginia, said visualizing data helps you to analyze it. “The eye is a very good sorter of patterns,” he said. Mr. Ray had wondered why witchcraft charges spread so rapidly and widely in 1692 from Salem across 25 communities, whereas previous incidents had remained small and localized. When he plotted the accusations on a digital map that showed a progression over time, it struck him immediately: “It looked like a kind of epidemic, almost a disease.”
That made him examine what the Salem authorities did differently this time that failed to contain the hysteria. He found that the judges broke their own rules by permitting people to make accusations without posting a monetary bond, letting accusers be interviewed in groups and allowing “spectral evidence” — evidence only visible to the accuser — as sufficient for a conviction. After adding church affiliation to the map, he saw there was also a correlation between church membership and the accusers, which reflected a rift in the village over support for the minister.
Mr. Bodenhamer said the humanities had become too abstract and neglected physical space. The value of what scholars are calling “the spatial turn,” he added, is that “it allows you to ask new questions: Why is it that something developed here and not somewhere else, what is it about the context of this place?”
Akyürek B, Duru M, Sütçü Y, Papak I, Şaroglu F, Pehlivan N, Gönenç O, Granit S, Yaşar T (1997) 1:100.000 ölçekli açýnsama nitelikli Türkiye Jeoloji Haritaları No: 55, Ankara-F15 Paftası Maden Tetkik ve Arama Genel Müdürlügü Jeoloji Etütleri Dairesi, Ankara
Baxter JW, Eyles JD, Elliot SJ (1999) From siting principles to siting practices: a case study of discord among trust, equity and community participation. J Environ Plann Manage 42(4):501–525
Eastman JR (1993) IDRISI: a grid based geographic analysis system, version 4.1. Graduate School of Geography, Clark University, Worcester
Erkut E, Moran SR (1991) Locating obnoxious facilities in the public sector: an application of the hierarchy process to municipal landfill siting decisions. Socioecon Plann Sci 25(2):89–102
Janssen R (1992) Multiobjective decision support for environmental management. Kluwer, Dordrecht, 232 p
Kao JJ, Lin H (1996) Multifactor spatial analysis for landfill siting. J Environ Eng 122(10):902–908
Lober DJ (1995) Resolving the siting impasse: modeling social and environmental locational criteria with a geographic information system. J Am Plann Assoc 61(4):482–495
Malczewski J (1997) Propagation of errors in multicriteria location analysis: a case study. In: Fandel G, Gal T (eds) Multiple criteria decision making. Springer, Berlin Heidelberg New York, pp 154–155
Malczewski J (1999) GIS and multicriteria decision analysis. John Wiley and Sons Inc., 392p
Şener B (2004) Landfill site selection by using geographic information systems. M.Sc Thesis, METU, 114 p. http://www.rsgis.metu.edu.tr
Saaty TL (1980) The analytic hierarchy process. McGraw Hill, New York
Siddiqui MZ, Everett JW, Vieux BE (1996) Landfill siting using geographic information systems: a demonstration. J Environ Eng 122(6):515–523
Süzen ML, Doyuran V (2004) Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu Catchment, Turkey. Eng Geol 71(3–4):303–321
Tchobanoglous G, Kreith F (2002) Handbook of solid waste management. McGraw Hill, New York
- When the Input Features are polygons, the Clip Features must also be polygons.
- When the Input Features are lines, the Clip Features can be lines or polygons. When clipping line features with line features, only the coincident lines or line segments are written to the output, as shown in the graphic below.
- When the Input Features are points, the Clip Features can be points, lines, or polygons. When clipping point features with point features, only the coincident points are written to the output, as shown in the graphic below. When clipping point features with line features, only the points that are coincident with the line features are written to the output.
The Output Feature Class will contain all the attributes of the Input Features .
This tool will use a tiling process to handle very large datasets for better performance and scalability. For more details, see Geoprocessing with large datasets.
Line features clipped by polygon features:
Point features clipped by polygon features:
Line features clipped with line features:
Point features clipped with point features:
Attribute values from the input feature classes will be copied to the output feature class. However, if the input is a layer or layers created by the Make Feature Layer tool and a field's Use Ratio Policy is checked, then a ratio of the input attribute value is calculated for the output attribute value. When Use Ratio Policy is enabled, whenever a feature in an overlay operation is split, the attributes of the resulting features are a ratio of the attribute value of the input feature. The output value is based on the ratio in which the input feature geometry was divided. For example, If the input geometry was divided equally, each new feature's attribute value is assigned one-half of the value of the input feature's attribute value. Use Ratio Policy only applies to numeric field types.
Geoprocessing tools do not honor geodatabase feature class or table field split policies.
Using Feature Class name to populate new field - Geographic Information Systems
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Advanced Feature Extraction In Remote Sensing Using Artificial Intelligence And Geographic Information Systems
John E Estes, 1 Mark A Friedl, 1 Jeffrey L Star 1
1 University of California (United States)
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Traditional computer assisted image analysis techniques in remote sensing lag well behind human abilities in terms of both speed and accuracy. A fundamental limitation of computer assisted techniques is their inability to assimilate a variety of different data types leading to an interpretation in a manner similar to human image interpretation. Expert systems and computer vision techniques are proposed as a potential solution to these limitations. Some aspects of human expertise in image analysis may be codified into expert systems. Image understanding and symbolic reasoning provide a means of assimilating spatial information and spatial reasoning into the analysis procedure. Knowledge-based image analysis systems incorporate many of these concepts and have been implemented for some well defined problem domains. Geographic information systems represent an excellent environment for this type of analysis by providing both analytic tools and contextual information to the analysis procedure.
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Meta-analyses of the integrated platform
In the preceding sections, we briefly reported on users’ requests, and on potential features, to be implemented in the future MIRRI Information System. In this section we highlight some of the technological and graphical options for certain features of the user interface.
Harmonization of field content in database
If one needs to search various databases in a network, the search-field content has to be controlled both semantically and syntactically. During the first World Data Centre for Microorganisms (WDCMs) seminar held at Beijing, China, in May 2011, evidence for differences in the description of strain properties in collection data fields were demonstrated. For example, data on one strain of Aspergillus brasiliensis (Varga et al. 2007) were extracted from four catalogues with the following related strain numbers: ATCC 9642, CBS 246.65, DSM 63263 and VKM F-1119. A total of 24 data fields for the given strain from each collection were then paired and compared: only two fields in two collections were syntactically identical, i.e., aside from the same content, they also had the same format. There was in fact no significant differences in the reported strain properties in the four catalogues, aside from the syntax in the terms used to describe them. This example shows that a mere problem of field values could potentially render common searches of various databases difficult and incoherent. Hence, the need for harmonization of the type and description of data in a network of databases is evident.
According to the Taxonomic Databases Working Group (TDWG) roadmap and the experience of the Global Biodiversity Information Facility (GBIF) (WFCC 2010 Technical_Architecture_Group 2008), and BioMedBridges (BioMedBridges WP3: ESFRI BMS Standards Description and Harmonization, http://www.biomedbridges.eu/workpackages/wp3), the crucial components for the interoperability of databases are: “community supported vocabularies” “ontologies expressing shared semantics of data” “common exchange protocols”, and “persistent identifiers”.
“Vocabularies” refers to information data standards with detailed specifications of content in data fields (controlled vocabularies), in a specified vocabulary format. On the basis of these standards, identical information for a given concept can be inserted in all involved documents. In our case, in all catalogues of the CC partners. “Community supported” refers to the actual support in adopting and maintaining the tool, i.e., the vocabulary, over time. The starting point for the definition of a community-supported vocabulary may be the creation of a list of popular fields and related content in the catalogues of community members. To determine the most used fields, online catalogues of the WDCM/CCINFO collections were compared. In addition, MIRRI partners provided the fields they included in their catalogues. The subsequent elaboration of a list of those fields commonly used by CCs has paved the way to the establishment of a shared list, and to its adoption by the community. This list may be termed “Recommended Datasets” or “Practical Datasets” (PDS), to avoid confusion with previous CABRI definitions.
“Ontologies” allow for the semantics of both textual and factual information to be encoded and expressed, which is however often implicit and therefore unusable by a software tool. An ontology is a well-defined description of all concepts inherent in a given knowledge domain and of the relationships among them. The most informative ontologies include all instances of concepts, i.e., all values that can be validly associated with a concept. These instances can also be expressed by using vocabularies. In bioinformatics, ontologies have many applications, the most important being data validation and data integration. With respect to data validation, software can be developed to allow the checking of values assigned to information described in the ontology. A simple example is the automatic validation of species names on the basis of a special ontology for microbial names. This could be straightforward, e.g., comparing values listed in a catalogue with the list of valid names in a vocabulary. It could also be further articulated, e.g., when assessing the validity of single components of a scientific name [genus, species, approbation, author(s), year] per se, and in conjunction with the other components of the name.
Regarding data integration, the assignment of a given ontological concept to a piece of information, for example in a database, allows semantically correct connections between heterogeneous databases to be established. One possible example relates to the Gene Ontology (GO http://geneontology.org/), a widely adopted ontology of gene products. It is possible to “annotate” the description of a strain, i.e., to add GO terms that best fit its properties, to establish a potential connection with all databases that use GO. The shared adoption and use of ontologies is therefore an essential prerequisite for data validation and integration in modern Information Systems. Although some data, such as dates, do not strictly need an ontological description, it is important that all specific information have one.
For strain-associated data, special ontologies including all related concepts and their relationships are required, along with lists of instances (vocabularies) that take into account the variety of CC data for each piece of information. An ontology of fungal names, introduced in April 2013 for use in BioloMICS (https://www.bio-aware.com/), covers many online-catalogue data fields. However, updating strain information to a “new taxonomy”, i.e., to current names, is not straightforward. A study carried out by MIRRI partners on a catalogue list of strains belonging to a species demonstrated that it was rare that a name change applies to all strains of this species. For this reason, the least requirement would be a reference to the publication citing the new taxonomy before a name change could be considered. The changes could eventually be implemented, but only after further work, e.g., such as sequencing being carried out when a species is split on this basis.
In Environmental Ontology, community ontology for the concise, controlled description of environments (EnvO), types of soil were compared with soil classifications and almost all the recognized types were absent from the Metagenome and Microbes Environmental Ontology (MEO) (http://bioportal.bioontology.org/ontologies/MEO?p=classes&conceptid=root). In order to verify if this mal-adoption of existing ontologies in the representation of data in CC catalogues applies to other information, MIRRI partners were asked to provide lists of unique values for each field in their catalogues. These values could then be compared to the content of related domain ontologies. The obvious need for ontologies in database networking suggests that where no ontology is available, an appropriate one should be created. This would need to be a joint effort between microbiologists and IT specialists. To this end, a careful evaluation of existing ontologies in biological, agricultural, and biomedical research is needed. This is especially relevant given that the MIRRI Information System should be made interoperable with many other systems that are not strictly linked to microbiology, but that are nonetheless relevant for microbial resources, such as databases of sequences, proteins, enzymes, and chemical compounds. In this context, the Open Biological and Biomedical Ontologies Foundry (OBO http://obofoundry.org/), the National Center for Biomedical Ontology (NCBO http://www.bioontology.org/), and the associated BioPortal (http://bioportal.bioontology.org/), which is self-defined as “the world’s most comprehensive repository of biomedical ontologies”, are all of paramount importance. When searching through the BioPortal, various concepts and instances related to microbiology can be found. For instance, the concept of “strain” is present in 33 distinct ontologies. Three examples of the definitions referring to the microbiological concept of a strain are listed in Table 2.
CCs can clearly benefit from current definitions to improve their Information Systems for better interoperability and, moreover, the community of CC researchers can offer important and relevant contributions to other interested parties by providing a proper and extended ontology for microbiological concepts.
Navigation in the information space of microbiology, bioinformatics, biotechnology, agriculture, medicine
The main goal of the user interface of an Information System is, evidently, to provide the users with: (1) facilitated access to the available information about the strains of interest in CCs, and (2) convenient and efficient tools to browse through, and to extract and/or compare, the associated data. In addition, the system should provide a unique interface for the supply of strains and genetic material held either in one collection, or in a number of different collections. The objective would be to make research easier and more efficient via a unique access point, a “One-stop shop”.
To date, no structure has been created which fulfils these functions. However, there is one example of a web server, the Global Catalogue of Microorganisms (GCMs) of WDCM, from which a large number of details, such as strain name, strain number, and strains per referenced CC, can be accessed (Fig. 3). Although the GCM and its efficient search portal is an important accomplishment, a number of its features do not cater for the CC users’ needs. The data of each CC needs to be manually transferred to the GCM by the CCs themselves, resulting in a number of out-of-date catalogues. Although advanced, searches are not completely versatile requests combining more than two fields are not available in the GCM. Nevertheless, the GCM catalogue and its search tools remain the most thorough Information System for microbiological services. This example demonstrates that the key response would be to harmonize the fields in the database network as described above.
The “Advanced search” interface of WDCM with the request Isolation Source. The result of the request for Isolation Source for the entire content of the GCM (http://gcm.wfcc.info/strains.jsp) 02/02/2015
An overview of the main tasks involved in the construction of the MIRRI-IS with a temporal perspective is given in Fig. 4.
Schematic representation of the main tasks of the construction of the MIRRI-IS. For simplicity’s sake, the hypothesis of an inter-operable interface linking all individual CC databases was chosen (see Proposed solutions for increased interoperability between the existing databases section)
In Study 2, we explored the links between self-selection to a STEM field—Geographic Information Systems (GIS)—and improvement in navigation skills after extended exposure to domain knowledge from that field. GIS involves the use of an integrated toolbox of hardware and software systems and processes designed to allow an individual to store, retrieve, visualize and transform spatial data. Over the last three decades, GIS applications have extended beyond the field of geography and into various educational domains (Madsen & Rump, 2012) with the ultimate goal to enhance our ability to address planning and management problems (National Research Council, 2006). Not unlike the field of geology, GIS entails large-scale spatial reasoning and transformations, albeit through a different medium of learning. Where geology expertise often relies on fieldwork in the real world, GIS training focuses on a technology-assisted ability to store, visualize and manipulate digitized spatial information. So, does a suite of spatial visualization and analyses software at a figural scale demand high large-scale spatial thinking and does domain-specific knowledge in this GEO field translate into better spatial skills, specifically navigation skills?
Lee and Bednarz (2009) found that students enrolled in a GIS course outperformed a control group on a spatial test. In addition, GIS participants showed significant improvement in spatial thinking during the semester. However, the questions on the spatial test created to measure spatial thinking skill were closely related to the GIS course work and as such may not have been reflective of domain-general large-scale and small-scale spatial skills. Similarly, Hall-Wallace and McAuliffe (2002) found a significant positive correlation between small-scale spatial skills—measured by the surface development and cubes comparison tasks—and GIS learning. Although limited, there is a growing body of research investigating the relation between spatial thinking skills and GIS learning (e.g., Albert & Golledge, 1999 Baker & Bednarz, 2003 Britz & Webb, 2016 Kim & Bednarz, 2013). However, research so far has been limited to small-scale spatial thinking and to spatial tests closely related to the GIS curriculum.
In Study 2, we compared large-scale and small-scale spatial skills of novice GIS students with students enrolled in a nonspatial communications (COM) course at the start (T1) and end (T2) of an academic semester. As in Study 1, participants in Study 2 completed a virtual navigation paradigm in addition to mental rotation and spatial working memory tasks. Spatial and nonspatial skill at T1 was used as a baseline to examine improvement over the course of a semester. We hypothesized that: GIS students will have significantly better spatial skills at T1 as compared to COM students GIS students will show greater improvement in spatial skills, specifically in navigation skills, from T1 to T2 compared to COM students and mental rotation and spatial working memory may mediate the relation between academic course and spatial skills improvement.
Geographic Information Systems
Data collection is an integral part of Geographic Information Systems (GIS). The GIS Lab has a suite of field mapping equipment capable of collecting data at a wide range of accuracies. The equipment is available for loan within the USM Community. Contact the lab for specific details or to make borrowing arrangements.
Garmin GPS units (6): The Garmin GPS units are termed recreation-grade because they can collect points with an accuracy of about 5 meters. These are used for GPS demonstrations, geocaching, and data collection where high accuracy is not required. These units are available to anyone who has attended a brief training with the GIS Lab Manager.
GPS-enabled PDAs running field GIS software (6): These are the newest addition to the Lab's resources. They are a streamlined alternative to traditional data collection. They are used in the Digital Mapping Class as well as by guest lectures in non-GIS courses. These units are available to anyone who has attended a brief training with the GIS Lab Manager.
Trimble GeoXTs (6): The GeoXTs are termed mapping-grade because they can collect data with an accuracy better than 1 meter with post-processing. These units are often borrowed for field projects and available to anyone who has taken a GIS Course.
SpectraPrecision Total Stations (3): These high precision instruments can accurately measure locations to within centimeters. They are heavily used by the Digital Mapping Class. These units are available to anyone who has taken a GIS Course. In addition, a member of the Lab Staff will accompany the equipment into the field.
Real Time Kinematic (RTK) GPS (3): The RTK units are termed survey-grade because they can measure locations with an accuracy of centimeters with no postprocessing required. They too are used by the Digital Mapping Class. These units are available to anyone who has taken a GIS Course. In addition, a member of the Lab Staff will accompany the equipment into the field.