22 research outputs found

    Shape Types for Labeling Natural Polygon Features with Maplex

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    The article presents information on a methodology used for describing natural polygons, which enables feature label placement rules to be derived for Maplex. Maplex is a cartographic label placement extension for ArcGIS, the complete Geographic Information Systems (GIS). In cartographic label placement one need to find the shape of the polygon and how large the polygon is within the context of the space required to place text. The methodology being is the polygon\u27s minimum bounding rectangle (MBR). MBRs have also been used for automated text placement algorithms. This method could prove to be very effective in labeling soil type, vegetation type, and surface geology type

    A Multi-scale, Multipurpose GIS Data Model to Add Named Features of the Natural Landscape to Maps

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    There is a certain class of features on maps that are difficult to generate from traditional GIS databases — named features of the natural landscape. Physical features, such as mountain ranges, canyons, ridges and valleys, and named water bodies, such as capes, bays and coves, are often not found in GIS databases. This results in their omission on maps or at best their addition to the map as graphic type that is not georeferenced to the data used to make the map. This paper describes an inherently multi-scale GIS data model for physiographic features, and by extension named water bodies and named islands and island chains and groups, that can be used to create many different types of maps. The semantic model (what features to include), the representation (how to define the geometry of the features and their attributes), and the symbology (the specifications for both type properties and type placement) are discussed. In addition, the sensitivity of the representations and symbology to the software used for mapping are described. These issues are reviewed in hopes that others will be better able to use GIS data and software to make maps that include these features. Cartographers know that without the inclusion of the type for these names on maps, the products created are less informationally — and cartographically— rich. If more GIS databases with these features in them were developed, non-cartographers using GIS software to make their maps, as well as cartographers who have not generally had these data at hand, could produce better products

    Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement

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    Accurate, up-to-date maps of and georeferenced data about human population distribution are essential for meeting the United Nations Sustainable Development Goals progress measures, for supporting real-time crisis mapping and response efforts, and for performing many demographic and economic analyses. In December 2014, Esri published the initial version of the World Population Estimate (WPE) image service to ArcGIS Online. The service represents a dasymetric footprint of human settlement at 250-meter resolution. It is global and contains an estimate of the 2013 population for each populated cell. In 2016 Esri published an additional image service representing the earth’s population in 2015 at 162-meter resolution. Esri’s WPE is produced by combining classified land cover data indicating predominantly built-up or agricultural locations with Landsat8 Panchromatic imagery, road intersections, and known populated places. The model detects where settlement is likely to exist beyond the areas classified as predominantly built up. The result is a global dasymetric raster surface of the footprint of settlement with a score of the likelihood of human settlement for each cell of the footprint. Population data are apportioned to this settlement likelihood surface by overlaying population counts in polygons representing census enumeration units or political units representing population surveys. This paper presents the method developed at Esri for producing the estimate of settlement likelihood

    A Global Ecological Classification of Coastal Segment Units to Complement Marine Biodiversity Observation Network Assessments

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    A new data layer provides Coastal and Marine Ecological Classification Standard (CMECS) labels for global coastal segments at 1 km or shorter resolution. These characteristics are summarized for six US Marine Biodiversity Observation Network (MBON) sites and one MBON Pole to Pole of the Americas site in Argentina. The global coastlines CMECS classifications were produced from a partitioning of a 30 m Landsat-derived shoreline vector that was segmented into 4 million 1 km or shorter segments. Each segment was attributed with values from 10 variables that represent the ecological settings in which the coastline occurs, including properties of the adjacent water, adjacent land, and coastline itself. The 4 million segments were classified into 81,000 coastal segment units (CSUs) as unique combinations of variable classes. We summarize the process to develop the CSUs and derive summary descriptions for the seven MBON case study sites. We discuss the intended application of the new CSU data for research and management in coastal areas

    When is it Too Late to Find a Cartographer?

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    A Multi-scale, Multipurpose GIS Data Model to Add Named Features of the Natural Landscape to Maps

    Get PDF
    There is a certain class of features on maps that are difficult to generate from traditional GIS databases — named features of the natural landscape. Physical features, such as mountain ranges, canyons, ridges and valleys, and named water bodies, such as capes, bays and coves, are often not found in GIS databases. This results in their omission on maps or at best their addition to the map as graphic type that is not georeferenced to the data used to make the map. This paper describes an inherently multi-scale GIS data model for physiographic features, and by extension named water bodies and named islands and island chains and groups, that can be used to create many different types of maps. The semantic model (what features to include), the representation (how to define the geometry of the features and their attributes), and the symbology (the specifications for both type properties and type placement) are discussed. In addition, the sensitivity of the representations and symbology to the software used for mapping are described. These issues are reviewed in hopes that others will be better able to use GIS data and software to make maps that include these features. Cartographers know that without the inclusion of the type for these names on maps, the products created are less informationally — and cartographically— rich. If more GIS databases with these features in them were developed, non-cartographers using GIS software to make their maps, as well as cartographers who have not generally had these data at hand, could produce better products
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