Search USGS⌕ Search
Home/Archive/USGS

USGS · 70192717

Crowdsourcing The National Map

Elizabeth McCartney·Kari J. Craun·Erin M. Korris·David A. Brostuen·Laurence R. Moore
View original source ↗Export metadata ↓

Abstract

Using crowdsourcing techniques, the US Geological Survey’s (USGS) Volunteered Geographic Information (VGI) project known as “The National Map Corps (TNMCorps)” encourages citizen scientists to collect and edit data about man-made structures in an effort to provide accurate and authoritative map data for the USGS National Geospatial Program’s web-based The National Map. VGI is not new to the USGS, but past efforts have been hampered by available technologies. Building on lessons learned, TNMCorps volunteers are successfully editing 10 different structure types in all 50 states as well as Puerto Rico and the US Virgin Islands.

Explore related subjects

Cartography and Geographic Information ScienceArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Elizabeth McCartney, Kari J. Craun, Erin M. Korris, David A. Brostuen, Laurence R. Moore. 2015-08-20. Crowdsourcing The National Map. https://doi.org/10.1080/15230406.2015.1059187

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2018

Validating the use of object-based image analysis to map commonly-recognized landform features in the United States

The U.S. Geological Survey (USGS) National Geospatial Program (NGP) seeks to i) create semantically-accessible terrain features from the pixel-based 3D Elevation Program (3DEP) data, and ii) enhance the usability of the USGS Geographic Names Information System (GNIS) by associating boundaries with GNIS features whose spatial representation is currently limited to 2D point locations. Geographic object-based image analysis (GEOBIA) was determined to be a promising method to approach both goals. An existing GEOBIA workflow was modified and the resulting segmented objects and terrain categories tested for a strategically chosen physiographic province in the mid-western US, the Ozark Plateaus. The chi-squared test of independence confirmed that there is significant overall spatial association between terrain categories of the GEOBIA and GNIS feature classes. Contingency table analysis also suggests strong category-specific associations between select GNIS and GEOBIA classes. However, 3D visual analysis revealed that GEOBIA objects resembled segmented regions more than they did individual landform objects, with their boundaries often failing to correspond to match what people would likely perceive as landforms. Still, objects derived through GEOBIA can provide initial baseline landscape divisions that can improve the efficiency of more specialized feature extraction methods.

Cartography and Geographic Information Science↗
USGS2017

An open source high-performance solution to extract surface water drainage networks from diverse terrain conditions

This paper describes a workflow for automating the extraction of elevation-derived stream lines using open source tools with parallel computing support and testing the effectiveness of procedures in various terrain conditions within the conterminous United States. Drainage networks are extracted from the US Geological Survey 1/3 arc-second 3D Elevation Program elevation data having a nominal cell size of 10 m. This research demonstrates the utility of open source tools with parallel computing support for extracting connected drainage network patterns and handling depressions in 30 subbasins distributed across humid, dry, and transitional climate regions and in terrain conditions exhibiting a range of slopes. Special attention is given to low-slope terrain, where network connectivity is preserved by generating synthetic stream channels through lake and waterbody polygons. Conflation analysis compares the extracted streams with a 1:24,000-scale National Hydrography Dataset flowline network and shows that similarities are greatest for second- and higher-order tributaries.

Cartography and Geographic Information Science↗
USGS2016

Shapes on a plane: Evaluating the impact of projection distortion on spatial binning

One method for working with large, dense sets of spatial point data is to aggregate the measure of the data into polygonal containers, such as political boundaries, or into regular spatial bins such as triangles, squares, or hexagons. When mapping these aggregations, the map projection must inevitably distort relationships. This distortion can impact the reader’s ability to compare count and density measures across the map. Spatial binning, particularly via hexagons, is becoming a popular technique for displaying aggregate measures of point data sets. Increasingly, we see questionable use of the technique without attendant discussion of its hazards. In this work, we discuss when and why spatial binning works and how mapmakers can better understand the limitations caused by distortion from projecting to the plane. We introduce equations for evaluating distortion’s impact on one common projection (Web Mercator) and discuss how the methods used generalize to other projections. While we focus on hexagonal binning, these same considerations affect spatial bins of any shape, and more generally, any analysis of geographic data performed in planar space.

Cartography and Geographic Information Science↗