Data Collection

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Data Collection
GEOG/ENVS 3/414 /PADM 435
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Outline

• Introduction
• Primary Geographic Data capture
• Secondary Geographic Data Capture
• Data Transfer from External Sources
• Capturing Attribute Data
• Managing a Data Acquisition Project

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1. Introduction

• There are three ways to capture data
• From Primary Sources or Direct Measurement
• From Secondary Sources or from previous studies
  or existing sources of information
• Data transfer from external source
• You may digitize a map or improve existing data
  as a part of the planning process in GIS data
  capture
• Data Capture can cost up to 85
• Data maintenance is also an important aspect
• Data dictionaries provide documentation about
  data
• Resolution is an important descriptor of data
• Two aspects of data quality must be considered in
  data capture
• Truth and labeling and
• Fitness for use

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2. Primary Geographic Data Capture

• Vector Data Capture
• GPS used network of satellites. At least four are
  available at any time on the earth. Based on the
  travel time of signal from the satellite to a
  ground receiver station. Accurate to about 5-10
  m. Using differential GPS system, you can keep
  one receiver fixed and other for measurements (
  Rad box 10.1)
• Survey measurements using Total Station

• Raster Data Capture
• Remote sensing E.g.. Thematic Mapper is based on
  spectral signature of electromagnetic
  reflectance.
• Arial Photos
• Resolution is a descriptor of data
• Spatial
• Spectral
• Temporal
• Radiometric

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3. Secondary Data Capture

• Raster Data capture using Scanning
• Resolution of scanner is important
• consideration 100 dots per inch ( 4 dots /mm) (
  bi-level black and white and multiple gray level
  8/16/32 bits
• Can you think about reasons for scanning
• Documents/ plans may be available in Maps,
• Arial photos and maps are scanned and
  geo-referenced to provide reference or context to
  geographic analysis
• aerial photos and images are scanned prior to
  vectorization

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3. Secondary Data Capture (contd)

• Vector Data Capture
• Digitize vector objects from a map or geographic
  data source
• Manual digitizing using existing map is simple,
  cheap and easy ( Read Box on digitizing in the
  text)
• Vectorization is converting from raster to vector
• Rasterization is converting from vector to
  raster.
• Photogramettry is used to make measurements from
  aerial photos and other image sources
• COGO (coordinate geometry) is a vector method of
  data capture. Bearings and distances are
  converted into X and Y coordinates ( see
  http//www.purplemath.com/modules/distform.htm)

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4. Data Transfer from External Sources

• The best approach is to search the Internet
• US NDSI clearing house http//www.fgdc.gov
• Geographic network is another good source of
  geographic information visit http//www.Geographi
  cNetwork.com
• See http//www.esri.com
• http//www.geocomm.com/
• See also free data link http//faculty.roosevelt.e
  du/achet/dear.htm
• See also http//www.nationalgeograpic.com
• For GEOWorld Data Directory visit
  http//www.geoplace.com
• For data Depot see http//www.gisdatadepot.com
• Visit EPA for environmental data

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4.Data Transfer from External Sources(contd)

• Transfer takes place by direct read or via
  intermediate file format.
• There are over 2 dozen institutions involved in
  the standardization of geographic data and
  processing.
• Once you find data, you import it
• Be ware of the most common data formats in Raster
  and Vector formats
• Familiarize yourself with the popular geographic
  data formats ( see Table in the textbook)

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5 Capturing Attribute Data

• Collected separately or together
• Simple using data logger or manual keyboard
  entry,
• Attribute data is generally entered in a tabular
  format where the key ( common identifier column)
  can be used to relate spatial and non-spatial
  attributes
• Metadata are a special kind of non-geometric data

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6. Managing a Data Acquisition Project

• One needs to build it over a period of time
• Prepare data dictionaries
• There is a relationship between quality, speed
  and price.
• Can use in house or external resources
• Think about data quality issues fitness for use
  and truth and labeling.