Data Acquisition Lecture 8

Data Sources  Data Transfer  Getting data from the internet and importing  Data Collection  One of the most expensive GIS activities  Many diverse sources  Raster based satellite data  Aerial pictures  Surveying  Data Transfer  Getting data from the internet and importing  Data Collection  One of the most expensive GIS activities  Many diverse sources  Raster based satellite data  Aerial pictures  Surveying

Data Collection  Data Capture (Direct Collection)  Primary (Direct Measurement)  Secondary (Indirect Measurement)  Data Capture (Direct Collection)  Primary (Direct Measurement)  Secondary (Indirect Measurement)

RasterVector 1o1o Digital remote sensing images Digital aerial photographs GPS Measurements Survey Measurements 2o2o Scanned Maps DEMs from Maps (Digital Elevation Models) Topographic Surveys Toponymy data sets from atlases Table Digitizing

1 o Raster  Capture specifically for GIS use  Remote sensing - Raster Data  E.g. - SPOT & IKONOS satellites and aerial photography  Passive & Active sensors  Platform  Airplane, satellite  Sensor  Device that records spatial data  Camera - B & W (Panchromatic)  Infrared Sensor - Hyperspectral imagery  Capture specifically for GIS use  Remote sensing - Raster Data  E.g. - SPOT & IKONOS satellites and aerial photography  Passive & Active sensors  Platform  Airplane, satellite  Sensor  Device that records spatial data  Camera - B & W (Panchromatic)  Infrared Sensor - Hyperspectral imagery

Resolution  Resolution of Raster data is a key consideration  Spatial  What size object can be resolved  What size is a pixel  Spectral  What part of the EM spectrum is being measured  Temporal  How often is the data being updated  Resolution of Raster data is a key consideration  Spatial  What size object can be resolved  What size is a pixel  Spectral  What part of the EM spectrum is being measured  Temporal  How often is the data being updated

Spatial-Temporal Characteristics of Remote Sensing Systems "Spatial and temporal characteristics of commonly used remote sensing systems and their sensors" From: Geographic Information Systems and Science, 2nd ed. Paul Longley, Michael Goodchild, David Maguire, and David Rhind. Originally From: Jenson, J.R. and Cowen, D.C 'Remote Sensing of urban/suburban infrastructure and socioeconomic attributes' PERS, 65,

1 o Vector  Surveying  Location of objects determined by angle and distance measurements from known locations  Uses expensive field equipment and crews  Most accurate method for large scale, small areas  GPS  Collection of satellites used to fix locations on earth’s surface  Increased accessibility  Surveying  Location of objects determined by angle and distance measurements from known locations  Uses expensive field equipment and crews  Most accurate method for large scale, small areas  GPS  Collection of satellites used to fix locations on earth’s surface  Increased accessibility

2 o Geographic Data Capture  Data collected for other purposes can be converted for use in GIS  Raster conversions  Scanning of maps, aerial photographs, documents, etc  Important scanning parameters are spatial and spectral resolution  Digitizing, Batch Vectorization  Common human errors in digitizing  Overshoot, undershoot, dangling segment, sliver polygon  Automated remedies to digitizing errors are not perfect  Data collected for other purposes can be converted for use in GIS  Raster conversions  Scanning of maps, aerial photographs, documents, etc  Important scanning parameters are spatial and spectral resolution  Digitizing, Batch Vectorization  Common human errors in digitizing  Overshoot, undershoot, dangling segment, sliver polygon  Automated remedies to digitizing errors are not perfect

Quality Standards  Metadata  Information about the data  Who Collected the data  How was it collected  What datum was used  What projection was used  What is the precision of the data  What type of data is it  When was the data last updated  There are standards established for different types of data and the metadata.  Metadata should be available for the data to be reliable.  Metadata should be available either for each data file, or a general metadata file will be available for a group of files.  Metadata  Information about the data  Who Collected the data  How was it collected  What datum was used  What projection was used  What is the precision of the data  What type of data is it  When was the data last updated  There are standards established for different types of data and the metadata.  Metadata should be available for the data to be reliable.  Metadata should be available either for each data file, or a general metadata file will be available for a group of files.

Accuracy  The degree to which information on a map or a digital database matches true or accepted values.  Accuracy is an issue pertaining to the quality of data and the number of errors contained in a dataset or map.  The level of accuracy required for particular applications varies greatly.  Highly accurate data can be very difficult and costly to produce and compile.  The degree to which information on a map or a digital database matches true or accepted values.  Accuracy is an issue pertaining to the quality of data and the number of errors contained in a dataset or map.  The level of accuracy required for particular applications varies greatly.  Highly accurate data can be very difficult and costly to produce and compile.

Precision  The level of measurement and exactness of description in a GIS database.  Precise locational data may measure position to a fraction of a unit.  It is important to realize that precise data - no matter how carefully measured - may be inaccurate.  Surveyors may make mistakes or data may be entered into the database incorrectly.  The level of measurement and exactness of description in a GIS database.  Precise locational data may measure position to a fraction of a unit.  It is important to realize that precise data - no matter how carefully measured - may be inaccurate.  Surveyors may make mistakes or data may be entered into the database incorrectly.

 The level of precision required for particular applications varies greatly.  Engineering projects such as road and utility construction require very precise information measured to the millimeter.  Demographic analyses of marketing or electoral trends can often make due with less, say to the closest zip code or precinct boundary.  Highly precise data can be very difficult and costly to collect.  The level of precision required for particular applications varies greatly.  Engineering projects such as road and utility construction require very precise information measured to the millimeter.  Demographic analyses of marketing or electoral trends can often make due with less, say to the closest zip code or precinct boundary.  Highly precise data can be very difficult and costly to collect.

Error in data  Positional Accuracy and Precision  Horizontal and Vertical  Scale  Attribute Accuracy and Precision  Non-spatial information linked to a location  E.g. Age, gender, income of a person at a particular address  Positional Accuracy and Precision  Horizontal and Vertical  Scale  Attribute Accuracy and Precision  Non-spatial information linked to a location  E.g. Age, gender, income of a person at a particular address

 Conceptual Accuracy and Precision  Abstraction and classification of real-world phenomena.  The user determines what amount of information is used and how it is classified into appropriate categories.  Rivers, streams, tributaries  Electrical utilities infrastructure  Voltage, line size, line type  Logical Accuracy and Precision  Information in a database can be employed illogically.  Information in a GIS database must be used and compared carefully if it is to yield useful results.  Conceptual Accuracy and Precision  Abstraction and classification of real-world phenomena.  The user determines what amount of information is used and how it is classified into appropriate categories.  Rivers, streams, tributaries  Electrical utilities infrastructure  Voltage, line size, line type  Logical Accuracy and Precision  Information in a database can be employed illogically.  Information in a GIS database must be used and compared carefully if it is to yield useful results.

Canned Data vs External Data  Canned Data  data that has been pre-screened and tested to work in a particular application.  For example: the data sets in Mapping Our World and Getting to Know ArcGIS.  External Data  Data in the form that is made available by the supplier.  For example: the data sets from SANDAG or SanGIS.  The data may not be in the same datum, projection etc.  The data may need to be manipulated before it can be projected correctly.  Canned Data  data that has been pre-screened and tested to work in a particular application.  For example: the data sets in Mapping Our World and Getting to Know ArcGIS.  External Data  Data in the form that is made available by the supplier.  For example: the data sets from SANDAG or SanGIS.  The data may not be in the same datum, projection etc.  The data may need to be manipulated before it can be projected correctly.

Downloading from Web Sources  Use reliable sites  Government or Education Sites  Reliability of data  Check the metadata for:  Source of the data  Accuracy and Precision of the data  Date of data collection and revision  Format of the data  Check the metadata for:  Datum  Projection  Use reliable sites  Government or Education Sites  Reliability of data  Check the metadata for:  Source of the data  Accuracy and Precision of the data  Date of data collection and revision  Format of the data  Check the metadata for:  Datum  Projection