UNIT 3 MODULE 5 Data Input Editing INTRODUCTION

UNIT 3 – MODULE 5: Data Input & Editing

INTRODUCTION • Putting data into a computer (called data coding) is a fundamental process for virtually all GIS projects. • Recall that spatial data: – Is acquired from various sources. – Has different formats. – Can be captured (inputted) with several methods. • Once data is contained within a GIS, nearly all data must be corrected/manipulated to fit the model chosen for data access.

ANALOG VS DIGITAL DATA • Analog – non-electronic data. Must be converted digitally prior to using in a GIS. • Examples: printed aerial photographs, paper maps. • Digital – data in a format that computers can use. • Examples: satellite imagery, GPS data. Credit: NASA

DATA INPUT METHODS • There are several ways to turn analog data into digital data: – Keyboard Entry – Entering data (into a file) by way of a computer terminal. • Example: entering names from a sign-in sheet. – Manual Digitizing – Requires a digitizing table connected to a computer station. • Example: digitizing mountain tops from an aerial photo. – Automatic Digitizing – Scanning is the most common. • Example: scanning an old map or photo into digital format.

SCANNING • When scanning analog data, several problems can arise: – Optical Distortions – Unwanted Data/Info (e. g. Coffee Stains, Hand-Written Annotations) – Non-Uniform Scanning Between Front & Background Data – File Format Non-Compatible with GIS – Time Required for Producing Compatible Data for Analysis Credit: Epson Credit: www. gifmania. us

ELECTRONIC DATA TRANSFER • Data input methods can create difficulties, which takes more time to address. • It is ideal to use data that is already in a digital format. Then it’s a question of format type. • If a format type is already compatible with a GIS, then it’s simply a matter of transferring data, but that will not likely be the case. • Data transfer often followed by converting data into a usable GIS format type.

DATA EDITING • Problems can arise during data encoding. • Data is seldom error-free upon transfer into a GIS. • Errors could exist from the source data, generated during data transfer or during the data encoding process. • Better to detect & correct errors during this process than integrating into a GIS and then addressing errors.

DETECTING & CORRECTING ERRORS – Attribute Data • Attribute data errors are simple to identify. • Methods available for checking errors: – Impossible Values – viable when range of data is known. Example: negative rainfall measurements. – Extreme Values – cross-check against source data. Example: a hotel with 50, 000 rooms is clearly wrong. – Internal Consistency – GIS attribute data totals should match source document data. – Scattergrams – a type of graphing that can identify errors if two or more attribute data variables are linked. Example: altitude & temperature.

DETECTING & CORRECTING ERRORS – Spatial Data • Spatial data errors more difficult to identify & correct. • Common spatial data errors include: – Missing Entities – points, lines or boundary segments (PLBS) are missing. – Duplicate Entities – PLBS digitized twice or more times. – Mislocated Entities – PLBS digitized in the wrong place. – Missing Labels – polygons are unidentified. – Duplicate Labels – a polygon has more than one identification label.

GEOCODING • Converting an address into a coordinate for a map. • Addresses are an important part of various data sets. • Most GIS software products are able to geocode addresses. • Geocoding is impacted by data quality. • Inconsistencies can exist in address data, such as using “Street” instead of “Avenue. ”