4.21 Spatial Data Preparation
4.21.01 An Example of Examining Map Accuracy
GIS technology has broadened our view of maps. Instead of being a static entity, a map is now a dynamic presentation of geographic data. The advantages are outstanding but there are also risks involved. In this case study, the importance of observing positional accuracy between the input data and the end product in form of a CLUP map is shown.
Sources of Data
In the preparation of the CLUP using GI Technology, secondary source data will be used when available. Sources and samples of these data are discussed in Chapter 4.17.01. The LGU planner makes use of data captured by a national agency (e.g. geologic map, soil map, erosion map, flooding map, etc.). More often than not, these data will be in a paper format though there could be some where digital files are available in JPEG (scanned or imported image of the map) or vector file. Some may have already been produced using modern methods (GPS, Aerial Photography, Satellite Imaging, Digital Processing, etc.), but majority of these have been produced manually. Scales vary and little is known about the accuracy when they were produced (little metadata is attached). Furthermore, the process of production, reproduction and use of these maps will also produce distortions or errors (e.g. crumpling, stretching, uneven surface or rotation during tracing or production). It is important that during request and acquisition, researchers should take the time to ask about the data. This will be critical in assessing the accuracy and limitations of the data being acquired. In Chapter 5, Metadata specifications are given on some of the data but a lot more needs to be done to assist the planner properly.
In order to be of use in a GIS, the source map must be transformed to a digital layer. In technical terms, the paper maps will need to be digitized. Scanning and georeferencing, which is discussed in Chapters 4.21.04 and 4.21.05, is the first step in digitizing where acceptance and accuracy should be observed with respect to those processes. The presence of errors within the source will be transferred into the digital form plus any errors that might have been incurred during reproduction of the source data, scanning and georeferencing process. The accuracy of the digital data will be limited to the accuracy of the secondary source and comparison will only be between secondary data sources. How to treat errors between primary and secondary sources will be taken up in a different chapter.
The accuracy of digitization is dependent on the accuracy of the source. The georeferencing operation and output digital file will never be more accurate than the source. We can only make these source data. The use of a more accurate source will be superseded when dealing with administrative boundaries that will be discussed later on. If the accuracy of a secondary source cannot be determined, it should be compared to other secondary sources that have comparable features.
Care must also be taken when comparing data. Most secondary sources were done manually, and could have a lot of errors. It is possible that there are secondary sources that have been produced digitally like orthophotos and GPS surveys. These sources would have greater accuracy than all other secondary sources and the manner in which this data is treated, compared to manually made data, should be considered differently.
First step is to compare secondary datasets, which were manually prepared.
Use a 1:50,000 topographic map published by NAMRIA and digitize a portion of a road (yellow line) in the map retaining the thickness.
When the digitized road (yellow line) is overlaid on a Soil Map published by the Bureau of Soils and Wastewater Management (BSWM) with a scale of 1:100000, it will be apparent that there is a big discrepancy in size and direction of the same road on the other map (thick blue line).
Compare the scales. A 1:50000 map would be twice more accurate than a 1:100000 map. Consider plotting in these two scales using 0.5 mm technical pen. A 0.5 mm thick line on a 1:50000 map would have an equivalent ground distance of 25 m and on a 1:100000 map, an equivalent ground distance of 50 m. A 10-meter main road will be more accurately plotted on a larger scale map. Data taken from a larger scale map should be treated more accurately. In the case shown above, the road on the NAMRIA map would be 30 meters wide while the Soil Map displayed the road as 100 meters wide. Of course the NAMRIA 1:50000 topomap will have to be used for the road data if no other up to date source is available.
Now take a look at another aspect to consider when assessing accuracy.
In some cases, there is metadata on the map. Look at the dates on the map, both source preparation and production. Unless otherwise known, newer data should have more weight in accuracy than old data. Newer data means that new methods were used, more accurate survey equipment, better plotters and printers and more accurate computations. In this example, it says “map series 1:50,000 compiled in 1955” so it should be treated as more accurate for map data created prior to that year and less accurate for map data after that year. Even if the map was produced recently, the source data will still be the old.
This map is a slope map from BSWM. The base map used is a NAMRIA 1:50000 topomap with the Metadata used above. A portion of a road was digitized on the map.
Overlaying the road with the NAMRIA 1:50000 Topomap and zooming in on the area shows that the road will still be out of place. In this example, it is off approximately 65 meters in ground units.
This example shows maps that have the same source but with different outcomes. One can never be more accurate than the source (NAMRIA 1:50000 topomap) so adjustment for the road data will have to be made in conformance with the NAMRIA Map.
(example of areal photo will be added if photo is already available)
Lessons Learned
In searching for data, there will always be discrepancies found. It is only now that these discrepancies become apparent through the use of GIS. It should not discourage the user because as has been shown, there is not one good single source for data.
4.21.02 Design Symbols with Information Value
Introduction
We envision information in order to reason about, communicate, document, and preserve that knowledge – activities nearly carried out on two-dimensional paper or computer screen. Escaping this ‘flatland’ and enriching the density of data displays are the essential tasks of information design – a progress of methods for enhancing density, complexity, dimensionality, and even sometimes beauty. A systematic approach to cartographic design is one of the tools and may prove to be very useful in the process of producing maps.
In GIS, lot of efforts goes to prepare data and make analysis but when it comes to map-making and design of the outcome not so much is done. But data and analyses do not speak for themselves, the result of your work must be designed so it is easy to read and comprehend.
The software provides you with a million opportunities of symbols and colors. However when you try it out you soon realize that you still are the one who will decide how it should look like.
Normally, graphic symbols can be more or less distinctive by manipulating with the following:
The Visual Variables
- Position
- Form
- Orientation
- Color
- Texture
- Value
- Size
- Checklist for Symbols
The Visual Variables
Seven types of variations are perceivable to the human eye, which graphically present information. These are called visual variables and are used for the construction of symbols. Through variations in the application of visual variables, symbols can become distinctive taking in consideration the three major categories of map symbols namely; point, line, and polygon (area) symbols. These visual variables are: position, form, orientation, color, texture, value, and size.
Position
Refers to X and Y location of the information, which determines its place on the map. This visual variable is always used in combination with one or more of the other visual variables.
Form
Refers to the form of individual elements with which the symbols are constructed.
Orientation
The direction in which symbols are placed. The attached figures show the use of this visual variable in the application to point, line, and polygon (area) symbols respectively. For point symbols, depending on the type of symbol used (e.g. square), variation in orientation is limited.
Color
This visual variable is the most powerful and most frequently used. Color can be described according to its three variable characteristics: hue, value and saturation.
Hue is the wavelength of a particular color such as red, orange, yellow, green, brown, blue, violet, etc.
Value is the amount of light reflected by a color; this reflectance value can be compared with the values of a grey scale.
Saturation is the purity or intensity of a color starting from a pure hue; the saturation is changed by adding neutral grey to it.
Texture
The variation in density of the graphic elements under constant value, i.e. with the same overall grey impression. Texture variation is applied to point, line and polygon (area) symbols.
Value
Refers to the values on a grey scale, ranging from the values white to black.
This gives an overall grey impression by using different shades or tones of grey ink or paint. Similar effects can be obtained by using line or dot screens. Attached figures illustrate the application of value to point line
Size
Refers to the dimensions of the individual elements with which the symbol is built up. Figures attached illustrate the application of the visual variable size of point, line and polygon (area) symbols.
To appreciate the difference between the size and value, if the dimensions are small such that the first impression is that of grey tone variation, the visual variable value is used. Only when the dimension is large and the eye will catch spontaneously and instantaneously the variations in the individual element sizes, will there be a proper application of the visual variable size.
Checklist for Symbols
For easy understanding of the (geo)graphic information such as a thematic map, it is important that to have a well thought-out strategy. The producer must know that the thematic map might be used together with other layers
4.21.03 Recommended Palettes to use for CLUP Spatial Feature Object (First Draft Version)
This write-up will serve as a guide on how to use the symbology and colors found in information products and tutorials.
The document is organized into the following subchapters:
- Recommended colors for the CLUP Map
- Recommended colors for some information products
- Recommended symbology for point features
A Color will be presented with its RGB-code.
1. Recommended colors for the CLUP map
In Annex 4-3 of Volume 1 the following colors are recommended. For the sake of consistency between different CLUP maps, these recommendations should be obeyed. The table below differs from the Annex 4-3 table in that the paths to ArcGIS standard symbols are included.
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LAND USE CATEGORIES |
COLOR CODING, FILL SYMBOL in ArcGIS |
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Urban Use Areas |
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| Residential |
Yellow |
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Commercial |
Red (super |
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Gray, |
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Blue (293-A), |
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Light Green, |
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Industrial |
Violet |
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Agriculture |
Green |
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Forest and forest use categories |
Dark Green |
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Mining/quarrying |
Brown |
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Grassland/pasture |
Olive Green |
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Agro-industrial |
Light Violet |
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Tourism |
Orange, |
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Other uses/categories |
Appropriate color other than the above |
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Water Uses |
Light blue |
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Esri styles/Fill symbols/swamps |
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Esri styles/Fill symbols/mangrove |
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Settlements on stilts |
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2. Recommended Colors for some Information Products
Colors Separating Different Categories
The objectives of some of the Information Products are only to present an inventory of different land categories. The recommended colors in these IPs conform in the most cases to the land use categories defined above for the CLUP map. However, note that in other cases colors are recommended only for a clear map presentation within the specific information product.
It is recommended that areas in an IP that are defined as not suitable are assigned hatching patterns.
Qualitative Scales.
Several Information Products in the Socio-Economic sector deal with quality indicators in a scale often ranging fair-poor-critical. These will be used in the needs analyses. A simple, intuitive and coherent color coding through different maps is:
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Fair |
Green, |
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Poor |
Yellow, |
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Critical |
Red, RGB-255,0,0 |
When different indicators are overlaid with each other, it is better to concentrate only on the poor and/or critical areas. Different types of hatching will refer to different indicators, thus from the map the following can be interpreted (see example below). Also refer to the respective Information Product.
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Indicator A, poor, not critical |
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Indicator A, critical |
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Indicator B, poor to critical |
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Indicator C, poor to critical |
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Both Indicator B and C, poor to critical |
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Quantitative Scales
Examples of indicators that are quantitatively compared to one another are population size and density per barangay, revenue per capita per barangay and crop yield per cultivated area. ArcGIS assign automatically a color scale grouping the value of the indicator in automatically rendered groups. It is recommended to use scales where darker colors refer to higher values and lighter colors to lower values. Also, the groups can be manually edited, which is recommended, especially if defined revenue categories exist.
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3. Recommended Symbology for Point Features
The table below presents symbols used in different IPs (ascending by IP number). The location of the symbol is given.
Tip! The symbols in the document (when HTML!) are bitmap-files. You can right-click on each symbol image and store in the ArcGIS symbology folder (normally it is found here: C:\Program Files\ArcGIS\Bin\Styles\). You can then create a new style file and add the symbols as marker picture symbols. Refer to ArcGIS help section for this.
| 4.05.03 Administration | ||
| City/Municipal Hall (is also captured as it can house a Barangay Office as well) |  | |
| Barangay Hall/Office |  | |
| 4.06.02 Health | ||
| Hospital | ||
| City/municipal Health Office | ||
| Barangay Health Center | ||
| 4.06.03 Education | ||
| Pre-School |  | Civic.style > Marker Symbols > School 2 |
| Primary |  | Civic.style > Marker Symbols > School 1 |
| Secondary |  | Civic.style > Marker Symbols > School 1 (larger) |
| Tertiary | Civic.style > Marker Symbols > School 2 (Modified. Explain how!) | |
| 4.06.04 Protection | ||
| Symbols for different crimes shown in screenshot are found in Civic.style > Marker Symbols > | ||
| 4.06.05 Religion | ||
| Church |  | Civic.style > Marker Symbols > church |
| Mosque |  | Civic.style > Marker Symbols > Place of Whorship - Moslim |
| Hindu Temple |  | Civic.style > Marker Symbols > Place of Whorship - Hindu |
| Synagogue |  | Civic.style > Marker Symbols > Place of Whorship - Jewish |
| Pagoda | ||
| Other | ||
| 4.06.06 Recreation | ||
| Basketball Court | ||
| 4.06.07 Social Welfare | ||
| Day Care Centers | ||
| 4.06.08 Commerce | ||
| Financial Intermediation |  | Business style > Marker Symbols > Bank 3 |
| Health and Social Work |  | |
| Hotels and Restaurants |  | Business style > Marker Symbols > Dining 2 |
| Other Community, Social and Personal Service Activity |  | |
| Real Estate, Renting and Business Activities |  | Business style > Marker Symbols > House 1 ??? |
| Wholesale and Retail Trade, Repair of Motor Vehicles, Motorcycle and Personal and Household Goods |  | Business style > Marker Symbols > Shopping 2 |
| 4.06.09 Industry | ||
| These symbols are created based on….. They don’t exist in style files. However, there are other industry symbols in for example…. | ||
| Light Industry |  | |
| Medium Industry |  | |
| Heavy Industry |  | |
| 4.06.10 Tourism | ||
| The object will be tourism accommodations, a point feature |  | Civic.style > Marker Symbols > Lodging |
| Restaurants |  | Civic.style > Marker Symbols > Dining 1 |
| Fastfood |  | Civic.style > Marker Symbols > Dining 2 |
| Transport Terminal |  | Civic.style > Marker Symbols > Bus 1 ? |
| Mall |  | Business style > Marker Symbols > Shopping 1 |
| Parks |  | |
| Groundwater Source |  | |
| 4.06.11 Agriculture | ||
| Refer to IP for exact symbols and their sizes. The support facility point is blue, the project square green and irrigation systems are whole lines | ||
| 4.07.02 Water | ||
| Water source (point) |  | |
| 4.07.04 Telecom | ||
| Large Tower |  | |
| Post office |  | |
| 4.09.06 Cultural Heritage | ||
| Symbols created based on Esri symbol “Conservation” in Conservation style file (Marker symbols), except the first symbol. | ||
| Cultural Heritage Object declared by UNESCO (World Heritage Site) |  | Conservation.style > Marker Symbols > Global Awareness |
| Cultural Heritage Object declared by National Historical Institute |  | |
| Cultural Heritage Object declared by National Commission for Culture and Arts |  | |
| Cultural Heritage Object declared by National Museum |  | |
| Cultural Heritage Object declared by LGU |  |
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4.21.04 Scanning
Introduction
On-Screen or Heads Up Digitizing is now the most popular method of digital conversion. It is recommended that Heads-up digitizing is used. First step to be performed in Heads-up digitizing is scanning.
Things to Know About Scanners
There are many scanners available today. For map digitizing, two features are to be considered.
Scan Size
For mapping, it is best to use large scale scanners or scanners that can scan big size documents in one scanning process. There are a variety of large scale scanners; some models offering both large scale printing and scanning are available in the market. The drawback is price. Large scale scanners are expensive. There are also A4 scanners today are very common and available at a cheap price which can scan slightly larger than an A4 size documents. A4 scanners will also come in two types, flatbed and paper feed. Use only flatbed if scanning for A4 scanners. A3 scanners are also available but comes at a higher price that A4 scanners but cheaper than large scale scanners.
Resolution
Second thing to consider is the resolution of the scanner. Usually it is represented in DPI or dots per inch. The more ‘dots per inch’, more details are captured thus a higher resolution. High resolution scanning would also result in a larger file size since it captures more details. And it also takes longer to scan in higher resolutions.
Resolution becomes a factor if we are to use maps with many details are to be captured.
Preparing Maps for Scanning
The accuracy of our digitize data will be dependent on how good the quality of our scanner. Thus it is important to remove any other errors that may cause discrepancies in the map.
Before scanning
- Inspect the map to be scanned. Straighten out any folds and crumples.
- Check map features if they are visible and clear. If not, find better a copy of the same map if available.
- Check for control points* within the map. A map should have at least 4 control points. The more available control points, the better.
* Control points are points on the map where exact positional location can be derived or acquired. - If there are no valid control points, do a research from other maps (digitized or paper) of the same area and identify the areas which can be used as their control points.
During scanning
- When using small scales scanners where it is not possible to cover the whole map in one scanning, make sure to cover all possible control points for each scan portion. Allow at least 15 to 30 % overlaps in between scan portion.
- If a scan portion would already have at least 4 valid control points, these portions can be individually georeference. If less than 4 is available, consolidate scan portions to (using an image processing software) to produce and image having at least 4 valid control points.
- Adjust scanning resolution accordingly if file size will be a consideration. For black and white or single colored map, small resolution can be used as long as the output is readable.
- Inspect the scanned image for clarity. All control points should be visible and all features required to be digitize can be clearly distinguishable.
- Some scanners can adjust they scanning process for better quality by consider the surface type of the material. Some maps may be made of glossy or covered in reflective material (like laminated maps) which will tend to make contrast very bright if the light sensitivity is not adjusted. Adjust appropriately if possible. Reflective surfaces should have a lower light sensitivity setting.
- Save in JPEG format uncompressed.
4.21.05 Georeferencing
Concept of Georeferencing
Georeferencing means that coordinates from a known system are assigned to an aerial photo or scanned paper map (both are raster data). Thus, the photo pixels get a geographic location. The procedure is carried out so that the raster datasets can be used with other spatial data.
If the raster data only consists of a scanned map or photo, attributive data needs to be assigned to the picture. A photo or scanned map is often manually digitized into vector format features.
What Are the Steps in Georeferencing?
The general steps for georeferencing a scanned map or (aerial) photograph are:
- Identifying the reference system of the source data (scanned map). In the Philippines, most of the available data as of now is based on the Luzon datum (the old system). For newer and future datasets produced by NAMRIA and DENR the PRS-92 is used.
- Import and opening of files in the GIS software. Open (and add to the work space) the source data and the other datasets/layers that to be used for the georeferencing.
- Identification of control points. Identify the quality of possible control points (at least four). They have to be identifiable in the source data and the coordinates should be estimated. Control points without clear coordinates must be clearly visible in both the source data and in the datasets. The coordinates must come from the reference datasets. Road intersections, buildings and other obvious landmarks can be used as control points.
- Rectification.
Things to Consider In Georeferencing
The georeferencing operation is a crucial step when transforming analogue data to digital data, as well as raster data to vector format data. The quality of the transformed data depends on both the type and properties of the scanned map (or photograph) as well as the vector datasets.
Control Points
Control points should be clearly seen in both the scanned map (photograph) and the reference data sets. One should aim at using the most accurately measured features in the reference data sets.
The approach should also be to distribute the control points evenly over the scanned map. Preferably, the control points should surround the features that are to be analyzed (and/or) digitized. The transformation itself is in general more accurate in the area that is delimited by the control points than the area outside of the control points.
Residual Errors on Transformation
A value that will indicate the accuracy of the map transformation is the RMS error. High values indicate that something is wrong with either the scanning or the assessed control points or the both.
If the error is particularly large for a control point, this should be removed and a new point could be assessed instead, hopefully with a better result. If the RMS still is very high and there are only four control points, consider re-scanning the map.
Rectification
The term rectification implies a permanent transformation; i.e. the scanned map will be saved as a new (raster) dataset which is georeferenced. It is always recommended to go ahead and rectify a good transformation. If the software asks for resolution or cell size of the rectified image, make use of the resolution of the original image.
4.21.06 Digitizing
Concept of Digitizing
The concept of digitizing refers to the capture of data from analogue maps into a digital format. The procedure includes, first, geographic data capture (e.g. the ‘actual’ extension of a road). Then, second, is attributive data capture (e.g. the name, width, classification or pavement of the road) which is mostly covered in other procedures. Needless to say, these different procedures very much depend on one another, and a coherent and carefully planned approach to the issues would be a good idea. Since current data often only occurs in analogue formats, constructing the GIS database will involve a lot of digitizing to input the data. Maintaining and updating the database will also involve digitizing.
It should be noted that digitizing during the construction phase of a GIS consumes much time and resources. When the LGU is confronted with a large number of analogue datasets to be digitized, it should consider two alternatives. First is to outsource the digitizing project to a professional company, and the second is to ensure that its personnel have proper training, equipment and enough time for the upcoming task of digitizing.
Table Digitizing
Through the set-up of a digitizing table to the GIS Software, digitizing is carried out straight from the paper map. Before starting to use the digitizer, the following steps must be followed.
- Set up the digitizing tablet and install the driver software.
- Configure the digitizer puck buttons.
- Ensure the quality of the paper map.
- Establish control points on the paper map.
- Register the paper map.
Table digitizing can be good if there are several large maps that can not be scanned in a normal scanner. However, the equipment is rather expensive and therefore the screen digitizing provides an option that is as good as table digitizing.
Screen Digitizing
Screen digitizing is carried out after a scanned map (or photograph) has been georeferenced (this corresponds to Steps 4 and 5 in the table digitizing set-up). In the ArcGIS software environment this kind of digitizing is referred to as creating new features while editing a layer. The layer is edited by digitizing the features on the underlying map or photograph.
What Are We Digitizing?
The most available data is analogue maps. Also, some aerial or satellite photographs can be acquired from some institutions.
- Old maps are digitized when the paper itself is “worn and torn” or the paper has shrunk in length but not in width (or the other way round). There is scarce (if any) documentation of the construction and the data of the map. The features range from measured to sketched (or even invented) and their topicality is very old;
- New maps are digitized on high-quality paper, good documentation and only on-ground-measured or photogrammetric measured features;
- Aerial photos are digitized, from which interpretation is done manually and separate different features;
- Satellite photos are digitized where simple remote sensing is done at the same time.
The features often constitute polygons or polylines. Point objects also occur, but very seldom.
Things to Consider When Digitizing
Detail Level/Zooming
Line features should be digitized along the middle of the source line. By using the zooming tool, one can verify that the digitized line follows closely to the middle.
The zooming level is a factor in how accurate digitizing is done. The type and extension of the feature that being digitized should decide the zooming level to be used. However, in order to be efficient one should keep a zooming rate that allows work without having to zoom in or out every ten seconds. In-zooming should be used where the digitizing feature has a complicated extension or border on other features. Large extent in-zooming should only be used when digitizing small features that are accurately represented on the source data, such as the demarcation marks of a piece of lot.
Snapping
Snapping is a valuable tool that helps the operator to avoid common digitizing errors. When using snapping, the new node of a line or polygon will be the same as an already existing node, provided that the cursor is within the snapping distance. Thus, polygons are closed if one clicks within the snapping distance of the polygon’s starting point, and, a new polygon adjacent to an existing polygon will have the same extension and nodes. However, it is important to be aware of the snapping settings and always check the digitized features afterwards to detect possible errors.
The snapping settings enable one to choose to what feature the snapping will be carried out. In ArcGIS choose the layer(s) that contain(s) those features and if snapping will be carried out to these features’ vertices, endpoints or edges. The ‘edge’ setting helps to snap the digitized feature, even if there is no node (vertex) in the proximity. This setting will be useful when a new polygon is digitized along an adjacent existing polygon.
Polygon snapping – A good example of a digitizing task where snapping should be used is in the digitizing of thematic maps (e.g. land coverage.) For example, the forest areas are first digitized as a certain feature class. Thereafter, the agricultural land areas are digitized as another feature class. The snapping settings should now be set to be carried out on vertex and edge to the forest feature class. Whenever an agricultural area is adjacent to a forest area, the operator will snap the agricultural circumference line along the forest circumference line. When it is time to move on with a new feature class, e.g. water bodies, the snapping settings will be set to include the agricultural feature class and so on.
However, if two polygons share a large extension of a circumference line, a better alternative to snapping could be to copy the first digitized polygon to the new feature class. Thereafter the new polygon is cut and the part that should not constitute the new feature class circumference is deleted. Then, digitizing (with appropriate snapping) is carried out on the rest of the polygon.
(Poly)line snapping – An example of snapping polylines is the digitizing of roads. Each road that intersects with another road is properly connected with the use of snapping.
End Points
For some polygon features the operator should not digitize the circumference line. Instead, the points to select are the marked corners. Examples of this include the lots on a cadastral map. The actual border consists of the straight line between two demarcation marks.
Common Digitizing Errors
Polygon Digitizing
There are some common errors that can easily be removed by using the software’s digitizing help tools. Errors like open polygons, surplus and deficits should not be a problem nowadays, since most software automatically finish the polygons in the starting point. “Loop polygons” can, however, still occur.
Polyline Digitizing
The risks with polyline digitizing are that surplus and deficits can occur. These errors are avoided by using the snapping tool.
The Accuracy of Digitized Features – An Important Part of the Metadata
In any GIS it is important to keep track of the quality, topicality and type of data capture.
The digitizing effort will never be better than the analogue source. For example, a barangay boundary identified as a one-millimeter thin line on an analogue map with the scale 1:10 000, already comes with an inaccuracy of 10(!) meters when the line is digitized. That is, providing that the georeferencing of the map was perfectly accomplished and that the boundary itself is perfectly presented in the analogue map, one still cannot apply a more accurate value than 10 meters on the digitized boundary. This is due to the uncertainty of having digitized the exact location on the 1 mm thin line.
This serves as an example of how important it is to be aware of the sources of errors in the whole process of converting data. Thus, it is recommended that a dataset’s metadata contains information about the original data (such as how it was measured, original scale, and its topicality) and how this was captured into digital format (e.g. through screen digitizing of a scanned georeferenced copy of the original map).