GIS DATA TYPES & DATA MODELS

Although the two terms, data and information, are often used indiscriminately, they both have a specific meaning. Data can bedescribed as different observations, which are collected and stored. Information is that data, which is useful in answering queries or solving a problem.Map making is an old art but digitizing a large number of maps provides a large amount of data after hours of painstaking works, but thedata can only render useful information if it is used in analysis.

● GIS DATA TYPES:

1. Attribute Data:
The attributes refer to the properties of spatial entities. They are often referred to as non-spatial data since they do not in themselves represent location information. This type of data describes characteristics of the spatial features. These characteristics can be quantitative and/or qualitative in nature. Attribute data is often referred to as tabular data.

2. Spatial Data:
Geographic position refers to the fact that each feature has a location that must be specified in a unique way. To specify the position in an absolute way a coordinate system is used. For small areas, the simplest coordinate system is the regular square grid. For larger areas, certain approved cartographic projections are commonly used. Internationally there are many different coordinate systems in use. This Locational information is provided in maps by using Points, Lines and Polygons.These geometric descriptions are the basic data elements of a map. Thus spatial data describes the absolute and relative location of geographic features.

The coordinate location of a forest would be spatial data, while the characteristics of that forest, e.g. cover group, dominant species, crown closure, height, etc., would be attribute data. Other data types, in particular image and multimedia data, have become more prevalent with changing technology. Depending on the specific content of the data, image data may be considered either spatial, e.g. photographs, animation, movies, etc., or attribute, e.g. sound, descriptions, narration’s, etc.

● GIS DATA MODELS:

A GIS is based on data, hence there must be a data model that has to be followed to standardize procedures.
They are :
1. Spatial Data Models
2. Attribute Data Models

● SPATIAL DATA MODELS:
Traditionally spatial data has been stored and presented in the form of a map. Three basic types of spatial data models have evolved for storing geographic data digitally. These are referred to as:

Raster Vector Image

The selection of a particular data model, vector or raster, is dependent on the source and type of data, as well as the intended use of the data. Certain analytical procedures require raster data while others are better suited to vector data.

Raster Data Formats:

A simple raster data set is a regular grid of cells divided into rows and columns. In a raster data set, data values for a given parameter are stored in each cell – these values may represent an elevation in meters above sea level, a land use class, a plant biomass in grams per square meter, and so forth. The spatial resolution of the raster data set is determined by the size of the cell.
For example, Landsat TM satellite imagery data are raster data that are corrected to have a cell size of approximately 30 meterson a side. However, spatial resolution can be much finer, or much coarser than 30 meters. In general, spatial resolution is a function of the data collection techniques used, and the desired outcomes.

The size of cells in a tessellated data structure is selected on the basis of the data accuracy and the resolution needed by the user. There is no explicit coding of geographic coordinates required since that is implicit in the layout of the cell A raster data structure is in fact a matrix where any coordinate can be quickly calculated if the origin point is known, and the size of the grid cells is known. Since grid-cells can be handled as two-dimensional arrays in computer encoding many analytical operations are easy to program. This makes tessellated data structures a popular choice for many GIS software. Topology is not a relevant concept with tessellated structures since adjacency and connectivity are implicit in the location of a particular cell in the data matrix.

Since geographic data is rarely distinguished by regularly spaced shapes, cells must be classified as to the most common attribute for the cell. The problem of determining the proper resolution for a particular data layer can be a concern. If one selects too coarse a cell size then data may be overly generalized. If one selects too fine a cell size then too many cells may be created resulting in a large data volume, slower processing times, and a more cumbersome data set. As well, one can imply accuracy greater than that of the original data capture process and this may result in some erroneous results during analysis. As well,since most data is captured in a vector format, e.g. digitizing, data must be converted to the raster data structure. This is called vector-raster conversion. Most GIS software allows the user to define the raster grid (cell) size for vector-raster conversion. It is imperative that the original scale, e.g. accuracy, of the data be known prior to conversion. The accuracy of the data, often referred to as the resolution, should determine the cell size of the output raster map during conversion. Most raster based GIS software
requires that the raster cell contain only a single discrete value. Accordingly, a data layer, e.g. forest inventory stands, may be broken down into a series of raster maps, each representing an attribute type, e.g. a species map, a height map, a density map, etc. These are often referred to as one attribute maps. This is in contrast to most conventional vector data models that maintain data as multiple attribute maps.
A Simple Raster Data SetEach cell in the raster is assigned a single data value. In the above example simple binary data values have been used meaning that the possibilities are limited to two digit numbers – either 0 or 1. This is an example of a 1-bit raster data file. Mathematically, there are only two possibilities for each pixel, 0 or 1. By contrast in an 8-bit data file, there are 256 possibilities of data values for each pixel. In the above example, the computer “sees” the cells that contain 0 as “turned off”, while the cells that contain 1
as “turned on”.
A One Bit Raster Image                                 source

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Biofuels: ‘Green gold’ or a problems untold?

No subject appears to divide as many people in the climate change arena as biofuels. Their potential to positively impact greenhouse gas emissions is undoubtedly great.   

But the pursuit of such non-fossil fuel energy replacements has raised concerns over the impact on the global food supply — and the environment itself.

When people talk about biofuels they are essentially referring to ethanol or biodiesel, the former favored by the Americas (Brazil and the U.S. produce 90 percent of the world’s ethanol between them); the latter preferred by Europe (accounting for 89 percent of global biodiesel production in 2005).

Biofuels can be produced from any number of plant crops; most ethanol in the world today being derived from corn, with sugar cane increasingly gaining favor (the latter providing double the yield per acre of the former).

Biodiesel emanates mainly from vegetable oils or animal fats, and waste cooking oil from China’s restaurants has been to thank for supplying China’s growing biodiesel industry.

Cellulosic ethanol, also fast becoming the darling of the biofuel movement, is produced by breaking down plant cell walls. And as cellulose is the most common organic compound around, it can be sourced from many more places and has the added bonus of turning things that used to be regarded as waste — corn stalks, wood chips, grasses — into incredibly useful sources of energy.

Are biofuels carbon neutral?

Biofuels are often referred to as being carbon neutral — that is, the carbon dioxide (CO2) that is released through burning them is roughly equal to the amount they sequester when in plant form. In that way, growing biofuel crops helps ease global warming, their proponents argue, as they act as “carbon sinks” reducing CO2 levels.

In that way, relative to petroleum, biofuels could reduce greenhouse gas emissions by “more than 100 percent”, World Watch says. Switchgrass and other grasses rate the highest in this regard, with the possibility of reducing emissions by between 70 and 110 percent (relative to petroleum fuels) compared to corn and wheat only offering maximum reductions of 40 percent.

According to the U.S. Departments of Energy and Agriculture, the 1.3 billion tons of “biomass feedstock… potentially available in the U.S. for the production of biofuels” is currently enough to replace around 30 percent of the country’s existing gasoline consumption.

Most existing cars can use them immediately — in small doses anyway (most gasoline-fueled cars can take up to a 10 percent ethanol blend in their fuel without any conversions). They are generally much better for your health when burnt too, reducing tailpipe carbon monoxide emissions by up to 30 percent, and fine particulate matter emissions by 50 percent, according to the Renewable Fuels Association.

Growing pains: Food vs. fuel

Biofuels currently only represent one percent of fuel used in transportation globally, but around one percent of the world’s fields — that’s around 12 million hectares — has already been turned over for their production.

But there are already questions over whether there is enough land available for biofuels to be able to replace even 50 percent of our fossil fuel use. According to The International Institute for Applied Systems Analysis, a maximum of 300 million hectares worldwide could be used for biofuels, but even if the biofuel industry used up 290 million hectares of that, it would only meet one tenth of the projected energy demands for 2030.

Europe only has a limited amount of land to set aside for biofuels, and according to the Soil Association, quoting OECD figures, if the EU gave up 72 per cent of its arable land, it would only only be able to serve 10 percent of its fuel needs.

Biofuels will however offer some economic incentives for developing nations. The concern is whether the pursuit of economic growth through biofuel production in developing countries will spur further deforestation and land conversions.

According to the World Food Programme (WFP) more poor African farmers are opting to sell crops like cassava for use as alternative energy instead of food — purely for economic reasons. Some say, at least they are decreasing their reliance on increasingly expensive oil at the same time as 25 out of the 47 poorest countries on earth import all of their oil.

However others are warning of a vicious cycle being created by a dangerous food versus fuel competition, which will lead to food shortages, driving up food prices, and encouraging even more farmers to choose to grow fuel over food crops to meet the increasing demand (exacerbated by population growth) and clearing more land in the process.

It was on the subject of land clearing that the biofuel industry was chastised this month, in what could be the biggest blow dealt to biofuels yet.

A report from the Nature Conservancy and the University of Minnesota has raised serious questions over how biofuels are grown. Converting rainforests, peatlands, savannahs or grasslands to grow fuel crops releases CO2, in some cases a staggering 420 times more CO2 than from burning fossil fuel, the report says.

And that, they say, creates a “carbon debt” (with the emissions savings of biofuels fully considered) that could take as long as 840 years to cancel out depending on what land you are converting.

The worst land to convert is tropical peatland rainforest (creating a carbon debt of 840 years) or Amazonian rainforest (320 years) with the lowest carbon debt of 17 years created by converting the wetter woodland-savannahs of Brazil’s Cerrado.

Creating new problems?

The methods used to grow fuel crops continue to be held under a magnifying glass; the initial furore over this new form of energy quickly dampened by realizations that in the pursuit of climate change solutions, nothing is ever simple.

Some have even questioned the role fertilizer is playing in climate change, with George Monbiot recently pointing out in The Guardian (based on a recent claim by the Nobel Laureate, Paul Crutzen) that using fertilizer on biofuel crops will be emitting enough nitrous oxide (more than 296 times more powerful heat trapping gas than CO2) to “wipe out all the carbon savings biofuels produce.”

Biofuel crops could also put an unbearable strain on the global water supply, Sweden’s Stockholm Environment Institute has warned. It says replacing 50 percent of the fossil fuels to meet 2050 transport and electricity demands with biofuels would require up to 12,000 extra cubic kilometers of water a year (the total annual flow down the world’s rivers is 14,000 cubic kilometers).

China and India are at particular risk of water scarcity issues, the International Water Management Institute has warned.

The “best case scenario” many believe is making biofuels from native grasses and woody biomass grown on lands unsuitable for crop growing. The U.S. Agricultural Research Service recently found, for example, that growing native switchgrass on the prairies of Nebraska and North and South Dakota resulted in cellulosic ethanol yielding 5.4 times more energy than all the energy that went into producing it.

Native plants are also viewed as “carbon negative,” as they are able to store excess C02 in their roots and the surrounding soil. But there are even downsides to using some agricultural and forestry waste products, as not only are they carbon sequesters but they provide nutrients for the soil. Removing them can lead to rapid soil erosion.

What Monbiot recently wrote in The Guardian reflects what many are increasingly coming to believe: “Apart from used chip fat, there is no such thing as a sustainable biofuel.”
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Posted in Environment, Global Warming | 1 Comment

ERRORS IN GIS

No map is perfect. Even the most accurate maps created by a GIS have some deficiencies. These deficiencies occur due to
“Errors” that may have taken place at different stages of GIS implementation. These errors reduce the accuracy of the map
generated. However by use of well defined and controlled procedures these errors can be avoided.

There are two types of errors in GIS:

I. Source Errors:

They are the errors that are present in “Source Data” that is given to the GIS. They occur before the actual implementation
of GIS:

a. Instrumental inaccuracies:
● Satellite/ air photo/ GPS/ surveying (spatial).
● Inaccuracies in attribute measuring instruments.

b. Human Processing:
● Misinterpretation (e.g. photos), spatial and attribute.
● Effects of scale change and generalization.
● Effects of classification (nominal / ordinal / interval).

c. Actual Changes:
● Gradual ‘natural’ changes: river courses, glacier recession.
● Catastrophic change: fires, floods, landslides.
● Seasonal and daily changes: lake/sea/ river levels.
● Man-made: urban development, new roads.
● Attribute change: forest growth (height etc.), discontinued trail / roads, road surfacing.

II. Processing Errors:
They are the errors that occur during the processing of the data i.e. during the implementation of GIS.

a. Input:
● Digitizing: human error, the width of a line, spikes, knots, also entering attribute data.
● Dangling nodes (connected to only one arc): permissible in arc themes (river headwaters etc.).
● Pseudo-nodes (connected to one or two arcs) – permissible in island arcs, and where attributes change,
e.g. road becomes paved from dirt or vice versa.
● Projection input error.

b. Manipulation:
● Interpolation of point data into lines and surfaces.
● Overlay of layers, digitized separately, e.g. soils and vegetation.
● The compounding effects of processing and analysis of multiple. layers: for example, if two layers each have
correctness of 90%, the accuracy of the resulting overlay is around 81%.
● Density of observations.
● Inappropriate or inadequate inputs for models.

c. Output:
● Scale changes – detail and scale bars.
● Color palettes: intended colors don’t match from screen to Printer.
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More About Cracks in India

After the drought-hit Hamirpur, Jalaun and Orai districts in Uttar Pradesh’s Bundelkhand region, wide cracks have now developed on the earth in several other districts including Rae Bareilly, Allahabad and Kanpur, officials said Monday.

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