Saturday, July 19, 2014

STATISTICS

STATISTICS

More probability density is found as one gets closer to the expected (mean) value in a normal distribution. Statistics used in standardized testing assessment are shown. The scales include standard deviations, cumulative percentages, percentile equivalents, Z-scores, T-scores, standard nines, and percentages in standard nines.
Statistics is the study of the collection, organization, analysis, interpretation and presentation of data.[1] It deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments.[1] When analyzing data, it is possible to use one of two statistics methodologies: descriptive statistics or inferential statistics.[2]

Scope

Statistics is a mathematical body of science that pertains to the collection, analysis, interpretation or explanation, and presentation of data,[3] or as a branch of mathematics.[4] Some consider statistics to be a distinct mathematical science rather than a branch of mathematics.[vague][5][6]

Mathematical statistics

Mathematical statistics is the application of mathematics to statistics, which was originally conceived as the science of the state — the collection and analysis of facts about a country: its economy, land, military, population, and so forth. Mathematical techniques which are used for this include mathematical analysis, linear algebra, stochastic analysis, differential equations, and measure-theoretic probability theory.[7][8]

Overview

In applying statistics to e.g. a scientific, industrial, or societal problem, it is necessary to begin with a population or process to be studied. Populations can be diverse topics such as "all persons living in a country" or "every atom composing a crystal".
Ideally, statisticians compile data about the entire population (an operation called census). This may be organized by governmental statistical institutes. Descriptive statistics can be used to summarize the population data. Numerical descriptors include mean and standard deviation for continuous data types (like income), while frequency and percentage are more useful in terms of describing categorical data (like race).
When a census is not feasible, a chosen subset of the population called a sample is studied. Once a sample that is representative of the population is determined, data is collected for the sample members in an observational or experimental setting. Again, descriptive statistics can be used to summarize the sample data. However, the drawing of the sample has been subject to an element of randomness, hence the established numerical descriptors from the sample are also due to uncertainty. In order to still draw meaningful conclusions about the entire population, inferential statistics is needed. It uses patterns in the sample data to draw inferences about the population represented, accounting for randomness. These inferences may take the form of: answering yes/no questions about the data (hypothesis testing), estimating numerical characteristics of the data (estimation), describing associations within the data (correlation) and modeling relationships within the data (for example, using regression analysis). Inference can extend to forecasting, prediction and estimation of unobserved values either in or associated with the population being studied; it can include extrapolation and interpolation of time series or spatial data, and can also include data mining.

Data collection

Sampling

In case census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Statistics itself also provides tools for prediction and forecasting the use of data through statistical models. To use a sample as a guide to an entire population, it is important that it truly represent the overall population. Representative sampling assures that inferences and conclusions can safely extend from the sample to the population as a whole. A major problem lies in determining the extent that the sample chosen is actually representative. Statistics offers methods to estimate and correct for any random trending within the sample and data collection procedures. There are also methods of experimental design for experiments that can lessen these issues at the outset of a study, strengthening its capability to discern truths about the population.
Sampling theory is part of the mathematical discipline of probability theory. Probability is used in "mathematical statistics" (alternatively, "statistical theory") to study the sampling distributions of sample statistics and, more generally, the properties of statistical procedures. The use of any statistical method is valid when the system or population under consideration satisfies the assumptions of the method. The difference in point of view between classic probability theory and sampling theory is, roughly, that probability theory starts from the given parameters of a total population to deduce probabilities that pertain to samples. Statistical inference, however, moves in the opposite direction—inductively inferring from samples to the parameters of a larger or total population.

Experimental and observational studies

A common goal for a statistical research project is to investigate causality, and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on dependent variables or response. There are two major types of causal statistical studies: experimental studies and observational studies. In both types of studies, the effect of differences of an independent variable (or variables) on the behavior of the dependent variable are observed. The difference between the two types lies in how the study is actually conducted. Each can be very effective. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation. Instead, data are gathered and correlations between predictors and response are investigated. While the tools of data analysis work best on data from randomized studies, they are also applied to other kinds of data – like natural experiments and observational studies[9] – for which a statistician would use a modified, more structured estimation method (e.g., Difference in differences estimation and instrumental variables, among many others) that will produce consistent estimators.

Experiments

The basic steps of a statistical experiment are:
  1. Planning the research, including finding the number of replicates of the study, using the following information: preliminary estimates regarding the size of treatment effects, alternative hypotheses, and the estimated experimental variability. Consideration of the selection of experimental subjects and the ethics of research is necessary. Statisticians recommend that experiments compare (at least) one new treatment with a standard treatment or control, to allow an unbiased estimate of the difference in treatment effects.
  2. Design of experiments, using blocking to reduce the influence of confounding variables, and randomized assignment of treatments to subjects to allow unbiased estimates of treatment effects and experimental error. At this stage, the experimenters and statisticians write the experimental protocol that shall guide the performance of the experiment and that specifies the primary analysis of the experimental data.
  3. Performing the experiment following the experimental protocol and analyzing the data following the experimental protocol.
  4. Further examining the data set in secondary analyses, to suggest new hypotheses for future study.
  5. Documenting and presenting the results of the study.
Experiments on human behavior have special concerns. The famous Hawthorne study examined changes to the working environment at the Hawthorne plant of the Western Electric Company. The researchers were interested in determining whether increased illumination would increase the productivity of the assembly line workers. The researchers first measured the productivity in the plant, then modified the illumination in an area of the plant and checked if the changes in illumination affected productivity. It turned out that productivity indeed improved (under the experimental conditions). However, the study is heavily criticized today for errors in experimental procedures, specifically for the lack of a control group and blindness. The Hawthorne effect refers to finding that an outcome (in this case, worker productivity) changed due to observation itself. Those in the Hawthorne study became more productive not because the lighting was changed but because they were being observed.[10]

Observational study

An example of an observational study is one that explores the correlation between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case, the researchers would collect observations of both smokers and non-smokers, perhaps through a case-control study, and then look for the number of cases of lung cancer in each group.

Type of data

Main articles: Statistical data type and Levels of measurement
Various attempts have been made to produce a taxonomy of levels of measurement. The psychophysicist Stanley Smith Stevens defined nominal, ordinal, interval, and ratio scales. Nominal measurements do not have meaningful rank order among values, and permit any one-to-one transformation. Ordinal measurements have imprecise differences between consecutive values, but have a meaningful order to those values, and permit any order-preserving transformation. Interval measurements have meaningful distances between measurements defined, but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit), and permit any linear transformation. Ratio measurements have both a meaningful zero value and the distances between different measurements defined, and permit any rescaling transformation.
Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically, sometimes they are grouped together as categorical variables, whereas ratio and interval measurements are grouped together as quantitative variables, which can be either discrete or continuous, due to their numerical nature. Such distinctions can often be loosely correlated with data type in computer science, in that dichotomous categorical variables may be represented with the Boolean data type, polytomous categorical variables with arbitrarily assigned integers in the integral data type, and continuous variables with the real data type involving floating point computation. But the mapping of computer science data types to statistical data types depends on which categorization of the latter is being implemented.
Other categorizations have been proposed. For example, Mosteller and Tukey (1977)[11] distinguished grades, ranks, counted fractions, counts, amounts, and balances. Nelder (1990)[12] described continuous counts, continuous ratios, count ratios, and categorical modes of data. See also Chrisman (1998),[13] van den Berg (1991).[14]
The issue of whether or not it is appropriate to apply different kinds of statistical methods to data obtained from different kinds of measurement procedures is complicated by issues concerning the transformation of variables and the precise interpretation of research questions. "The relationship between the data and what they describe merely reflects the fact that certain kinds of statistical statements may have truth values which are not invariant under some transformations. Whether or not a transformation is sensible to contemplate depends on the question one is trying to answer" (Hand, 2004, p. 82).[15]

Terminology and theory of inferential statistics

Statistics, estimators and pivotal quantities

Consider an independent identically distributed (iid) random variables with a given probability distribution: standard statistical inference and estimation theory defines a random sample as the random vector given by the column vector of these iid variables.[16] The population being examined is described by a probability distribution which may have unknown parameters.
A statistic is random variable which is a function of the random sample, but not a function of unknown parameters. The probability distribution of the statistics, though, may have unknown parameters.
Consider now a function of the unknown parameter: an estimator is a statistic used to estimate such function. Commonly used estimators include sample mean, unbiased sample variance and sample covariance.
A random variable which is a function of the random sample and of the unknown parameter, but whose probability distribution does not depend on the unknown parameter is called a pivotal quantity or pivot. Widely used pivots include the z-score, the chi square statistic and Student's t-value.
Between two estimators of a given parameter, the one with lower mean squared error is said to be more efficient. Furthermore an estimator is said to be unbiased if it's expected value is equal to the true value of the unknown parameter which is being estimated and asymptotically unbiased if its expected value converges at the limit to the true value of such parameter.
Other desirable properties for estimators include: UMVUE estimators which have the lowest variance for all possible values of the parameter to be estimated (this is usually an easier property to verify than efficiency) and consistent estimators which converges in probability to the true value of such parameter.
This still leaves the question of how to obtain estimators in a given situation and carry the computation, several methods have been proposed: the method of moments, the maximum likelihood method, the least squares method and the more recent method of estimating equations.

Null hypothesis and alternative hypothesis

Interpretation of statistical information can often involve the development of a null hypothesis in that the assumption is that whatever is proposed as a cause has no effect on the variable being measured.
The best illustration for a novice is the predicament encountered by a jury trial. The null hypothesis, H0, asserts that the defendant is innocent, whereas the alternative hypothesis, H1, asserts that the defendant is guilty. The indictment comes because of suspicion of the guilt. The H0 (status quo) stands in opposition to H1 and is maintained unless H1 is supported by evidence "beyond a reasonable doubt". However, "failure to reject H0" in this case does not imply innocence, but merely that the evidence was insufficient to convict. So the jury does not necessarily accept H0 but fails to reject H0. While one can not "prove" a null hypothesis, one can test how close it is to being true with a power test, which tests for type II errors.
What statisticians call a alternative hypothesis is simply an hypothesis which contradicts the null hypothesis.

Error

Working from a null hypothesis two basic forms of error are recognized:
  • Type I errors where the null hypothesis is falsely rejected giving a "false positive".
  • Type II errors where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a "false negative".
Standard deviation refers to the extent to which individual observations in a sample differ from a central value, such as the sample or population mean, while Standard error refers to an estimate of difference between sample mean and population mean.
A statistical error is the amount by which an observation differs from its expected value, a residual is the amount an observation differs from the value the estimator of the expected value assumes on a given sample (also called prediction).
Mean squared error is used for obtaining efficient estimators, a widely used class of estimators. Root mean square error is simply the square root of mean squared error.
Many statistical methods seek to minimize the residual sum of squares, and these are called "methods of least squares" in contrast to Least absolute deviations. The later gives equal weight to small and big errors, while the former gives more weight to large errors. Residual sum of squares is also differentiable, which provides a handy property for doing regression. Least squares applied to linear regression is called ordinary least squares method and least squares applied to nonlinear regression is called non-linear least squares. Also in a linear regression model the non deterministic part of the model is called error term, disturbance or more simply noise.
Measurement processes that generate statistical data are also subject to error. Many of these errors are classified as random (noise) or systematic (bias), but other important types of errors (e.g., blunder, such as when an analyst reports incorrect units) can also be important. The presence of missing data and/or censoring may result in biased estimates and specific techniques have been developed to address these problems.[17]

Interval estimation

Main article: Interval estimation
Most studies only sample part of a population, so results don't fully represent the whole population. Any estimates obtained from the sample only approximate the population value. Confidence intervals allow statisticians to express how closely the sample estimate matches the true value in the whole population. Often they are expressed as 95% confidence intervals. Formally, a 95% confidence interval for a value is a range where, if the sampling and analysis were repeated under the same conditions (yielding a different dataset), the interval would include the true (population) value in 95% of all possible cases. This does not imply that the probability that the true value is in the confidence interval is 95%. From the frequentist perspective, such a claim does not even make sense, as the true value is not a random variable. Either the true value is or is not within the given interval. However, it is true that, before any data are sampled and given a plan for how to construct the confidence interval, the probability is 95% that the yet-to-be-calculated interval will cover the true value: at this point, the limits of the interval are yet-to-be-observed random variables. One approach that does yield an interval that can be interpreted as having a given probability of containing the true value is to use a credible interval from Bayesian statistics: this approach depends on a different way of interpreting what is meant by "probability", that is as a Bayesian probability.
In principle confidence intervals can be symmetrical or asymmetrical. An interval can be asymmetrical because it works as lower or upper bound for a parameter (left-sided interval or right sided interval), but it can also be asymmetrical because the two sided interval is built violating symmetry around the estimate. Sometimes the bounds for a confidence interval are reached asymptotically and these are used to approximate the true bounds.

Significance

Main article: Statistical significance
Statistics rarely give a simple Yes/No type answer to the question asked of them. Interpretation often comes down to the level of statistical significance applied to the numbers and often refers to the probability of a value accurately rejecting the null hypothesis (sometimes referred to as the p-value).
Referring to statistical significance does not necessarily mean that the overall result is significant in real world terms. For example, in a large study of a drug it may be shown that the drug has a statistically significant but very small beneficial effect, such that the drug is unlikely to help the patient noticeably.
Criticisms arise because the hypothesis testing approach forces one hypothesis (the null hypothesis) to be "favored," and can also seem to exaggerate the importance of minor differences in large studies. A difference that is highly statistically significant can still be of no practical significance, but it is possible to properly formulate tests in account for this. (See also criticism of hypothesis testing.)
One response involves going beyond reporting only the significance level to include the p-value when reporting whether a hypothesis is rejected or accepted. The p-value, however, does not indicate the size of the effect. A better and increasingly common approach is to report confidence intervals. Although these are produced from the same calculations as those of hypothesis tests or p-values, they describe both the size of the effect and the uncertainty surrounding it.

Examples

Some well-known statistical tests and procedures are:

Misuse of statistics

Main article: Misuse of statistics
Misuse of statistics can produce subtle, but serious errors in description and interpretation—subtle in the sense that even experienced professionals make such errors, and serious in the sense that they can lead to devastating decision errors. For instance, social policy, medical practice, and the reliability of structures like bridges all rely on the proper use of statistics.
Even when statistical techniques are correctly applied, the results can be difficult to interpret for those lacking expertise. The statistical significance of a trend in the data—which measures the extent to which a trend could be caused by random variation in the sample—may or may not agree with an intuitive sense of its significance. The set of basic statistical skills (and skepticism) that people need to deal with information in their everyday lives properly is referred to as statistical literacy.
There is a general perception that statistical knowledge is all-too-frequently intentionally misused by finding ways to interpret only the data that are favorable to the presenter.[18] A mistrust and misunderstanding of statistics is associated with the quotation, "There are three kinds of lies: lies, damned lies, and statistics". Misuse of statistics can be both inadvertent and intentional, and the book How to Lie with Statistics[18] outlines a range of considerations. In an attempt to shed light on the use and misuse of statistics, reviews of statistical techniques used in particular fields are conducted (e.g. Warne, Lazo, Ramos, and Ritter (2012)).[19]
Ways to avoid misuse of statistics include using proper diagrams and avoiding bias.[20] Misuse can occur when conclusions are overgeneralized and claimed to be representative of more than they really are, often by either deliberately or unconsciously overlooking sampling bias.[21] Bar graphs are arguably the easiest diagrams to use and understand, and they can be made either by hand or with simple computer programs.[20] Unfortunately, most people do not look for bias or errors, so they are not noticed. Thus, people may often believe that something is true even if it is not well represented.[21] To make data gathered from statistics believable and accurate, the sample taken must be representative of the whole.[22] According to Huff, "The dependability of a sample can be destroyed by [bias]... allow yourself some degree of skepticism."[23]
To assist in the understanding of statistics Huff proposed a series of questions to be asked in each case:[23]
  • Who says so? (Does he/she have an axe to grind?)
  • How does he/she know? (Does he/she have the resources to know the facts?)
  • What’s missing? (Does he/she give us a complete picture?)
  • Did someone change the subject? (Does he/she offer us the right answer to the wrong problem?)
  • Does it make sense? (Is his/her conclusion logical and consistent with what we already know?)

Misinterpretation: correlation

The concept of correlation is particularly noteworthy for the potential confusion it can cause. Statistical analysis of a data set often reveals that two variables (properties) of the population under consideration tend to vary together, as if they were connected. For example, a study of annual income that also looks at age of death might find that poor people tend to have shorter lives than affluent people. The two variables are said to be correlated; however, they may or may not be the cause of one another. The correlation phenomena could be caused by a third, previously unconsidered phenomenon, called a lurking variable or confounding variable. For this reason, there is no way to immediately infer the existence of a causal relationship between the two variables. (See Correlation does not imply causation.)

History of statistical science

Main articles: History of statistics and Founders of statistics
R A Fisher is the founder of Statistics.
Statistical methods date back at least to the 5th century BC.
Some scholars pinpoint the origin of statistics to 1663, with the publication of Natural and Political Observations upon the Bills of Mortality by John Graunt.[24] Early applications of statistical thinking revolved around the needs of states to base policy on demographic and economic data, hence its stat- etymology. The scope of the discipline of statistics broadened in the early 19th century to include the collection and analysis of data in general. Today, statistics is widely employed in government, business, and natural and social sciences.
Its mathematical foundations were laid in the 17th century with the development of the probability theory by Blaise Pascal and Pierre de Fermat. Mathematical probability theory arose from the study of games of chance, although the concept of probability was already examined in medieval law and by philosophers such as Juan Caramuel.[25] The method of least squares was first described by Adrien-Marie Legendre in 1805.
Karl Pearson, the founder of mathematical statistics.
The modern field of statistics emerged in the late 19th and early 20th century in three stages.[26] The first wave, at the turn of the century, was led by the work of Sir Francis Galton and Karl Pearson, who transformed statistics into a rigorous mathematical discipline used for analysis, not just in science, but in industry and politics as well. Galton's contributions to the field included introducing the concepts of standard deviation, correlation, regression and the application of these methods to the study of the variety of human characteristics – height, weight, eyelash length among others.[27] Pearson developed the Correlation coefficient, defined as a product-moment,[28] the method of moments for the fitting of distributions to samples and the Pearson's system of continuous curves, among many other things.[29] Galton and Pearson founded Biometrika as the first journal of mathematical statistics and biometry, and the latter founded the world's first university statistics department at University College London.[30]
The second wave of the 1910s and 20s was initiated by William Gosset, and reached its culmination in the insights of Sir Ronald Fisher, who wrote the textbooks that were to define the academic discipline in universities around the world. Fisher's most important publications were his 1916 seminal paper The Correlation between Relatives on the Supposition of Mendelian Inheritance and his classic 1925 work Statistical Methods for Research Workers. His paper was the first to use the statistical term, variance. He developed rigorous experimental models and also originated the concepts of sufficiency, ancillary statistics, Fisher's linear discriminator and Fisher information.[31]
The final wave, which mainly saw the refinement and expansion of earlier developments, emerged from the collaborative work between Egon Pearson and Jerzy Neyman in the 1930s. They introduced the concepts of "Type II" error, power of a test and confidence intervals. Jerzy Neyman in 1934 showed that stratified random sampling was in general a better method of estimation than purposive (quota) sampling.[32] Today, statistical methods are applied in all fields that involve decision making, for making accurate inferences from a collated body of data and for making decisions in the face of uncertainty based on statistical methodology. The use of modern computers has expedited large-scale statistical computations, and has also made possible new methods that are impractical to perform manually.

Trivia

Applied statistics, theoretical statistics and mathematical statistics

"Applied statistics" comprises descriptive statistics and the application of inferential statistics.[33][verification needed] Theoretical statistics concerns both the logical arguments underlying justification of approaches to statistical inference, as well encompassing mathematical statistics. Mathematical statistics includes not only the manipulation of probability distributions necessary for deriving results related to methods of estimation and inference, but also various aspects of computational statistics and the design of experiments.

Machine learning and data mining

Statistics has many ties to machine learning and data mining.

Statistics in society

Statistics is applicable to a wide variety of academic disciplines, including natural and social sciences, government, and business. Statistical consultants can help organizations and companies that don't have in-house expertise relevant to their particular questions.

Statistical computing

gretl, an example of an open source statistical package
Main article: Computational statistics
The rapid and sustained increases in computing power starting from the second half of the 20th century have had a substantial impact on the practice of statistical science. Early statistical models were almost always from the class of linear models, but powerful computers, coupled with suitable numerical algorithms, caused an increased interest in nonlinear models (such as neural networks) as well as the creation of new types, such as generalized linear models and multilevel models.
Increased computing power has also led to the growing popularity of computationally intensive methods based on resampling, such as permutation tests and the bootstrap, while techniques such as Gibbs sampling have made use of Bayesian models more feasible. The computer revolution has implications for the future of statistics with new emphasis on "experimental" and "empirical" statistics. A large number of both general and special purpose statistical software are now available.

Statistics applied to mathematics or the arts

Traditionally, statistics was concerned with drawing inferences using a semi-standardized methodology that was "required learning" in most sciences. This has changed with use of statistics in non-inferential contexts. What was once considered a dry subject, taken in many fields as a degree-requirement, is now viewed enthusiastically.[according to whom?] Initially derided by some mathematical purists, it is now considered essential methodology in certain areas.
  • In number theory, scatter plots of data generated by a distribution function may be transformed with familiar tools used in statistics to reveal underlying patterns, which may then lead to hypotheses.
  • Methods of statistics including predictive methods in forecasting are combined with chaos theory and fractal geometry to create video works that are considered to have great beauty.
  • The process art of Jackson Pollock relied on artistic experiments whereby underlying distributions in nature were artistically revealed.[citation needed] With the advent of computers, statistical methods were applied to formalize such distribution-driven natural processes to make and analyze moving video art.[citation needed]
  • Methods of statistics may be used predicatively in performance art, as in a card trick based on a Markov process that only works some of the time, the occasion of which can be predicted using statistical methodology.
  • Statistics can be used to predicatively create art, as in the statistical or stochastic music invented by Iannis Xenakis, where the music is performance-specific. Though this type of artistry does not always come out as expected, it does behave in ways that are predictable and tunable using statistics.

Specialized disciplines

Main article: List of fields of application of statistics
Statistical techniques are used in a wide range of types of scientific and social research, including: biostatistics, computational biology, computational sociology, network biology, social science, sociology and social research. Some fields of inquiry use applied statistics so extensively that they have specialized terminology. These disciplines include:
Medical Statistics
In addition, there are particular types of statistical analysis that have also developed their own specialised terminology and methodology:
Statistics form a key basis tool in business and manufacturing as well. It is used to understand measurement systems variability, control processes (as in statistical process control or SPC), for summarizing data, and to make data-driven decisions. In these roles, it is a key tool, and perhaps the only reliable tool.

See also

Find more about Statistics at Wikipedia's sister projects
Definitions and translations from Wiktionary
Media from Commons
Quotations from Wikiquote
Source texts from Wikisource
Textbooks from Wikibooks
Learning resources from Wikiversity
Library resources about
Statistics
Main article: Outline of statistics
Foundations and major areas of statistics

References

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AERIAL PHOTOGRAPHIC AND SATELLITE IMAGE INTERPRETATION

AAERIAL  PHOTOGRAPHIC   AND    SATELLITE   INTERPRETATION.

Photographic interpretation is “the act of examining photographic images for the purpose of identifying objects and judging their significance” (Colwell, 1997). This mainly refers to its usage in military aerial reconnaissance using photographs taken from reconnaissance aircraft.
Principles of image interpretation have been developed empirically for more than 150 years. The most basic of these principles are the elements of image interpretation. They are: location, size, shape, shadow, tone/color, texture, pattern, height/depth and site/situation/association. These are routinely used when interpreting an aerial photo or analyzing a photo-like image. A well-trained image interpreter uses many of these elements during his or her analysis without really thinking about them. However, a beginner may not only have to force himself or herself to consciously evaluate an unknown object with respect to these elements, but also analyze its significance in relation to the other objects or phenomena in the photo or image.

Elements of Interpretation

The following are elements of aerial photographic and satellite image interpretation.

Location

There are two primary methods to obtain precise location in the form of coordinates. 1) survey in the field using traditional surveying techniques or global positioning system instruments, or 2) collect remotely sensed data of the object, rectify the image and then extract the desired coordinate information. Most scientists who choose option 1 now use relatively inexpensive GPS instruments in the field to obtain the desired location of an object. If option 2 is chosen, most aircraft used to collect the remotely sensed data have a GPS receiver. This allows the aircraft to obtain exact latitude/longitude coordinates each time a photo is taken.

Size

The size of an object is one of the most distinguishing characteristics and one of the more important elements of interpretation. Most commonly, length, width and perimeter are measured. To be able to do this successfully, it is necessary to know the scale of the photo. Measuring the size of an unknown object allows the interpreter to rule out possible alternatives. It has proved to be helpful to measure the size of a few well-known objects to give a comparison to the unknown-object. For example, field dimensions of major sports like soccer, football, and baseball are standard throughout the world. If objects like this are visible in the image, it is possible to determine the size of the unknown object by simply comparing the two.

Shape

There is an infinite number of uniquely shaped natural and man-made objects in the world. A few examples of shape are the triangular shape of modern jet aircraft and the shape of a common single-family dwelling. Humans have modified the landscape in very interesting ways that has given shape to many objects, but nature also shapes the landscape in its own ways. In general, straight, recti-linear features in the environment are of human origin. Nature produces more subtle shapes.

Shadow

Virtually all remotely sensed data is collected within 2 hours of solar noon to avoid extended shadows in the image or photo. This is because shadows can obscure other objects that could otherwise be identified. On the other hand, the shadow cast by an object may be key to the identity of another object. Take for example the Washington Monument in Washington D.C. While viewing this from above it can be difficult to discern the shape of the monument, but with a shadow cast, this process becomes much easier. It is good practice to orient the photos so that the shadows are falling towards the interpreter. A pseudoscopic illusion can be produced if the shadow is oriented away from the observer. This happens when low points appear high and high points appear low.

Tone and color

Real-world materials like vegetation, water and bare soil reflect different proportions of energy in the blue, green, red, and infrared portions of the electro-magnetic spectrum. An interpreter can document the amount of energy reflected from each at specific wavelengths to create a spectral signature. These signatures can help to understand why certain objects appear as they do on black and white or color imagery. These shades of gray are referred to as tone. The darker an object appears, the less light it reflects.
Color imagery is often preferred because, as opposed to shades of gray, humans can detect thousands of different colors. Color aids in the process of photo interpretation.

Texture

This is defined as the “characteristic placement and arrangement of repetitions of tone or color in an image.” Adjectives often used to describe texture are smooth (uniform, homogeneous), intermediate, and rough (coarse, heterogeneous). It is important to remember that texture is a product of scale. On a large scale depiction, objects could appear to have an intermediate texture. But, as the scale becomes smaller, the texture could appear to be more uniform, or smooth. A few examples of texture could be the “smoothness” of a paved road, or the “coarseness” a pine forest.

Pattern

Pattern is the spatial arrangement of objects in the landscape. The objects may be arranged randomly or systematically. They can be natural, as with a drainage pattern of a river, or man-made, as with the squares formed from the United States Public Land Survey System. Typical adjectives used in describing pattern are: random, systematic, circular, oval, linear, rectangular, and curvilinear to name a few.

Height and Depth

Height and depth, also known as “elevation” and “bathymetry”, is one of the most diagnostic elements of image interpretation. This is because any object, such as a building or electric pole that rises above the local landscape will exhibit some sort of radial relief. Also, objects that exhibit this relief will cast a shadow that can also provide information as to its height or elevation. A good example of this would be buildings of any major city.

Site/Situation/Association

Site has unique physical characteristics which might include elevation, slope, and type of surface cover (e.g., grass, forest, water, bare soil). Site can also have socioeconomic characteristics such as the value of land or the closeness to water. Situation refers to how the objects in the photo or image are organized and “situated” in respect to each other. Most power plants have materials and building associated in a fairly predictable manner. Association refers to the fact that when you find a certain activity within a photo or image, you usually encounter related or “associated” features or activities. Site, situation, and association are rarely used independent of each other when analyzing an image. An example of this would be a large shopping mall. Usually there are multiple large buildings, massive parking lots, and it is usually located near a major road or intersection.

References

  • Jensen, John R. Remote Sensing of the Environment, Prentice Hall, 2000
  • Colwell, R.N., Manual of Photographic Interpretation, American Society for Photogrammetry & Remote Sensing, 1997
  • Olson, C.E., Elements of photographic interpretation common to several sensors. Photogrammetric Engineering, 26(4), 651–656, 1960

See also

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WHAT IS AERIAL PHOTO INTERPRETATION ?

AERIAL   PHOTO    INTERPRETATION ?

It is defined as an act of examining photographic images and judging their significance. This includes interpreter and photograph
1. Interpreter:
  • Good vision
  • Well-trained for photo interpreter
  • Sufficient experience of doing the work successfully
  • Good knowledge of locality and type of forest present there
  • Good power of concentration and great deal of patience
2. Photographs: Photographs should be of high quality and free from defects

Elements of aerial photo interpretation

It is done on the basis of following pictorial elements:
  1. Tone: Tone refers to the relative brightness of objects on photographs. On B/W photographs, tone varies from white (1) to black (10) with various shade of grey in between. The tone of an object provides more information than any other single element of object recognition. Young stands are lighter compared to mature stands. The phenological changes such leaf fall, new flush of leaves, flowering and fruiting also affect tone of trees’ spps on APs.
  2. Size: The size of an object image depends upon the object’s size, scale of photograph and resolve power of camera. The minimum size of object to be visible on APs should be about 1/20th minimum. A super highway should not be confused with rural road, a small residence with an apparent building, etc.The size of the crowns and their heights often give a good indication for identification of certain species when other pictorial elements, i.e. tone may not differentiate them from other species.
  3. Shape: Shape refers to the general form or outline of individual objects eg. roads, building, rivers, trees, etc.The shape of a tree crown is important in identification of the species. Most conifers and young broadleaved species have an ovate shaped while those of mature broadleaved species are dome shaped crowns (circular).
  4. Texture: Texture is the degree of coarseness or smoothness of an image and is dependent on shape, size, tone, scale, sun elevation as well as reflection properties of the objects. In forestry, smooth texture is often associated with young trees and coarser texture with older trees. It is more useful in interpretation of larger groups of objects like tree stands. Branching habit and age of trees decide the texture of trees.
  5. Location or site: As different species are found in different places under the influence of locality factors, location or site is helpful in identifying species, egridges and slopes are covered by coniferous in hills whereas, nallahand valleys covered by broadleaved species. In plain, only certain species are found while in hills, other certain species are found, eg. Fir, spruce, chirpineand deodar occur in certain elevation and on certain aspects.
  6. Association: Association refers to occurrence of certain features in relation to others. Some tree species are so closely associated that each helps to confirm the presence of others. Certain tree species can be identified by recognition of other species, which grow together, eg. Khairand Sisooare associated with fresh alluvial deposits in riverainareas.
  7. Shadow: Shadow of objects falling on ground gives an indication of objects. It depends on the time of photography and direction of flight. Shadow of the trees falling on ground help in identification of species as they give an indication of the shape of the crown. Wind, however, affects the shadows and makes identification difficult
  8. Pattern: Pattern refers to the spatial arrangement of objects like orchard, plantation, etc is a characteristics of man made objects. Those can be easily separated from natural objects such as natural forest, ridges, drainage, which have random pattern.It describes the regularity and characteristics arrangement of different shades of tone or texture in a photograph.
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WHAT ARE COLONIAL SOCIAL SERVICES ?

Colonial social services refers to different services provided by the colonialists in their colonies so as to meet their interest.such services include health,education, water supply,electricity,housing e.t.c. These services were provided for the colonialists and Africans who worked for the colonialists.Therefore,the areas which were very potential to the colonialists were provided with better social services.
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