|
Lee's L
Lee's ''L'' is a bivariate spatial correlation coefficient which measures the association between two sets of observations made at the same spatial sites. Standard measures of association such as the Pearson correlation coefficient do not account for the spatial dimension of data, in particular they are vulnerable to inflation due to spatial autocorrelation. Lee's ''L'' is available in numerous spatial analysis software libraries including ''spdep'' and ''PySAL'' (where it is called ''Spatial_Pearson'') and has been applied in diverse applications such as studying air pollution, viticulture and housing rent. Formula For spatial data x_i and y_i measured at N locations connected with the spatial weight matrix w_ first define the spatially lagged vector :\tilde_i = \sum_j w_ x_j with a similar definition for \tilde_i. Then Lee's ''L'' is defined as : L_ = \frac \frac where \bar, \bar are the mean values of x_i, y_i. When the spatial weight matrix is row normalized, such th ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Correlation Coefficient
A correlation coefficient is a numerical measure of some type of linear correlation, meaning a statistical relationship between two variables. The variables may be two columns of a given data set of observations, often called a sample, or two components of a multivariate random variable with a known distribution. Several types of correlation coefficient exist, each with their own definition and own range of usability and characteristics. They all assume values in the range from −1 to +1, where ±1 indicates the strongest possible correlation and 0 indicates no correlation. As tools of analysis, correlation coefficients present certain problems, including the propensity of some types to be distorted by outliers and the possibility of incorrectly being used to infer a causal relationship between the variables (for more, see Correlation does not imply causation). Types There are several different measures for the degree of correlation in data, depending on the kind of data: ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Correlation
In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are '' linearly'' related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve. Correlations are useful because they can indicate a predictive relationship that can be exploited in practice. For example, an electrical utility may produce less power on a mild day based on the correlation between electricity demand and weather. In this example, there is a causal relationship, because extreme weather causes people to use more electricity for heating or cooling. However, in g ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pearson Correlation Coefficient
In statistics, the Pearson correlation coefficient (PCC) is a correlation coefficient that measures linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviations; thus, it is essentially a normalized measurement of the covariance, such that the result always has a value between −1 and 1. As with covariance itself, the measure can only reflect a linear correlation of variables, and ignores many other types of relationships or correlations. As a simple example, one would expect the age and height of a sample of children from a school to have a Pearson correlation coefficient significantly greater than 0, but less than 1 (as 1 would represent an unrealistically perfect correlation). Naming and history It was developed by Karl Pearson from a related idea introduced by Francis Galton in the 1880s, and for which the mathematical formula was derived and published by Auguste Bravais in 1844. The nami ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Autocorrelation
Autocorrelation, sometimes known as serial correlation in the discrete time case, measures the correlation of a signal with a delayed copy of itself. Essentially, it quantifies the similarity between observations of a random variable at different points in time. The analysis of autocorrelation is a mathematical tool for identifying repeating patterns or hidden periodicities within a signal obscured by noise. Autocorrelation is widely used in signal processing, time domain and time series analysis to understand the behavior of data over time. Different fields of study define autocorrelation differently, and not all of these definitions are equivalent. In some fields, the term is used interchangeably with autocovariance. Various time series models incorporate autocorrelation, such as unit root processes, trend-stationary processes, autoregressive processes, and moving average processes. Autocorrelation of stochastic processes In statistics, the autocorrelation of a real ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Spatial Analysis
Spatial analysis is any of the formal Scientific technique, techniques which study entities using their topological, geometric, or geographic properties, primarily used in Urban design, Urban Design. Spatial analysis includes a variety of techniques using different analytic approaches, especially ''spatial statistics''. It may be applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, or to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures. In a more restricted sense, spatial analysis is geospatial analysis, the technique applied to structures at the human scale, most notably in the analysis of geographic data. It may also applied to genomics, as in Spatial transcriptomics, transcriptomics data, but is primarily for spatial data. Complex issues arise in spatial analysis, many of which are neither clearly defined nor completely resolved, but form the basis for current resear ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Air Pollution
Air pollution is the presence of substances in the Atmosphere of Earth, air that are harmful to humans, other living beings or the environment. Pollutants can be Gas, gases like Ground-level ozone, ozone or nitrogen oxides or small particles like soot and dust. It affects both outdoor air and indoor air. Natural sources of air pollution include Wildfire, wildfires, Dust storm, dust storms, and Volcanic eruption, volcanic eruptions. Indoor air pollution is often Energy poverty and cooking, caused by the use of biomass (e.g. wood) for cooking and heating. Outdoor air pollution comes from some industrial processes, the burning of Fossil fuel, fossil fuels for electricity and transport, waste management and agriculture. Many of the contributors of local air pollution, especially the burning of fossil fuels, also cause greenhouse gas emissions that cause climate change, global warming. Air pollution causes around 7 or 8 million deaths each year. It is a significant risk factor for ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Viticulture
Viticulture (, "vine-growing"), viniculture (, "wine-growing"), or winegrowing is the cultivation and harvesting of grapes. It is a branch of the science of horticulture. While the native territory of ''Vitis vinifera'', the common grape vine, ranges from Western Europe to the Persian shores of the Caspian Sea, the vine has demonstrated high levels of adaptability to new environments, hence viticulture can be found on every continent except Antarctica. The duties of a viticulturist include monitoring and controlling pests and diseases, fertilizing, irrigation, canopy management, monitoring fruit development and characteristics, deciding when to harvest, and vine pruning during the winter months. Viticulturists are often intimately involved with winemakers, because vineyard management and the resulting grape characteristics provide the basis from which winemaking can begin. A great number of varieties are now approved in the European Union as true grapes for winegrowin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
