【 以下文字转载自 Statistics 讨论区 】 发信人: chaoz (面朝大海,吃碗凉皮), 信区: Statistics 标 题: suggestion on geospatial data? 发信站: BBS 未名空间站 (Mon Jun 30 12:48:25 2014, 美东) Hi all, I am looking at a project related to geospatial data. The sample sizes are small and samples are highly correlated. Can anyone give some suggestion on how to deal with these kind of data? Thanks a lot!
Some background: This project is related to oil and gas drilling, e.g. where to drill and how to drill. I am not a domain expert on it and this is about all the information I have. Also, the data is geospatial, there are few data points and they are highly correlated.
c*z
11 楼
Is 100$/hr the rate you guys charge? Actually not bad. :)
c*t
12 楼
这不是data问题吧。不如去土木工程版上吼一声
how have. highly
【在 c***z 的大作中提到】 : Some background: : This project is related to oil and gas drilling, e.g. where to drill and how : to drill. : I am not a domain expert on it and this is about all the information I have. : Also, the data is geospatial, there are few data points and they are highly : correlated.
l*m
13 楼
first, formulate a supervised learning. then, discuss different models and algorithms.
how have. highly
【在 c***z 的大作中提到】 : Some background: : This project is related to oil and gas drilling, e.g. where to drill and how : to drill. : I am not a domain expert on it and this is about all the information I have. : Also, the data is geospatial, there are few data points and they are highly : correlated.
e*y
14 楼
kriging or multiple points kriging
b*e
15 楼
kriging seems familiar. Google and refresh the knowledge. Interpolates a raster surface from points using kriging. Surface interpolation tools create a continuous (or prediction) surface from sampled point values. Visiting every location in a study area to measure the height, concentration , or magnitude of a phenomenon is usually difficult or expensive. Instead, you can measure the phenomenon at strategically dispersed sample locations, and predicted values can be assigned to all other locations. Input points can be either randomly or regularly spaced or based on a sampling scheme. The continuous surface representation of a raster dataset represents some measure, such as the height, concentration, or magnitude (for example, elevation, acidity, or noise level). Surface interpolation tools make predictions from sample measurements for all locations in an output raster dataset, whether or not a measurement has been taken at the location. There are a variety of ways to derive a prediction for each location; each method is referred to as a model. With each model, there are different assumptions made of the data, and certain models are more applicable for specific data—for example, one model may account for local variation better than another. Each model produces predictions using different calculations. The interpolation tools are generally divided into deterministic and geostatistical methods. The geostatistical methods are based on statistical models that include autocorrelation (the statistical relationship among the measured points). Because of this, geostatistical techniques not only have the capability of producing a prediction surface but also provide some measure of the certainty or accuracy of the predictions. Kriging is a geostatistical method of interpolation.
b*e
16 楼
In GIS (geographic information science), spatial analysis to find best location usually needs more than one data layer. For example, to find gold mine in the mining industry, the GIS Specialist/scientist will consider the geology data, hydrology data, soil data, vegetation data, elevation data ( slope, aspect, etc.), besides the existing location of gold mines.