c***z
发帖数: 6348 | 1 【 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! | d****n
发帖数: 12461 | | T*****u
发帖数: 7103 | | z****e
发帖数: 54598 | | c***z
发帖数: 6348 | 5 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
发帖数: 6348 | 6 Is 100$/hr the rate you guys charge? Actually not bad. :) | c****t
发帖数: 19049 | 7 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
发帖数: 1096 | 8 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
发帖数: 49 | 9 kriging or multiple points kriging | b*****e
发帖数: 853 | 10 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
发帖数: 853 | 11 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. | T*****u
发帖数: 7103 | 12 我的建议是尽量尽量跟多客户谈，听他们的意见，尽量用他们的经验，这种情况下没有
domain knowledge做model木出路。 |
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