tailieunhanh - Niche Modeling: Predictions From Statistical Distributions - Chapter 3

Mô hình thích hợp thường sử dụng những thông tin không gian về sự phân bố của các loài, vì vậy hệ thống thông tin địa lý (GIS) thường là công cụ của sự lựa chọn khi phải đối mặt với việc quản lý và trình bày số lượng lớn thông tin địa lý. Tuy nhiên, khi sự quan tâm chính là phân tích một lượng vừa phải các dữ liệu, như trong chương trước khi R đã được sử dụng như là một cơ sở dữ liệu quan hệ, R có thể được sử dụng để thực hiện nhiệm. | Chapter 3 Spatial Niche modeling often uses spatial information about the distribution of species so Geographic Information Systems GIS are often the tool of choice when faced with managing and presenting large amounts of geographic information. However when the main interest is analysis of moderate amounts of data as in the previous chapter when R was used as a relational database R can be used to perform simple spatial tasks. This both avoids the need for a separate GIS system when not necessary and helps to build knowledge of advanced use of the R language. On the down side native R operations are not very efficient for spatial operations on large matrices. Vectors and matrices in R the form necessary for mathematical operations use quite a lot of memory thus limiting the size of the data that can be handled. Another approach is to perform basic niche modeling functions on large sets of data with image processing modules. Examples of the image processing package called netpbm performing fundamental analytical operations for modeling are given. Data types The two main types of data representing geographic information are surface features such as temperature and rainfall called raster and linear features such as roads streams or areas represented by polygons. A raster is a regular grid of numbers where each number represents the value of a variable in a regular area or cell. A raster can be represented in R either as a matrix or a vector. The contents of the cells can be integers floating point numbers characters or raw bytes. In Figures and are examples of two ways we might generate a raster to use in analysis by simulation or input from a data file. The first is a 31 2007 by Taylor and Francis Group LLC 32 Niche Modeling palette gray seq 0 len 30 pts - seq -pi pi by z - sin pts 0 1i cos pts points - cbind Re z 20 62 Im z 10 26 t1 - matrix 255 124 52 t1 points - 0 image t1 col 1 30 labels F FIGURE Example of a simple raster to use for .