% 读取经纬度数据 data1 = readmatrix('2017前.csv'); lon1 = data1(:, 1); lat1 = data1(:, 2); data2 = readmatrix('2017后.csv'); lon2 = data2(:, 1); lat2 = data2(:, 2); % 将经纬度转换为弧度制 lon1_rad = deg2rad(lon1); lat1_rad = deg2rad(lat1); lon2_rad = deg2rad(lon2); lat2_rad = deg2rad(lat2); % 计算距离矩阵 dist_mat = pdist2([lat1_rad, lon1_rad], [lat2_rad, lon2_rad], @haversine); % 设定距离阈值 threshold = 0.1; % 单位为弧度 % 找到距离小于阈值的坐标点对 [row, col] = find(dist_mat < threshold & dist_mat > 0); % 输出相近坐标点对 for i = 1:length(row) fprintf('Coordinate %d in group 1 is close to coordinate %d in group 2\n', row(i), col(i)); end

这段代码是一个MATLAB脚本，用于读取两个经纬度数据文件（2017前.csv和2017后.csv），将经纬度转换为弧度制，计算距离矩阵，并找到距离小于阈值的坐标点对。

具体来说，代码首先使用readmatrix函数读取经纬度数据文件，然后将经纬度转换为弧度制，这是因为计算距离需要使用球面三角学公式，而该公式中的经纬度单位为弧度。接着，代码使用pdist2函数计算距离矩阵，该函数可以计算两个矩阵中所有点之间的距离，这里用它来计算两组经纬度之间的距离矩阵。最后，代码设定距离阈值threshold，找到距离小于阈值的坐标点对，并输出相近坐标点对的信息。

需要注意的是，该代码使用了haversine函数来计算两个经纬度之间的距离，该函数是一个自定义函数，用于计算球面距离，具体实现可以参考以下代码：

function [dist] = haversine(latlon1, latlon2)
% HAVERSINE  Compute distance between two points on a sphere
%
%   DIST = HAVERSINE(LATLON1, LATLON2) returns the great-circle
%   distance between two points on a sphere in kilometers. LATLON1
%   and LATLON2 are vectors of the form [LATITUDE, LONGITUDE],
%   where LATITUDE and LONGITUDE are given in degrees.
%
%   This implementation uses the Haversine formula. Other methods
%   may be more accurate for small distances or for distances
%   between points near the poles.

% Radius of the Earth in km
R = 6371;

% Convert latitude and longitude to radians
lat1 = latlon1(1);
lon1 = latlon1(2);
lat2 = latlon2(1);
lon2 = latlon2(2);
lat1_rad = deg2rad(lat1);
lon1_rad = deg2rad(lon1);
lat2_rad = deg2rad(lat2);
lon2_rad = deg2rad(lon2);

% Compute Haversine formula
delta_lat = lat2_rad - lat1_rad;
delta_lon = lon2_rad - lon1_rad;
a = sin(delta_lat/2)^2 + cos(lat1_rad)*cos(lat2_rad)*sin(delta_lon/2)^2;
c = 2*atan2(sqrt(a), sqrt(1-a));
dist = R*c;
end

该函数接受两个经纬度向量，计算它们之间的球面距离，并返回距离值。