Java Geotools：捕捉到捕捉到的线识别线
Java Geotools: Snap to line identifiying line that was snapped to
我正在尝试编写一个 Java 程序，它将大量 GPS 坐标捕捉到线形文件（道路网络）并且 return 不仅仅是新坐标, 但捕捉到的线段的唯一标识符。此标识符是 FID、其他语言中使用的 "index"（即 1 是第一个特征等）或属性 table.
中的任何列都无关紧要
我在 R 中使用 maptools::snapPointsToLines 函数完成了此操作，但考虑到我需要处理的数据量，这是不可扩展的，所以我正在寻找 Java 来处理更多数据快速在 R 中进行分析。
我的代码（下方）目前与用于捕捉的 geotools 教程非常相似，我在（1900 万行）CSV GPS 点而不是生成它们时读取了一些细微差别，我写了一个 CSV结果。它捕捉得很好，比我得到的快得多，但我不知道如何识别捕捉到的线。可用的文档似乎涵盖了我无法特别适用于此代码创建的索引行对象的功能集的查询和过滤，以及我代码中的现有功能 toString() returns 一些我无法理解的东西目的，例如 com.vividsolutions.jts.linearreff.LocationIndexedLine@74cec793.
基本上，我只希望 lineID 字段生成任何其他 GIS 软件或语言都可以匹配特定路段的内容。
package org.geotools.tutorial.quickstart;
import java.io.*;
import java.util.List;
import java.util.Arrays;
import com.vividsolutions.jts.geom.Coordinate;
import com.vividsolutions.jts.geom.Envelope;
import com.vividsolutions.jts.geom.Geometry;
import com.vividsolutions.jts.geom.LineString;
import com.vividsolutions.jts.geom.MultiLineString;
import com.vividsolutions.jts.index.SpatialIndex;
import com.vividsolutions.jts.index.strtree.STRtree;
import com.vividsolutions.jts.linearref.LinearLocation;
import com.vividsolutions.jts.linearref.LocationIndexedLine;
import org.geotools.data.FeatureSource;
import org.geotools.data.FileDataStore;
import org.geotools.data.FileDataStoreFinder;
import org.geotools.feature.FeatureCollection;
import org.geotools.geometry.jts.ReferencedEnvelope;
import org.geotools.swing.data.JFileDataStoreChooser;
import org.geotools.util.NullProgressListener;
import org.opengis.feature.Feature;
import org.opengis.feature.FeatureVisitor;
import org.opengis.feature.simple.SimpleFeature;
import com.opencsv.*;
public class SnapToLine {
public static void main(String[] args) throws Exception {
/*
* Open a shapefile. You should choose one with line features
* (LineString or MultiLineString geometry)
*
*/
File file = JFileDataStoreChooser.showOpenFile("shp", null);
if (file == null) {
return;
}
FileDataStore store = FileDataStoreFinder.getDataStore(file);
FeatureSource source = store.getFeatureSource();
// Check that we have line features
Class<?> geomBinding = source.getSchema().getGeometryDescriptor().getType().getBinding();
boolean isLine = geomBinding != null
&& (LineString.class.isAssignableFrom(geomBinding) ||
MultiLineString.class.isAssignableFrom(geomBinding));
if (!isLine) {
System.out.println("This example needs a shapefile with line features");
return;
}
final SpatialIndex index = new STRtree();
FeatureCollection features = source.getFeatures();
//FeatureCollection featurecollection = source.getFeatures(Query.FIDS);
System.out.println("Slurping in features ...");
features.accepts(new FeatureVisitor() {
@Override
public void visit(Feature feature) {
SimpleFeature simpleFeature = (SimpleFeature) feature;
Geometry geom = (MultiLineString) simpleFeature.getDefaultGeometry();
// Just in case: check for null or empty geometry
if (geom != null) {
Envelope env = geom.getEnvelopeInternal();
if (!env.isNull()) {
index.insert(env, new LocationIndexedLine(geom));
}
}
}
}, new NullProgressListener());
/*
/*
* We defined the maximum distance that a line can be from a point
* to be a candidate for snapping
*/
ReferencedEnvelope bounds = features.getBounds();
final double MAX_SEARCH_DISTANCE = bounds.getSpan(0) / 1000.0;
int pointsProcessed = 0;
int pointsSnapped = 0;
long elapsedTime = 0;
long startTime = System.currentTimeMillis();
double longiOut;
double latiOut;
int moved;
String lineID = "NA";
//Open up the CSVReader. Reading in line by line to avoid memory failure.
CSVReader csvReader = new CSVReader(new FileReader(new File("fakedata.csv")));
String[] rowIn;
//open up the CSVwriter
String outcsv = "fakedataOUT.csv";
CSVWriter writer = new CSVWriter(new FileWriter(outcsv));
while ((rowIn = csvReader.readNext()) != null) {
// Get point and create search envelope
pointsProcessed++;
double longi = Double.parseDouble(rowIn[0]);
double lati = Double.parseDouble(rowIn[1]);
Coordinate pt = new Coordinate(longi, lati);
Envelope search = new Envelope(pt);
search.expandBy(MAX_SEARCH_DISTANCE);
/*
* Query the spatial index for objects within the search envelope.
* Note that this just compares the point envelope to the line envelopes
* so it is possible that the point is actually more distant than
* MAX_SEARCH_DISTANCE from a line.
*/
List<LocationIndexedLine> lines = index.query(search);
// Initialize the minimum distance found to our maximum acceptable
// distance plus a little bit
double minDist = MAX_SEARCH_DISTANCE + 1.0e-6;
Coordinate minDistPoint = null;
for (LocationIndexedLine line : lines) {
LinearLocation here = line.project(pt);
Coordinate point = line.extractPoint(here);
double dist = point.distance(pt);
if (dist < minDist) {
minDist = dist;
minDistPoint = point;
lineID = line.toString();
}
}
if (minDistPoint == null) {
// No line close enough to snap the point to
System.out.println(pt + "- X");
longiOut = longi;
latiOut = lati;
moved = 0;
lineID = "NA";
} else {
System.out.printf("%s - snapped by moving %.4f\n",
pt.toString(), minDist);
longiOut = minDistPoint.x;
latiOut = minDistPoint.y;
moved = 1;
pointsSnapped++;
}
//write a new row
String [] rowOut = {Double.toString(longiOut), Double.toString(latiOut), Integer.toString(moved), lineID};
writer.writeNext(rowOut);
}
System.out.printf("Processed %d points (%.2f points per second). \n"
+ "Snapped %d points.\n\n",
pointsProcessed,
1000.0 * pointsProcessed / elapsedTime,
pointsSnapped);
writer.close();
}
}
我不仅是 Java 的新手，而且只接受过领域特定语言（如 R）的自我培训；我不是编码员，而是使用代码的人，所以如果解决方案看起来很明显，我可能缺乏基本理论！
p.s 我知道有更好的地图匹配解决方案（graphhopper 等），我只是想从简单开始！
谢谢！
我会尽量避免在 JTS 兔子洞中走得太远，并坚持使用 GeoTools（当然，我是 GeoTools 开发人员，所以我会这么说）。
首先，我会使用 SpatialIndexFeatureCollection 来保存我的线条（假设它们适合内存，否则 PostGIS table 是可行的方法）。这使我不必建立自己的索引。
然后我会使用 CSVDataStore 来保存从 GPS 流中解析我自己的点（因为我很懒，那里也有很多错误）。
这意味着大部分工作归结为这个循环，DWITHIN 找到指定距离内的所有特征：
try (SimpleFeatureIterator itr = pointFeatures.getFeatures().features()) {
while (itr.hasNext()) {
SimpleFeature f = itr.next();
Geometry snapee = (Geometry) f.getDefaultGeometry();
Filter filter = ECQL.toFilter("DWITH(\"the_geom\",'" + writer.write(snapee) + "'," + MAX_SEARCH_DISTANCE + ")");
SimpleFeatureCollection possibles = indexed.subCollection(filter);
double minDist = Double.POSITIVE_INFINITY;
SimpleFeature bestFit = null;
Coordinate bestPoint = null;
try (SimpleFeatureIterator pItr = possibles.features()) {
while (pItr.hasNext()) {
SimpleFeature p = pItr.next();
Geometry line = (Geometry) p.getDefaultGeometry();
double dist = snapee.distance(line);
if (dist < minDist) {
minDist = dist;
bestPoint = DistanceOp.nearestPoints(snapee, line)[1];
bestFit = p;
}
}
}
在该循环结束时，您应该知道线（包括它的 id 和名称等）中最近的特征 (bestFit)、最近的点 (bestPoint) 和移动的距离 (minDist)。
同样，我可能会使用 CSVDatastore 将功能写回。
如果您有数百万个点，我可能会考虑使用 FilterFactory 直接创建过滤器，而不是使用 ECQL 解析器。
根据 iant 在已接受的答案中提供的代码，我已将我的代码更改如下，我 post 用于谷歌搜索此类问题的其他人。
两件事：确保 ECQL 解析器中有一个单元（我还没有尝试 FilterFactory 建议）。 CSVDataStore 包含在以下 link 中。请注意，默认情况下，经度和纬度分别硬编码为 lon 和 lat。 http://docs.geotools.org/latest/userguide/tutorial/datastore/intro.html
package org.geotools.tutorial.quickstart;
import java.io.*;
import java.util.List;
import java.util.Arrays;
import com.vividsolutions.jts.geom.Coordinate;
import com.vividsolutions.jts.geom.Envelope;
import com.vividsolutions.jts.geom.Geometry;
import com.vividsolutions.jts.geom.LineString;
import com.vividsolutions.jts.geom.MultiLineString;
import com.vividsolutions.jts.index.SpatialIndex;
import com.vividsolutions.jts.index.strtree.STRtree;
import com.vividsolutions.jts.linearref.LinearLocation;
import com.vividsolutions.jts.linearref.LocationIndexedLine;
import com.vividsolutions.jts.operation.distance.DistanceOp;
import com.vividsolutions.jts.io.WKTWriter;
import org.geotools.data.FeatureSource;
import org.geotools.data.simple.SimpleFeatureSource;
import org.geotools.data.simple.SimpleFeatureCollection;
import org.geotools.data.simple.SimpleFeatureIterator;
import org.geotools.data.FileDataStore;
import org.geotools.data.FileDataStoreFinder;
import org.geotools.feature.FeatureCollection;
import org.geotools.geometry.jts.ReferencedEnvelope;
import org.geotools.swing.data.JFileDataStoreChooser;
import org.geotools.util.NullProgressListener;
import org.geotools.data.collection.SpatialIndexFeatureCollection;
import org.geotools.data.collection.SpatialIndexFeatureSource;
import org.geotools.filter.text.ecql.ECQL;
import org.opengis.filter.Filter;
import org.opengis.feature.Feature;
import org.opengis.feature.FeatureVisitor;
import org.opengis.feature.simple.SimpleFeature;
import com.opencsv.*;
import com.csvreader.CsvReader;
public class SnapToLine {
public static void main(String[] args) throws Exception {
//input and output files and other parameters
String inputpoints = "/home/bitre/fakedata.csv";
String outcsv = "fakedataOUT.csv";
final double MAX_SEARCH_DISTANCE = 0.5;
/*
* Open a shapefile. You should choose one with line features
* (LineString or MultiLineString geometry)
*
*/
File file = JFileDataStoreChooser.showOpenFile("shp", null);
if (file == null) {
return;
}
FileDataStore store = FileDataStoreFinder.getDataStore(file);
SimpleFeatureSource source = store.getFeatureSource();
// Check that we have line features
Class<?> geomBinding = source.getSchema().getGeometryDescriptor().getType().getBinding();
boolean isLine = geomBinding != null
&& (LineString.class.isAssignableFrom(geomBinding) ||
MultiLineString.class.isAssignableFrom(geomBinding));
if (!isLine) {
System.out.println("This example needs a shapefile with line features");
return;
}
SimpleFeatureCollection features = source.getFeatures();
SpatialIndexFeatureCollection indexed = new SpatialIndexFeatureCollection(features);
/*
/*
* We defined the maximum distance that a line can be from a point
* to be a candidate for snapping
*/
ReferencedEnvelope bounds = features.getBounds();
//open up the CSVwriter
CSVWriter csvWriter = new CSVWriter(new FileWriter(outcsv));
//CSVDataStore features for the points
CSVDataStore pointFeaturesCSV = new CSVDataStore(new File(inputpoints));
String typeName = pointFeaturesCSV.getTypeNames()[0];
SimpleFeatureSource pointFeatures = pointFeaturesCSV.getFeatureSource(typeName);
double longiOut;
double latiOut;
int progress = 0;
int remn;
String[] rowOut = new String[4];
try (SimpleFeatureIterator itr = pointFeatures.getFeatures().features()) {
while (itr.hasNext()) {
SimpleFeature f = itr.next();
Geometry snapee = (Geometry) f.getDefaultGeometry();
WKTWriter writer = new WKTWriter();
Filter filter = ECQL.toFilter("DWITHIN(\"the_geom\",'" + writer.write(snapee) + "'," + MAX_SEARCH_DISTANCE + "," + "kilometers" + ")");
SimpleFeatureCollection possibles = indexed.subCollection(filter);
double minDist = Double.POSITIVE_INFINITY;
SimpleFeature bestFit = null;
Coordinate bestPoint = null;
try (SimpleFeatureIterator pItr = possibles.features()) {
while (pItr.hasNext()) {
SimpleFeature p = pItr.next();
Geometry line = (Geometry) p.getDefaultGeometry();
double dist = snapee.distance(line);
if (dist < minDist) {
minDist = dist;
bestPoint = DistanceOp.nearestPoints(snapee, line)[1]; // google DistanceOp
bestFit = p;
}
longiOut = bestPoint.x;
latiOut = bestPoint.y;
rowOut[0] = bestFit.getID();
rowOut[1] = Double.toString(minDist);
rowOut[2] = Double.toString(longiOut);
rowOut[3] = Double.toString(latiOut);
//rowOut = {bestFit.getID(), Double.toString(minDist), Double.toString(longiOut), Double.toString(latiOut)};
}
csvWriter.writeNext(rowOut);
progress ++;
remn = progress % 1000000;
if(remn == 0){
System.out.println("Just snapped line" + progress);
}
}
}
}
}
}
我正在尝试编写一个 Java 程序，它将大量 GPS 坐标捕捉到线形文件（道路网络）并且 return 不仅仅是新坐标, 但捕捉到的线段的唯一标识符。此标识符是 FID、其他语言中使用的 "index"（即 1 是第一个特征等）或属性 table.
中的任何列都无关紧要我在 R 中使用 maptools::snapPointsToLines 函数完成了此操作，但考虑到我需要处理的数据量，这是不可扩展的，所以我正在寻找 Java 来处理更多数据快速在 R 中进行分析。
我的代码（下方）目前与用于捕捉的 geotools 教程非常相似，我在（1900 万行）CSV GPS 点而不是生成它们时读取了一些细微差别，我写了一个 CSV结果。它捕捉得很好，比我得到的快得多，但我不知道如何识别捕捉到的线。可用的文档似乎涵盖了我无法特别适用于此代码创建的索引行对象的功能集的查询和过滤，以及我代码中的现有功能 toString() returns 一些我无法理解的东西目的，例如 com.vividsolutions.jts.linearreff.LocationIndexedLine@74cec793.
基本上，我只希望 lineID 字段生成任何其他 GIS 软件或语言都可以匹配特定路段的内容。
package org.geotools.tutorial.quickstart;
import java.io.*;
import java.util.List;
import java.util.Arrays;
import com.vividsolutions.jts.geom.Coordinate;
import com.vividsolutions.jts.geom.Envelope;
import com.vividsolutions.jts.geom.Geometry;
import com.vividsolutions.jts.geom.LineString;
import com.vividsolutions.jts.geom.MultiLineString;
import com.vividsolutions.jts.index.SpatialIndex;
import com.vividsolutions.jts.index.strtree.STRtree;
import com.vividsolutions.jts.linearref.LinearLocation;
import com.vividsolutions.jts.linearref.LocationIndexedLine;
import org.geotools.data.FeatureSource;
import org.geotools.data.FileDataStore;
import org.geotools.data.FileDataStoreFinder;
import org.geotools.feature.FeatureCollection;
import org.geotools.geometry.jts.ReferencedEnvelope;
import org.geotools.swing.data.JFileDataStoreChooser;
import org.geotools.util.NullProgressListener;
import org.opengis.feature.Feature;
import org.opengis.feature.FeatureVisitor;
import org.opengis.feature.simple.SimpleFeature;
import com.opencsv.*;
public class SnapToLine {
public static void main(String[] args) throws Exception {
/*
* Open a shapefile. You should choose one with line features
* (LineString or MultiLineString geometry)
*
*/
File file = JFileDataStoreChooser.showOpenFile("shp", null);
if (file == null) {
return;
}
FileDataStore store = FileDataStoreFinder.getDataStore(file);
FeatureSource source = store.getFeatureSource();
// Check that we have line features
Class<?> geomBinding = source.getSchema().getGeometryDescriptor().getType().getBinding();
boolean isLine = geomBinding != null
&& (LineString.class.isAssignableFrom(geomBinding) ||
MultiLineString.class.isAssignableFrom(geomBinding));
if (!isLine) {
System.out.println("This example needs a shapefile with line features");
return;
}
final SpatialIndex index = new STRtree();
FeatureCollection features = source.getFeatures();
//FeatureCollection featurecollection = source.getFeatures(Query.FIDS);
System.out.println("Slurping in features ...");
features.accepts(new FeatureVisitor() {
@Override
public void visit(Feature feature) {
SimpleFeature simpleFeature = (SimpleFeature) feature;
Geometry geom = (MultiLineString) simpleFeature.getDefaultGeometry();
// Just in case: check for null or empty geometry
if (geom != null) {
Envelope env = geom.getEnvelopeInternal();
if (!env.isNull()) {
index.insert(env, new LocationIndexedLine(geom));
}
}
}
}, new NullProgressListener());
/*
/*
* We defined the maximum distance that a line can be from a point
* to be a candidate for snapping
*/
ReferencedEnvelope bounds = features.getBounds();
final double MAX_SEARCH_DISTANCE = bounds.getSpan(0) / 1000.0;
int pointsProcessed = 0;
int pointsSnapped = 0;
long elapsedTime = 0;
long startTime = System.currentTimeMillis();
double longiOut;
double latiOut;
int moved;
String lineID = "NA";
//Open up the CSVReader. Reading in line by line to avoid memory failure.
CSVReader csvReader = new CSVReader(new FileReader(new File("fakedata.csv")));
String[] rowIn;
//open up the CSVwriter
String outcsv = "fakedataOUT.csv";
CSVWriter writer = new CSVWriter(new FileWriter(outcsv));
while ((rowIn = csvReader.readNext()) != null) {
// Get point and create search envelope
pointsProcessed++;
double longi = Double.parseDouble(rowIn[0]);
double lati = Double.parseDouble(rowIn[1]);
Coordinate pt = new Coordinate(longi, lati);
Envelope search = new Envelope(pt);
search.expandBy(MAX_SEARCH_DISTANCE);
/*
* Query the spatial index for objects within the search envelope.
* Note that this just compares the point envelope to the line envelopes
* so it is possible that the point is actually more distant than
* MAX_SEARCH_DISTANCE from a line.
*/
List<LocationIndexedLine> lines = index.query(search);
// Initialize the minimum distance found to our maximum acceptable
// distance plus a little bit
double minDist = MAX_SEARCH_DISTANCE + 1.0e-6;
Coordinate minDistPoint = null;
for (LocationIndexedLine line : lines) {
LinearLocation here = line.project(pt);
Coordinate point = line.extractPoint(here);
double dist = point.distance(pt);
if (dist < minDist) {
minDist = dist;
minDistPoint = point;
lineID = line.toString();
}
}
if (minDistPoint == null) {
// No line close enough to snap the point to
System.out.println(pt + "- X");
longiOut = longi;
latiOut = lati;
moved = 0;
lineID = "NA";
} else {
System.out.printf("%s - snapped by moving %.4f\n",
pt.toString(), minDist);
longiOut = minDistPoint.x;
latiOut = minDistPoint.y;
moved = 1;
pointsSnapped++;
}
//write a new row
String [] rowOut = {Double.toString(longiOut), Double.toString(latiOut), Integer.toString(moved), lineID};
writer.writeNext(rowOut);
}
System.out.printf("Processed %d points (%.2f points per second). \n"
+ "Snapped %d points.\n\n",
pointsProcessed,
1000.0 * pointsProcessed / elapsedTime,
pointsSnapped);
writer.close();
}
}
我不仅是 Java 的新手，而且只接受过领域特定语言（如 R）的自我培训；我不是编码员，而是使用代码的人，所以如果解决方案看起来很明显，我可能缺乏基本理论！
p.s 我知道有更好的地图匹配解决方案（graphhopper 等），我只是想从简单开始！
谢谢！
我会尽量避免在 JTS 兔子洞中走得太远，并坚持使用 GeoTools（当然，我是 GeoTools 开发人员，所以我会这么说）。
首先，我会使用 SpatialIndexFeatureCollection 来保存我的线条（假设它们适合内存，否则 PostGIS table 是可行的方法）。这使我不必建立自己的索引。
然后我会使用 CSVDataStore 来保存从 GPS 流中解析我自己的点（因为我很懒，那里也有很多错误）。
这意味着大部分工作归结为这个循环，DWITHIN 找到指定距离内的所有特征：
try (SimpleFeatureIterator itr = pointFeatures.getFeatures().features()) {
while (itr.hasNext()) {
SimpleFeature f = itr.next();
Geometry snapee = (Geometry) f.getDefaultGeometry();
Filter filter = ECQL.toFilter("DWITH(\"the_geom\",'" + writer.write(snapee) + "'," + MAX_SEARCH_DISTANCE + ")");
SimpleFeatureCollection possibles = indexed.subCollection(filter);
double minDist = Double.POSITIVE_INFINITY;
SimpleFeature bestFit = null;
Coordinate bestPoint = null;
try (SimpleFeatureIterator pItr = possibles.features()) {
while (pItr.hasNext()) {
SimpleFeature p = pItr.next();
Geometry line = (Geometry) p.getDefaultGeometry();
double dist = snapee.distance(line);
if (dist < minDist) {
minDist = dist;
bestPoint = DistanceOp.nearestPoints(snapee, line)[1];
bestFit = p;
}
}
}
在该循环结束时，您应该知道线（包括它的 id 和名称等）中最近的特征 (bestFit)、最近的点 (bestPoint) 和移动的距离 (minDist)。
同样，我可能会使用 CSVDatastore 将功能写回。
如果您有数百万个点，我可能会考虑使用 FilterFactory 直接创建过滤器，而不是使用 ECQL 解析器。
根据 iant 在已接受的答案中提供的代码，我已将我的代码更改如下，我 post 用于谷歌搜索此类问题的其他人。
两件事：确保 ECQL 解析器中有一个单元（我还没有尝试 FilterFactory 建议）。 CSVDataStore 包含在以下 link 中。请注意，默认情况下，经度和纬度分别硬编码为 lon 和 lat。 http://docs.geotools.org/latest/userguide/tutorial/datastore/intro.html
package org.geotools.tutorial.quickstart;
import java.io.*;
import java.util.List;
import java.util.Arrays;
import com.vividsolutions.jts.geom.Coordinate;
import com.vividsolutions.jts.geom.Envelope;
import com.vividsolutions.jts.geom.Geometry;
import com.vividsolutions.jts.geom.LineString;
import com.vividsolutions.jts.geom.MultiLineString;
import com.vividsolutions.jts.index.SpatialIndex;
import com.vividsolutions.jts.index.strtree.STRtree;
import com.vividsolutions.jts.linearref.LinearLocation;
import com.vividsolutions.jts.linearref.LocationIndexedLine;
import com.vividsolutions.jts.operation.distance.DistanceOp;
import com.vividsolutions.jts.io.WKTWriter;
import org.geotools.data.FeatureSource;
import org.geotools.data.simple.SimpleFeatureSource;
import org.geotools.data.simple.SimpleFeatureCollection;
import org.geotools.data.simple.SimpleFeatureIterator;
import org.geotools.data.FileDataStore;
import org.geotools.data.FileDataStoreFinder;
import org.geotools.feature.FeatureCollection;
import org.geotools.geometry.jts.ReferencedEnvelope;
import org.geotools.swing.data.JFileDataStoreChooser;
import org.geotools.util.NullProgressListener;
import org.geotools.data.collection.SpatialIndexFeatureCollection;
import org.geotools.data.collection.SpatialIndexFeatureSource;
import org.geotools.filter.text.ecql.ECQL;
import org.opengis.filter.Filter;
import org.opengis.feature.Feature;
import org.opengis.feature.FeatureVisitor;
import org.opengis.feature.simple.SimpleFeature;
import com.opencsv.*;
import com.csvreader.CsvReader;
public class SnapToLine {
public static void main(String[] args) throws Exception {
//input and output files and other parameters
String inputpoints = "/home/bitre/fakedata.csv";
String outcsv = "fakedataOUT.csv";
final double MAX_SEARCH_DISTANCE = 0.5;
/*
* Open a shapefile. You should choose one with line features
* (LineString or MultiLineString geometry)
*
*/
File file = JFileDataStoreChooser.showOpenFile("shp", null);
if (file == null) {
return;
}
FileDataStore store = FileDataStoreFinder.getDataStore(file);
SimpleFeatureSource source = store.getFeatureSource();
// Check that we have line features
Class<?> geomBinding = source.getSchema().getGeometryDescriptor().getType().getBinding();
boolean isLine = geomBinding != null
&& (LineString.class.isAssignableFrom(geomBinding) ||
MultiLineString.class.isAssignableFrom(geomBinding));
if (!isLine) {
System.out.println("This example needs a shapefile with line features");
return;
}
SimpleFeatureCollection features = source.getFeatures();
SpatialIndexFeatureCollection indexed = new SpatialIndexFeatureCollection(features);
/*
/*
* We defined the maximum distance that a line can be from a point
* to be a candidate for snapping
*/
ReferencedEnvelope bounds = features.getBounds();
//open up the CSVwriter
CSVWriter csvWriter = new CSVWriter(new FileWriter(outcsv));
//CSVDataStore features for the points
CSVDataStore pointFeaturesCSV = new CSVDataStore(new File(inputpoints));
String typeName = pointFeaturesCSV.getTypeNames()[0];
SimpleFeatureSource pointFeatures = pointFeaturesCSV.getFeatureSource(typeName);
double longiOut;
double latiOut;
int progress = 0;
int remn;
String[] rowOut = new String[4];
try (SimpleFeatureIterator itr = pointFeatures.getFeatures().features()) {
while (itr.hasNext()) {
SimpleFeature f = itr.next();
Geometry snapee = (Geometry) f.getDefaultGeometry();
WKTWriter writer = new WKTWriter();
Filter filter = ECQL.toFilter("DWITHIN(\"the_geom\",'" + writer.write(snapee) + "'," + MAX_SEARCH_DISTANCE + "," + "kilometers" + ")");
SimpleFeatureCollection possibles = indexed.subCollection(filter);
double minDist = Double.POSITIVE_INFINITY;
SimpleFeature bestFit = null;
Coordinate bestPoint = null;
try (SimpleFeatureIterator pItr = possibles.features()) {
while (pItr.hasNext()) {
SimpleFeature p = pItr.next();
Geometry line = (Geometry) p.getDefaultGeometry();
double dist = snapee.distance(line);
if (dist < minDist) {
minDist = dist;
bestPoint = DistanceOp.nearestPoints(snapee, line)[1]; // google DistanceOp
bestFit = p;
}
longiOut = bestPoint.x;
latiOut = bestPoint.y;
rowOut[0] = bestFit.getID();
rowOut[1] = Double.toString(minDist);
rowOut[2] = Double.toString(longiOut);
rowOut[3] = Double.toString(latiOut);
//rowOut = {bestFit.getID(), Double.toString(minDist), Double.toString(longiOut), Double.toString(latiOut)};
}
csvWriter.writeNext(rowOut);
progress ++;
remn = progress % 1000000;
if(remn == 0){
System.out.println("Just snapped line" + progress);
}
}
}
}
}
}