C#+AE 密度分析

一时失言乱红尘 2023-02-09 11:57 14阅读 0赞

### 文章目录 ###

*  一、密度分析
 *  二、代码实现
 *   *  2.1核密度分析
     *  2.2线密度分析
     *  2.3点密度分析

# 一、密度分析 #

> 密度分析是根据输入的要素数据集计算整个区域的数据聚集状况，从而产生一个连续的密度表面；并通过密度计算，可以将每个采样点的值散布到整个研究区域，并返回输出栅格中每个像元的密度值的栅格操作。

> 密度分析主要有以下几种：  
> 1、核密度分析：用于计算要素(点线均可)在其周围领域中的密度，其密度值是通过对某个搜索区域内的点(或线)采用加权(权重可以是距离、重要程度等等)的方法计算得出的。  
> 2、线密度分析：其计算方法为以各个栅格像元中心为圆心按照某个搜索半径绘制一个圆，每条线上落入该圆内的部分长度与计数字段相乘，并对这些数值进行求和，然后再将所得的总和除以圆面积就可以得到该栅格像元的密度。  
> 3、点密度分析：其计算方法是将每个栅格像元中心的周围都定义了一个邻域，并将邻域内的样本点的计数字段值相加，然后再除以邻域的面积即可。

# 二、代码实现 #

原始数据图像为：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ_size_16_color_FFFFFF_t_70]

## 2.1核密度分析 ##

代码如下：

public partial class DensityAnalysis : DevExpress.XtraEditors.XtraForm
        { 
            public DensityAnalysis()
            { 
                InitializeComponent();
            }
            //全局变量
            public AxMapControl axMapControl {  get; set; }
            private IMap pMap;
    
            private double cellSize;//设置像元大小
            private double radioDis ;//设置搜索半径
            private object Missing = Type.Missing;
            private IRasterNeighborhood rnh;//邻域分析
    
            private object cellSizeObj;
            private object radioDisObj;
            private object extentProviderObj;
    
    
            private IRasterAnalysisEnvironment rasterEnv;//环境分析对象
            private IDensityOp densityOp;//密度分析对象
            private IFeatureClass featureClass;
            private IFeatureClassDescriptor featureDes;
            private IGeoDataset inGeoDataset;//输入数据集
            private IGeoDataset outGeoDataset;//输出数据集
    
            //窗体加载事件
            private void DensityAnalysis_Load(object sender, EventArgs e)
            { 
                pMap = axMapControl.Map;
                if (pMap == null)
                    return;
    
                rasterEnv = new RasterDensityOpClass();
                for (int i = 0; i < pMap.LayerCount; i++)
                { 
                    comboBox_InputDataset.Properties.Items.Add(pMap.Layer[i].Name);
                }
            }
    
            //显示分析结果
            private void ShowResult(IGeoDataset geoDataset, string densityType)
            { 
                IRasterLayer rasterLayer = new RasterLayerClass();
                IRaster raster = new Raster();
                raster = (IRaster)geoDataset;
                rasterLayer.CreateFromRaster(raster);
                rasterLayer.Name = densityType;
    
                axMapControl.AddLayer((ILayer)rasterLayer);
                axMapControl.ActiveView.Refresh();
            }
    
            //通过图层名获取图层
            private ILayer GetLayerByName(IMap pMap, string layerName)
            { 
                ILayer pLayer = null;
                ILayer tempLayer = null;
                for (int i = 0; i < pMap.LayerCount; i++)
                { 
                    tempLayer = pMap.Layer[i];
                    if (tempLayer.Name.ToUpper() == layerName.ToUpper())
                    { 
                        pLayer = tempLayer;
                        break;
                    }
                }
                return pLayer;
            }
    
            //图层选择,并设置分析的范围
            private void comboBox_InputDataset_SelectedIndexChanged(object sender, EventArgs e)
            { 
                try
                { 
                    comboBoxEdit_Field.Properties.Items.Clear();
    
                    ILayer layer = GetLayerByName(pMap,comboBox_InputDataset.SelectedItem.ToString());
                    IFeatureLayer fl = layer as IFeatureLayer;
                    featureClass = fl.FeatureClass;
                    for (int i = 0; i < featureClass.Fields.FieldCount; i++)
                    { 
                        comboBoxEdit_Field.Properties.Items.Add(featureClass.Fields.get_Field(i).Name);
                    }
    
                    extentProviderObj = layer;
                    rasterEnv.SetExtent(esriRasterEnvSettingEnum.esriRasterEnvValue, ref extentProviderObj, Missing);
                }
                catch
                { 
                    MessageBox.Show("请输入点图层或线图层！");
                }
            }
    
            //字段选择
            private void comboBoxEdit_Field_SelectedIndexChanged(object sender, EventArgs e)
            { 
                try
                { 
                    featureDes = new FeatureClassDescriptorClass();
                    featureDes.Create(featureClass, null, comboBoxEdit_Field.SelectedItem.ToString());
    
                    inGeoDataset = featureDes as IGeoDataset;
                }
                catch {  }
            }
    
            //核函数分析
            private void simpleButton_KernalDensity_Click(object sender, EventArgs e)
            { 
                try
                { 
                    //设置输出栅格的大小
                    cellSize = Convert.ToDouble(textEdit_CellSize.Text.Trim());
                    cellSizeObj = cellSize;
                    rasterEnv.SetCellSize(esriRasterEnvSettingEnum.esriRasterEnvValue, ref cellSizeObj);
    
                    radioDis =Convert.ToDouble(textEdit_KernalRadius.Text.Trim());
                    radioDisObj = radioDis;
                    densityOp = rasterEnv as IDensityOp;
                    //核密度分析方法
                    outGeoDataset = densityOp.KernelDensity(inGeoDataset, ref radioDisObj, Missing);//核密度分析方法
                    ShowResult(outGeoDataset, "KernelDensity");
                    SaveResult(outGeoDataset);
                }
                catch(Exception ex)
                { 
                    MessageBox.Show(ex.Message);
                };
            }
    
            //数据保存
            void SaveResult(IGeoDataset geoDataset)
            { 
                string path = textEdit_Output.Text;
    
                string pathName = System.IO.Path.GetDirectoryName(path);
                string fileName = System.IO.Path.GetFileName(path);
    
                IWorkspaceFactory wf = new RasterWorkspaceFactoryClass();
                IWorkspace ws = wf.OpenFromFile(pathName, 0);
    
                IConversionOp conversionOp = new RasterConversionOpClass();
                conversionOp.ToRasterDataset(geoDataset, "IMAGINE Image", ws, fileName);
            }
    
            //保存文件路径
            private void simpleButton_Output_Click(object sender, EventArgs e)
            { 
                SaveFileDialog flg = new SaveFileDialog();
                flg.Title = "保存文件";
                //flg.Filter = "TIFF(.tif)|*.tif";
                flg.Filter = "Image(.img)|*.img";
                flg.ShowDialog();
    
                textEdit_Output.Text = flg.FileName;
            }

实现效果：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ_size_16_color_FFFFFF_t_70 1]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ_size_16_color_FFFFFF_t_70 2]

## 2.2线密度分析 ##

代码如下：

//线密度分析
            private void simpleButton_LineDensity_Click(object sender, EventArgs e)
            { 
                //线密度分析方法
                try
                { 
                    //设置输出栅格的大小
                    cellSize = Convert.ToDouble(textEdit_CellSize.Text.Trim());
                    cellSizeObj = cellSize;
                    rasterEnv.SetCellSize(esriRasterEnvSettingEnum.esriRasterEnvValue, ref cellSizeObj);
    
                    radioDis = Convert.ToDouble(textEdit_LineRadius.Text.Trim());
                    radioDisObj = radioDis;
                    densityOp = rasterEnv as IDensityOp;
                    outGeoDataset = densityOp.LineDensity(inGeoDataset, ref radioDisObj, Missing);
                    ShowResult(outGeoDataset, "LineDensity");
                    SaveResult(outGeoDataset);
                }
                catch(Exception ex)
                { 
                    MessageBox.Show(ex.Message);
                };
            }

实现效果：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ_size_16_color_FFFFFF_t_70 3]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ_size_16_color_FFFFFF_t_70 4]

## 2.3点密度分析 ##

代码如下：

//点密度分析
       private void simpleButton_PointDensity_Click(object sender, EventArgs e)
       { 
           try
           { 
               //设置输出栅格的大小
               cellSize = Convert.ToDouble(textEdit_CellSize.Text.Trim());
               cellSizeObj = cellSize;
               rasterEnv.SetCellSize(esriRasterEnvSettingEnum.esriRasterEnvValue, ref cellSizeObj);
    
               densityOp = rasterEnv as IDensityOp;
               outGeoDataset = densityOp.PointDensity(inGeoDataset, rnh, Missing);//点密度分析方法
               ShowResult(outGeoDataset, "PointDensity");
               SaveResult(outGeoDataset);
           }
           catch {  }
       }
    
    	private void comboBoxEdit_NeighborAnalysis_SelectedIndexChanged(object sender, EventArgs e)
         { 
             try
             { 
                 rnh = new RasterNeighborhoodClass();
                 switch (comboBoxEdit_NeighborAnalysis.SelectedIndex)
                 { 
                     case 0://邻域分析类型为圆形，半径为20像元
                         rnh.SetCircle(20, esriGeoAnalysisUnitsEnum.esriUnitsCells);
                         break;
                     case 1://邻域分析类型为矩形，高为10像元，宽为20像元
                         rnh.SetRectangle(20, 20, esriGeoAnalysisUnitsEnum.esriUnitsCells);
                         break;
                     case 2://邻域分析类型为环形，内半径为10像元，外半径为30像元
                         rnh.SetAnnulus(10, 30, esriGeoAnalysisUnitsEnum.esriUnitsCells);
                         break;
                 }
             }
             catch {  }
         }

实现效果：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ_size_16_color_FFFFFF_t_70 5]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ_size_16_color_FFFFFF_t_70 6]

> 参考资料：《ArcgisEngine地理信息系统开发教程》

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20200518224919421.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ==,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/20200518202504186.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ==,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ_size_16_color_FFFFFF_t_70 2]: https://img-blog.csdnimg.cn/20200518202528235.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ==,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ_size_16_color_FFFFFF_t_70 3]: https://img-blog.csdnimg.cn/20200518211438285.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ==,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ_size_16_color_FFFFFF_t_70 4]: https://img-blog.csdnimg.cn/2020051821150375.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ==,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ_size_16_color_FFFFFF_t_70 5]: https://img-blog.csdnimg.cn/20200518214919858.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ==,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ_size_16_color_FFFFFF_t_70 6]: https://img-blog.csdnimg.cn/20200518214939117.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2RheXVoYWl0YW5nMQ==,size_16,color_FFFFFF,t_70