WO2016141753A1 - 基于路网和兴趣点的声环境功能区划分方法 - Google Patents

基于路网和兴趣点的声环境功能区划分方法 Download PDF

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WO2016141753A1
WO2016141753A1 PCT/CN2015/098758 CN2015098758W WO2016141753A1 WO 2016141753 A1 WO2016141753 A1 WO 2016141753A1 CN 2015098758 W CN2015098758 W CN 2015098758W WO 2016141753 A1 WO2016141753 A1 WO 2016141753A1
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蔡铭
陈韩杰
周展鸿
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中山大学
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  • the invention relates to a method for dividing a sound environment functional area in a noise environment assessment, and more particularly to a method for dividing a sound environment functional area based on a road network and a point of interest.
  • Noise pollution is one of the main sources of pollution in urban environments. Noise pollution and acoustic environmental quality need to be evaluated and managed, and acoustic environmental functional zone division is the basis for urban acoustic environmental quality assessment and management.
  • the division of acoustic environment functional area is divided into different acoustic functional area categories according to the functional characteristics and environmental quality requirements of the area, and the noise emission limits of each area are defined to prevent environmental noise pollution, protect and improve life. surroundings.
  • the national standard GB3096-08 "Acoustic Environmental Quality Standard" clarifies that there are five categories of sound functional area division, which are 0, 1, 2, 3 and 4 acoustic environment functional areas.
  • How to divide the sound functional area to which it belongs according to the attributes of a region is the core content of the sound environment functional area division method, which has become one of the hot topics in the field of acoustic environment research in China.
  • all parts of China have made sound functional areas based on urban attributes, urban planning and urban land use, and have made positive progress.
  • the division method of the acoustic environment functional area is mainly based on the land planning data in the urban planning, and the acoustic environment functional area to which the area belongs is divided according to the area ratio of various land types in the area.
  • the use of GIS technology, remote sensing technology, global satellite positioning technology to improve the efficiency of sound functional area division; the use of quantitative methods such as gray clustering and fuzzy clustering to improve the scientific nature of the division.
  • the sound function area is divided into coarse scales, and detailed noise pollution assessment cannot be performed.
  • the division scale of the acoustic functional area is limited by the urban land use planning data, and the regional scale of the land use planning is large, so that the scale of the acoustic functional area division is not too small.
  • the sub-area is too large, and the same noise limit standard is used inside the area, so that the noise pollution assessment cannot be detailed.
  • the sound function area is updated slowly and cannot adapt to the current situation of urban development.
  • China's urbanization process is accelerating, urban road network and regional expansion, traditional acoustic environment functional areas based on urban planning, using a large number of manual processing, the renewal cycle takes about 10 years, mostly inconsistent with the actual requirements of urban acoustic environment.
  • geospatial data reflect the attributes and functions of the area, especially the road network data and point of interest data.
  • the road network shows the accessibility of the area, and divides the city into relatively small areas with relatively simple functions; the points of interest are various types of objects that provide various services in the city, which exist in real time and are constantly updated in each area. Can be used to describe the current regional attributes of the city.
  • the present invention proposes a method for dividing a city acoustic environment functional area based on an urban road network and a point of interest.
  • the method uses a road network to divide urban areas.
  • the regional sound function area can be divided by cluster analysis of interest points in each area.
  • the invention is applicable to the division of acoustic functional zones in different cities, and can realize the detailed division of acoustic functional zones and reflect the acoustic functional requirements of the current regions.
  • the basic data used in this method is real-time, and the results of the partitioning are applicable to the current noise assessment of the city.
  • the basic data such as the road network and the interest point in the present invention are obtained through the open interface of the Internet map, the data source is public, and the update period is short, which is helpful for quickly carrying out noise evaluation work.
  • a method for dividing the acoustic environment functional area based on road network and interest points, using the road network to divide the urban area, clustering the interest points in each area according to the acoustic environment quality requirements of each interest point category in the area, and calibrating each The sound functional area to which the clustering category belongs is used to divide the sound functional area of the urban area.
  • the method includes the following steps:
  • the road network picture of step A is generated by the existing city road vector data.
  • the use of image morphology methods for road network pictures to divide cities into multiple sub-areas mainly includes the following steps: graphics expansion of road network pictures, merger of parallel two-way roads, intersections and other small areas; fine-grained road network To obtain a road network with a single-point link; identify road intersections and obtain road segments connecting the intersections through boundary tracking; mark each connected area, and identify the road network nodes and road segments that are adjacent to the connected area, and sequentially splicing Get the outline of each area.
  • the city is divided into two according to the main road and the ordinary road.
  • the area is divided according to the main road, and then the ordinary road inside the area obtained by the division is divided into two, and each sub-area is obtained.
  • the divided sub-region set is R
  • the obtained sub-region number is N
  • the i-th sub-region in R is denoted as r i , i ⁇ N.
  • Step B wherein each POI category in each area proportion accounted called sub-region, the number of a point of interest category object region r i representing the ratio of the total number of points of interest within the region of r i is the proportion of sub-areas, referred to as d j,i , the formula is as follows:
  • c j is the j-th interest point category
  • the area description parameter screening two indicators are set to filter the area description parameters, which are the correlation coefficient of the area description parameter and the regional density of the interest point category.
  • Set the correlation screening threshold R t for the positive interest rate is higher than the threshold and the acoustic function requirements are the same, select the interest point category; set the regional density screening threshold E t , for the category below the threshold, think that it can not Used to describe the area and discard it.
  • Clustering method was used to cluster the regions according to the selected parameters of the selected regions, and K clustering region categories were obtained, which were recorded as CC 1 , CC 2 ,..., CC K .
  • the acoustic functional area division of each regional clustering category is realized, and then the acoustic functional area is divided along the road area.
  • acoustic functional zones for cluster calibration are set to class 1 acoustic functional zone, class 2 acoustic functional zone, class 3 acoustic functional zone and no obvious acoustic functional requirement zone, respectively denoted as S 1 , S 2 , S 3 , S none
  • S 1 , S 2 , S 3 , S none The set of sound functional area categories divided into SCs.
  • the acoustic functional area requirements corresponding to each interest point category are determined, and the acoustic functional area requirement of the j-th interest point category is SR j ; according to the service function, the land use area of each interest point, The attributes of the service group and other attributes determine the acoustic functional area impact factor of each interest point category, and the acoustic functional area influence factor of the j-th interest point category is A j .
  • a j represents the acoustic functional zone influence factor of the j-th interest point category
  • d j, cc represents the j-th description parameter of the cluster category cc center point
  • SR j represents the acoustic functional zone requirement of the j-th interest point category
  • S is a sort of sound functional area category.
  • the present invention sets the classification thresholds of the type 1 acoustic function zone, the class 3 acoustic functional zone and the region without the obvious acoustic function requirement, and is respectively recorded as W 1 , W 3 , W none .
  • the mark that exceeds W 1 is a type 1 sound function area
  • the mark that exceeds W 3 is a class 3 sound function area
  • the mark that exceeds W none is an area with no obvious sound function requirement
  • the remaining area mark It It is a class 2 sound functional area.
  • the classification of the four types of acoustic functional zones is to divide the area of a certain extension distance outside the boundary line of the traffic trunk into four types of acoustic functional zones.
  • the data used in the partitioning method is the massive road network data and interest point data in the city.
  • the road network data is used for the division of urban areas
  • the interest point data is used for the calibration of the acoustic environment functional areas in various regions of the city.
  • These data are derived from Internet maps, which are easy to acquire and use, and have fast update speeds, so that domestic cities can quickly carry out research on the division of acoustic functional areas.
  • the method achieves a detailed division of the acoustic environment functional area.
  • the urban areas can be carefully divided, so that different social activity types of areas can be isolated as much as possible, and detailed sound function area division can be realized. It also helps to improve the reasonableness of the noise limits for each area, and supports data support for noise environment assessment.
  • the basic data of the method has real-time performance.
  • the result of the division shows the acoustic function requirements of the current region, and the division speed is fast, and the division update period is short, so the division result is applicable to the current noise estimation of the city.
  • FIG. 1 is a schematic flow chart of a method for dividing an acoustic functional area based on a road network and a point of interest according to the present invention.
  • FIG. 2 is a schematic diagram of a process of dividing a region based on a road network picture according to the present invention.
  • Fig. 2(a) is a schematic diagram of a road network tile
  • Fig. 2(b) is a road network expansion diagram
  • Fig. 2(c) is a road network refinement diagram
  • Fig. 2(d) is a road network node and a road section diagram
  • Fig. 2 (e) is a schematic diagram of the connected areas
  • FIG. 2(f) is a schematic diagram of the outline of each area.
  • FIG. 1 is a schematic flowchart of a method for dividing a sound environment functional area based on a road network and a point of interest according to the present invention. Referring to FIG. 1, the method includes the following steps:
  • the road network picture is generated by the existing city road vector data.
  • the use of image morphology methods for road network pictures to divide cities into multiple sub-areas mainly includes the following steps: identifying and extracting road network nodes and road segments, and then constructing regional contours by road network nodes and road segments.
  • the main steps are: setting map parameters, downloading and splicing to obtain the road network image of the research area; performing image morphology expansion on the road network image, merging parallel small roads, intersections and other small areas; refining the expanded road network to obtain Single-point connected road network; identify road intersections and obtain road segments connecting the intersections by boundary tracking method; mark each connected area; identify road network nodes and road segment fold lines adjacent to the connected area, and sequentially splicing to obtain each area profile.
  • the transformation parameters and the number of times of the main road and the ordinary road are different from the degree of detailing of the division.
  • the area is divided according to the main road, and then the ordinary road inside the area obtained by the division is divided twice. Get each sub-area.
  • the divided sub-region set is R
  • the obtained number of regions is N
  • the i-th sub-region in R is denoted as r i , i ⁇ N.
  • r i number of regions within a point of interest category is the total number of points of interest within the object region r i proportion accounted called sub-region, referred to as d j, i, the following formula formula:
  • c j is the j -th interest point category of the invention
  • two indicators are set to filter the area description parameters, which are the correlation coefficient of the area description parameter and the regional density of the interest point category.
  • the correlation coefficient matrix RE can be obtained, and RE is a symmetric matrix of M order.
  • RE is a symmetric matrix of M order.
  • re pq represents the correlation coefficient of D p and D q , and the formula is as follows:
  • Cov(D p , D q ) represents the covariance of D p and D q ;
  • Var(D p ) represents the variance of D p ;
  • Var(D q ) represents the variance of D q .
  • the average distribution density of each interest point in each sub-area is described by dividing the total number of the j-th interest points in the study area by the number of sub-areas, expressed by e j , and the formula is as follows :
  • the regional description parameter is filtered according to the following principle: setting the correlation screening threshold R t , for the positive correlation is higher than the threshold and the acoustic function requires the same interest Point category, do the merging process; set the regional density screening threshold E t , for the category below the threshold, it is considered that it can not be used to describe the area, discard.
  • the new interest point categories are c' 1 , c' 2 ,..., c' M' , and a total of M' categories.
  • the number of points of interest n' j,i and the distribution density d' j,i ,j ⁇ M' of the M' categories are recalculated for the region r i .
  • Let d' 1,i ,d' 2,i ,...,d' M',i be the region description parameter of the region r i and denote F' i .
  • Clustering method was used to cluster the regions according to the selected parameters of the selected regions, and K clustering region categories were obtained, which were recorded as CC 1 , CC 2 ,..., CC K .
  • the acoustic functional area division of each regional clustering category is realized, and then the acoustic functional area division is performed on the road along the line area.
  • the present invention uses a point of interest to divide the 0-3 type of acoustic functional area.
  • Class 0 acoustic functional zones are less distributed in cities, and the acoustic environment is demanding. Many cities do not set this category when performing acoustic functional zone division.
  • a small number of points of interest such as place names, road facilities, etc. do not indicate specific human activities. If the proportion of such points of interest in the area is large, indicating that the human activities in the area are weak or the area has not been developed, it is set to a region with no obvious acoustic function requirements.
  • the type of sound function area of the cluster calibration is set to class 1 sound function area, class 2 sound function area, class 3 sound function area and area without obvious sound function, which are respectively recorded as S 1 , S 2 , S 3 , S None , the set of sound functional area categories divided into SC.
  • the acoustic functional area requirements corresponding to each interest point category are determined, and the acoustic functional area requirement of the j-th interest point category is SR j ; according to the service function of various interest points, the land use area, The attributes of the service group and other attributes determine the acoustic functional area impact factor of each interest point category, and the acoustic functional area influence factor of the j-th interest point category is A j .
  • the ratio of S to a certain functional area of the cluster category cc is W S,cc , and the formula is as follows:
  • a j represents the acoustic functional zone influence factor of the j-th interest point category
  • d j, cc represents the j-th description parameter of the cluster category cc center point
  • SR j represents the acoustic functional zone requirement of the j-th interest point category
  • S is a sort of sound functional area category.
  • the present invention sets the classification thresholds of the type 1 acoustic function zone, the class 3 acoustic functional zone and the region without the obvious acoustic function requirement, and is respectively recorded as W 1 , W 3 , W none .
  • the mark that exceeds W 1 is a type 1 sound function area
  • the mark that exceeds W 3 is a class 3 sound function area
  • the mark that exceeds W none is an area with no obvious sound function requirement
  • the remaining area mark It It is a class 2 sound functional area.
  • the classification of the four types of acoustic functional zones is to divide the area of a certain extension distance outside the boundary line of the traffic trunk into four types of acoustic functional zones.
  • the extension distance of the traffic trunk line is about 50 ⁇ 5m when it is adjacent to the Class 1 acoustic functional zone; the extension distance is 35 ⁇ 5m when it is adjacent to the Class 2 acoustic functional zone; and the extension distance is 20 ⁇ 5m when it is adjacent to the Class 3 acoustic functional zone.
  • the line type of the traffic trunk is obtained when the area is divided.
  • the epitaxial distance is set, and the four types of acoustic functional areas are divided.

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Abstract

一种基于路网和兴趣点的声环境功能区划分方法，该方法包括：根据路网图片使用图像形态学将城市区域划分为多个子区域；以各兴趣点类别在区域内的比重作为区域描述参数，对区域描述参数进行筛选然后聚类得到不同的区域聚类类别；根据各兴趣点类别的声功能区要求和声环境影响因子，实现各区域聚类类别的声功能区标定并对道路沿线区域进行声功能区划分。该方法将城市中海量的地理空间数据运用到声功能区划分中，具有自动化程度高，划分效率高，更新周期短的优点，适用于城市当前的声环境质量评估。

Description

基于路网和兴趣点的声环境功能区划分方法 技术领域
本发明涉及噪声环境评估中的声环境功能区划分方法类，更具体地，涉及一种基于路网和兴趣点的声环境功能区划分方法。
背景技术
噪声污染是城市环境主要的污染源之一，需要对噪声污染和声环境质量进行评估和管理，而声环境功能区划分是城市声环境质量评价和管理的基础。声环境功能区划分是按照区域的使用功能特点和环境质量要求，将城市各个区域划分为不同的声功能区类别，明确了各个区域的噪声排放限值，以防治环境噪声污染，保护和改善生活环境。国标GB3096-08《声环境质量标准》明确了声功能区划分的类别有五类，分别为0类、1类、2类、3类和4类声环境功能区。
如何根据一个区域的属性划分其所属的声功能区是声环境功能区划分方法的核心内容，其已成为我国声环境研究领域的热点课题之一。目前我国各地根据城市属性、城市规划和城市的土地利用情况进行声功能区划分，取得了积极的进展。目前声环境功能区的划分方法主要根据城市规划中的土地规划数据，按照区域内各种土地类型的面积比例来划分区域所属的声环境功能区。同时，使用了地理信息系统技术、遥感技术、全球卫星定位技术等提升声功能区划分的效率；运用了定量化方法如灰色聚类和模糊聚类等提高划分的科学性。
但是，目前城市声功能区划分方法存在以下问题：
1、声功能区划分尺度粗糙，无法进行详细的噪声污染评估。目前声功能区的划分尺度受限于城市的用地规划数据，而用地规划的区域尺度较大，使得声功能区划分的尺度是宜大不宜小的。子区域过大，区域内部使用同样的噪声限值标准，使得进行噪声污染评估无法细致。
2、声功能区更新速度缓慢，无法适应城市发展现状。目前我国城市化进程加快，城市路网和区域扩张，传统基于城市规划的声环境功能区，使用大量的人工处理，更新周期需要10年左右，大多与城市声环境实际要求不符合。
另一方面，随着互联网技术的快速发展、城市中各类传感器的增多和用户 产生数据的不断丰富，海量城市地理空间数据被收集和公开。这些地理空间数据反映了区域的属性和功能，特别是其中的路网数据和兴趣点数据。路网显示了区域的可达性，并把城市划分为相对独立，功能相对单一的细小区域；兴趣点是城市中提供各种服务的各类地物，在各个区域中实时存在并不断更新，可以用来描述城市当前的区域属性。
发明内容
为了克服现有技术的不足，本发明提出一种基于城市道路网络和兴趣点的城市声环境功能区划分方法。该方法是使用路网划分城市区域，根据区域内各兴趣点类别的声环境质量要求，通过各区域内兴趣点的聚类分析，可以进行区域声功能区的划分。
本发明通过标定合适的模型参数，适用于不同城市的声功能区划分，能实现细致的声功能区划分并反映当下区域的声功能要求。该方法使用的基础数据具有实时性，划分结果适用于城市当前的噪声评估。而且本发明中的路网、兴趣点等基础数据通过互联网地图的开放接口获得，数据来源公开，更新周期短，有助于快速开展噪声评估工作。
为达到上述目的，本发明采用的的技术方案具体为：
一种基于路网和兴趣点的声环境功能区划分方法，使用路网划分城市区域，根据区域内各兴趣点类别的声环境质量要求，对各区域内兴趣点进行聚类分析，并标定各个聚类类别所属声功能区，实现城市区域声功能区的划分，该方法包括以下步骤：
A、对路网图片使用图像形态学方法将城市划分为多个子区域。
其中步骤A的路网图片是通过已有的城市道路矢量数据生成的。对路网图片使用图像形态学方法将城市划分为多个子区域主要包括以下几步：对路网图片进行图形学膨胀，合并并行的双向道路、交叉口等细小区域；对膨胀的路网进行细化，得到单点链接的道路网络；识别道路交叉点并通过边界追踪获取连接各个交叉点的路段；对每个连通的区域进行标记，并识别连通区域临近的路网节点和路段折线，顺序拼接得到各区域轮廓。
在图像形态学处理过程中，根据主要道路和普通道路对城市进行二次划分。首先根据主要道路进行区域划分，然后根据划分得到的区域内部的普通道路进 行二次划分，得到各个子区域。记划分的子区域集合为R，得到的子区域数目为N，R中的第i个子区域记为ri，i≤N。
B、以兴趣点类别在各区域内的比重作为区域描述参数，对区域描述参数进行筛选然后聚类得到不同的区域聚类类别。
其中步骤B中各兴趣点类别在各区域内的比重称为子区域占比，区域ri内某一兴趣点类别的数目占区域ri内兴趣点总数目的比值为子区域占比，记为dj,i，公式如下式：
Figure PCTCN2015098758-appb-000001
其中，cj为第j种兴趣点类别，M为兴趣点类别数目，M＝21，nj,i为区域ri中第cj类兴趣点的数目。
在区域描述参数筛选中，设置两个指标对区域描述参数进行筛选，分别是区域描述参数相关系数和兴趣点类别区域密度。设置相关性筛选阈值Rt，对于正相关性高于此阈值且声功能要求相同的兴趣点类别，做合并处理；设置区域密度筛选阀值Et，对于低于阀值的类别，认为其无法用于描述区域，舍弃。
使用聚类方法根据筛选后的区域描述参数，对区域进行聚类分析，得到K种聚类区域类别，分别记为CC1，CC2，...，CCK。
C、根据各兴趣点类别的声环境功能区要求和声环境影响因子，实现各区域聚类类别的声功能区划分，然后对道路沿线区域进行声功能区划分。
聚类标定的声功能区类型设定为1类声功能区、2类声功能区、3类声功能区和无明显声功能要求区域，分别记为S1，S2，S3，Snone，记划分的声功能区类别集合为SC。
根据各兴趣点类别的社会活动类型，确定各兴趣点类别对应的声功能区要求，记第j类兴趣点类别的声功能区要求为SRj；根据各类兴趣点的服务功能、 用地区域、服务人群等属性，确定各兴趣点类别的声功能区影响因子，记第j种兴趣点类别的声功能区影响因子为Aj。
记聚类类别cc的某一声功能区要求S的比例为WS,cc，公式如下式：
Figure PCTCN2015098758-appb-000002
其中，Aj表示第j种兴趣点类别的声功能区影响因子；dj,cc表示聚类类别cc中心点的第j个描述参数；SRj表示第j种兴趣点类别的声功能区要求；S为某一种划分的声功能区类别。
本发明设置1类声功能区、3类声功能区和无明显声功能要求区域的划分阈值，分别记为W1，W3，Wnone。对各聚类类别，将超过W1的标记为1类声功能区，将超过W3的标记为3类声功能区，再将超过Wnone的标记为无明显声功能要求区域，剩余区域标记为2类声功能区。
4类声功能区的划分方式是将交通干线边界线外一定外延距离的区域划分为4类声功能区。
与现有技术相比，有益效果是：
一是划分方法采用的数据是城市中海量的路网数据和兴趣点数据，将路网数据用于城市区域的划分，将兴趣点数据用于城市各区域声环境功能区的标定。这些数据来源于互联网地图，获取和运用方便，更新速度快，使得国内各个城市可以快速开展声功能区划分研究。
二是该方法实现了细致的声环境功能区划分。运用城市细致的路网数据，可以将城市区域进行细致的划分，使得不同社会活动类型的区域尽可能隔离开来，可以实现细致的声功能区划分。并且有助于提升对各个区域的噪声限值的合理性，为噪声环境评估通过数据支持。
三是该方法的的基础数据具有实时性，划分的结果放映了当下区域的声功能要求，并且划分速度快，划分更新周期短，所以划分结果适用于城市当前的噪声评估。
附图说明
图1是本发明基于路网和兴趣点的声功能区划分方法流程示意图。
图2是本发明基于路网图片的区域划分过程示意图。图2(a)为路网瓦片示意图，图2(b)为路网膨胀示意图，图2(c)为路网细化示意图，图2(d)为路网节点和路段示意图，图2(e)为标记连通区示意图，图2(f)为各区域轮廓示意图。
具体实施方式
下面结合附图对本发明做进一步的描述，但本发明的实施方式并不限于此。
图1为本发明基于路网和兴趣点的声环境功能区划分方法的流程示意图，参见图1，该方法包括以下步骤：
A、对路网图片使用图像形态学方法将城市划分为多个子区域。
其中路网图片是通过已有的城市道路矢量数据生成的。对路网图片使用图像形态学方法将城市划分为多个子区域主要包括以下几步：对路网节点和路段进行识别和提取，再由路网节点和路段构建出各区域轮廓。主要步骤为：设置地图参数，下载和拼接得到研究区域路网图片；对路网图片进行图像形态学膨胀，合并并行的双向道路、交叉口等细小区域；对膨胀的路网进行细化，得到单点连接的道路网络；识别道路交叉点并通过边界追踪法获取连接各个交叉点的路段；对每个连通的区域进行标记；识别连通区域临近的路网节点和路段折线，顺序拼接得到各区域轮廓。
在图像形态学处理过程中，主要道路和普通道路的变换参数、次数和其对划分的细致程度不同，首先根据主要道路进行区域划分，然后根据划分得到的区域内部的普通道路进行二次划分，得到各个子区域。记划分的子区域集合为R，得到的区域数目为N，R中的第i个子区域记为ri，i≤N。
B、以兴趣点类别在各区域内的比重作为区域描述参数，对区域描述参数进行筛选然后聚类得到不同的区域聚类类别。
其中区域ri内某一兴趣点类别的数目占区域ri内兴趣点总数目的比重称为子区域占比，记为dj,i，公式如下式：
Figure PCTCN2015098758-appb-000003
其中，cj为本发明第j种兴趣点类别，兴趣点类别数目M＝21，nj,i为区域ri中第cj类兴趣点的数目。
将d1,i，d2,i，...，dM,i作为区域ri的区域描述参数，记为Fi，Fi＝(d1,i，d2,i，...，dM,i)。记R中所有子区域的第j类兴趣点的子区域占比集合为Dj，Dj＝(dj,1，dj,2，...，dj,N)。
在区域描述参数筛选中，设置两个指标对区域描述参数进行筛选，分别是区域描述参数相关系数和兴趣点类别区域密度。
首先考虑各个区域描述参数的两两相关系数，可以得到相关系数矩阵RE，RE是M阶的对称矩阵。对于RE的一个元素repq，表示Dp和Dq的相关系数，公式如下式：
Figure PCTCN2015098758-appb-000004
rep，q＝req，p
式中：Cov(Dp,Dq)表示Dp和Dq的协方差；Var(Dp)表示Dp的方差；Var(Dq)表示Dq的方差。
然后考虑兴趣点类别的区域密度，用研究区域内第j类兴趣点的总数目除以子区域的数目来描述各类兴趣点在各子区域的平均分布密度，用ej表示，公式如下式：
Figure PCTCN2015098758-appb-000005
在得到区域参数的两两相关系数和各兴趣点类别的区域密度后，根据以下原则筛选区域描述参数：设置相关性筛选阈值Rt，对于正相关性高于此阈值且 声功能要求相同的兴趣点类别，做合并处理；设置区域密度筛选阀值Et，对于低于阀值的类别，认为其无法用于描述区域，舍弃。
经过筛选组成新的兴趣点类别为c'1，c'2，...，c'M'，共M'个类别。对区域ri重新计算M'个类别的兴趣点数目n'j,i和分布密度d'j,i，j≤M'。将d'1,i，d'2,i，...，d'M',i作为区域ri的区域描述参数，记为F'i。使用聚类方法根据筛选后的区域描述参数，对区域进行聚类分析，得到K种聚类区域类别，分别记为CC1，CC2，...，CCK。
C、根据各兴趣点类别的声环境功能区要求和声功能区影响因子，实现各区域聚类类别的声功能区划分，然后对道路沿线区域进行声功能区划分。
本发明使用兴趣点进行0-3类声功能区的划分。0类声功能区在城市中分布较少，且声环境要求苛刻，许多城市进行声功能区划分时不设置该类别。小部分兴趣点类别如地名、道路设施等并不指示特定的人类活动，如果区域内这类兴趣点比例大，表明该区域的人类活动微弱或区域尚未开发，设置为无明显声功能要求区域。所以聚类标定的声功能区类型设定为1类声功能区、2类声功能区、3类声功能区和无明显声功能要求区域，分别记为S1，S2，S3，Snone，记划分的声功能区类别集合为SC。
根据各兴趣点类别的社会活动类型，确定各兴趣点类别对应的声功能区要求，记第j类兴趣点类别的声功能区要求为SRj；根据各类兴趣点的服务功能、用地区域、服务人群等属性，确定各兴趣点类别的声功能区影响因子，记第j种兴趣点类别的声功能区影响因子为Aj。记聚类类别cc的某一声功能区要求S的比例为WS,cc，公式如下式：
Figure PCTCN2015098758-appb-000006
其中，Aj表示第j种兴趣点类别的声功能区影响因子；dj,cc表示聚类类别cc 中心点的第j个描述参数；SRj表示第j种兴趣点类别的声功能区要求；S为某一种划分的声功能区类别。
本发明设置1类声功能区、3类声功能区和无明显声功能要求区域的划分阈值，分别记为W1，W3，Wnone。对各聚类类别，将超过W1的标记为1类声功能区，将超过W3的标记为3类声功能区，再将超过Wnone的标记为无明显声功能要求区域，剩余区域标记为2类声功能区。
4类声功能区的划分方式是将交通干线边界线外一定外延距离的区域划分为4类声功能区。交通干线临近1类声功能区时外延距离为50±5m；临近2类声功能区时外延距离35±5m；临近3类声功能区时外延距离20±5m。交通干线的线型在区域划分时获得，结合本发明已经得到的1-3类声功能区，设置外延距离，进行4类声功能区划分。
以上所述的本发明的实施方式，并不构成对本发明保护范围的限定。任何在本发明的精神原则之内所作出的修改、等同替换和改进等，均应包含在本发明的权利要求保护范围之内。

Claims (10)

  1. 一种基于路网和兴趣点的声环境功能区划分方法，其特征在于，使用路网划分城市区域，根据区域内各兴趣点类别的声环境质量要求，对各区域内兴趣点进行聚类分析，并标定各个聚类类别所属声功能区，实现城市区域声功能区的划分。
  2. 如权利要求1所述的方法，其特征在于，该方法包括以下步骤:
    A、对路网图片使用图像形态学方法将城市划分为多个子区域；
    B、以各兴趣点类别在区域内的比重作为区域描述参数，对区域描述参数进行筛选然后聚类得到不同的区域聚类类别；
    C、根据各兴趣点类别的声环境功能区要求和声环境影响因子，实现各区域聚类类别的声功能区划分，然后对道路沿线区域进行声功能区划分。
  3. 如权利要求2所述的方法，其特征在于，步骤A中的路网图片是通过已有的城市道路矢量数据生成的，对路网图片使用图像形态学方法将城市划分为多个子区域，主要包括以下几步：对路网图片进行图形学膨胀，合并细小区域；对膨胀的路网进行细化，得到单点链接的道路网络；识别道路交叉点并通过边界追踪获取连接各个交叉点的路段；对每个连通的区域进行标记，并识别连通区域临近的路网节点和路段折线，顺序拼接得到各区域轮廓。
  4. 如权利要求3所述的方法，其特征在于，在图像形态学处理过程中，根据主要道路和普通道路对城市进行二次划分，先根据主要道路进行区域划分，然后根据划分得到的区域内部的普通道路进行二次划分，得到各个子区域；记划分的子区域集合为R，得到的子区域数目为N，R中的第i个子区域记为ri，i≤N。
  5. 如权利要求2所述的方法，其特征在于，步骤B中的各兴趣点类别在区域内的比重，称为子区域占比，区域ri内某一兴趣点类别的数目占区域ri内兴趣点总数目的比值为子区域占比，记为dj,i，公式如下式：
    Figure PCTCN2015098758-appb-100001
    其中，cj为第j种兴趣点类别，M为兴趣点类别数目，nj,i为区域ri中第cj类兴趣点的数目。
  6. 如权利要求2或5所述的方法，其特征在于，步骤B中的区域描述参数筛选，设置两个指标对区域描述参数进行筛选，分别是区域描述参数相关系数和兴趣点类别区域密度，设置相关性筛选阈值Rt，对于正相关性高于此阈值且声功能要求相同的兴趣点类别，做合并处理；设置区域密度筛选阀值Et，对于低于阀值的类别，认为其无法用于描述区域，舍弃。
  7. 如权利要求6所述的方法，其特征在于，步骤B中的聚类过程，使用聚类方法根据筛选后的区域描述参数对区域进行聚类分析，得到K种聚类区域类别，分别记为CC1，CC2，...，CCK。
  8. 如权利要求7所述的方法，其特征在于，步骤C中的道路沿线区域声功能区划分，划分方式为，将交通干线边界线外一定外延距离的区域划分为4类声功能区，声功能区类别包括1类声功能区、2类声功能区、3类声功能区和无明显声功能要求区域，分别记为S1，S2，S3，Snone，记划分的声功能区类别集合为SC。
  9. 如权利要求8所述的方法，其特征在于，步骤C中的各兴趣点类别的声环境功能区要求和声环境影响因子，根据各兴趣点类别的社会活动类型，确定各兴趣点类别对应的声功能区要求，记第j类兴趣点类别的声功能区要求为SRj；根据各类兴趣点的属性，确定各兴趣点类别的声功能区影响因子，记第j种兴趣点类别的声功能区影响因子为Aj。
  10. 如权利要求9所述的方法，其特征在于，步骤C中使用声功能区要求比例来标定各聚类类别所属声功能区，记聚类类别cc的某一声功能区要求S的比例为WS,cc，公式如下式：
    Figure PCTCN2015098758-appb-100002
    其中，Aj表示第j种兴趣点类别的声功能区影响因子；dj,cc表示聚类类别cc中心点的第j个描述参数；是表示第j种兴趣点类别的声功能区要求；S为某一种划分的声功能区类别；
    设置1类声功能区、3类声功能区和无明显声功能要求区域的划分阈值，分别记为W1，W3，Wnone，对各聚类类别，将超过W1的标记为1类声功能区，将超过W3的标记为3类声功能区，再将超过Wnone的标记为无明显声功能要求区域，剩余区域标记为2类声功能区。
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