OpenStreetMap数据清洗(SQL&MonogoDB版本)

目标:通过网上下载的OpenStreetMap.xml数据格式,将该文件的格式进行统计,清洗,并导出成CSV格式的文件,最后倒入到SQLite中

本案例中所需的包

import csv
import codecs
import pprint
import re
import xml.etree.cElementTree as ET
from collections import defaultdict
import cerberus
import schema

1.统计文件中每一个标签出现的次数

思路:将xml文件使用sax解析,将每一个节点的的标签值设为字典的key,次数为value,初始化为0,

　　 循环文件,如果可以找到key,那么value的值+1,否则不变

def count_tags(filename):
    #1.读文件
    osm = ET.ElementTree(file=filename)
    #2.获取根节点
    root = osm.getroot()
    #3.获取根节点的标签,创建一个字典来存放标签名和次数
    tags_count_dic = {root.tag:0}
    #4.循环文件
    for _,ele in ET.iterparse(filename,events=('start',)):
        #5.如果有元素的tag在字典中,则value的值+1,否则表示该标签只出现一次
        if ele.tag in tags_count_dic:
            tags_count_dic[ele.tag] += 1
        else:
            tags_count_dic[ele.tag] = 1
    return tags_count_dic

def test():
    #测试函数的断言不出错,表示结果正确
    tags = count_tags('example.osm')
    pprint.pprint(tags)
    assert tags == {'bounds': 1,
                     'member': 3,
                     'nd': 4,
                     'node': 20,
                     'osm': 1,
                     'relation': 1,
                     'tag': 7,
                     'way': 1}

if __name__ == "__main__":
    test()

2.根据正则表达式,确定各种标签类型的数量

思路:获取根据传入的element,来获取tag,获取到tag即可获取到k的值,在根据正则表达式进行匹配,将匹配成功的值放入到不同的字典中

lower = re.compile(r'^([a-z]|_)*$') #仅包含小写字母且有效的标记
lower_colon = re.compile(r'^([a-z]|_)*:([a-z]|_)*$') #名称中有冒号的其他有效标记
problemchars = re.compile(r'[=\+/&<>;\'"\?%#$@\,\. \t\r\n]') #字符存在问题的标记

def key_type(element,keys):
    #1.找到需要处理的标签进行处理
    if element.tag == 'tag':
        #2.获取带匹配的字符串
        key = element.attrib['k']
        #逐次匹配,并将匹配成功的结果放到keys中,并返回
        if lower.search(key):
            keys['lower'] += 1
        elif lower_colon.search(key):
            keys['lower_colon'] += 1
        elif problemchars.search(key):
            keys['problemchars'] += 1
        else:
            keys['other'] += 1
    return keys

def process_map(filename):
    keys = {"lower": 0, "lower_colon": 0, "problemchars": 0, "other": 0}
    for _, element in ET.iterparse(filename):
        keys = key_type(element, keys)

    return keys

def test():
    #测试函数的断言不报错,代码正确
    keys = process_map('example.osm')
    pprint.pprint(keys)
    assert keys == {'lower': 5, 'lower_colon': 0, 'other': 1, 'problemchars': 1}

if __name__ == "__main__":
    test()

3.搜索用户,返回一组唯一的用户ID

思路:找到uid所对应的tag,循环xml文件,如果标签存在uid,就加入到set中返回

def get_user(element):
    #如果标签中包含'uid'这一属性,则返回该属性的值
    if 'uid' in element.attrib:
        return element.attrib['uid']

def process_map(filename):
    users = set()
    #循环xml文件,如果每行的元素中有'uid'这一标签,则其值取出加入到set中,返回
    for _, element in ET.iterparse(filename):
        if get_user(element):
            users.add(get_user(element))
    return users

def test():
    #断言不出错,程序正确
    users = process_map('example.osm')
    pprint.pprint(users)
    assert len(users) == 6

if __name__ == "__main__":
    test()

4.完善街道名,将街道中的一些不合法的值去除

思路:循环街道的字典,名称在mapping,则进行替换,返回替换后的字符串

OSMFILE = "example1.osm"
street_type_re = re.compile(r'\b\S+\.?$', re.IGNORECASE)

expected = ["Street", "Avenue", "Boulevard", "Drive", "Court", "Place", "Square", "Lane", "Road",
            "Trail", "Parkway", "Commons"]

# UPDATE THIS VARIABLE
#题目这里的字符串需要更改,否则结果错误
mapping = { "Rd.": "Road",
            "St.": "Street",
            "Ave": "Avenue"
            }

def audit_street_type(street_types,street_name):
    m = street_type_re.search(street_name)
    if m:
        street_type = m.group()
        if street_type not in expected:
            street_types[street_type].add(street_name)

def is_street_name(elem):
    return (elem.attrib['k'] == 'addr:street')

def audit(osmfile):
    osm_file = open(osmfile,'r')
    street_types = defaultdict(set)
    for event,ele in ET.iterparse(osmfile,events=('start',)):
        if ele.tag == 'tag' or ele.tag == 'way':
            for tag in ele.iter('tag'):
                if is_street_name(tag):
                    audit_street_type(street_types,tag.attrib['v'])
    osm_file.close()
    return street_types

def update_name(name, mapping):
    #获取需要修改的key
    changewords = mapping.keys()
    #如果名称相同,则替换字符,并返回
    for word in changewords:
        if word in name:
            name = name.replace(word,mapping.get(word))
    return name

def test():
    #断言不出错,则结果正确
    st_types = audit(OSMFILE)
    assert len(st_types) == 3
    pprint.pprint(dict(st_types))
    for st_type, ways in st_types.iteritems():
        for name in ways:
            better_name = update_name(name, mapping)
            print name, "=>", better_name
            if name == "West Lexington St.":
                assert better_name == "West Lexington Street"
            if name == "Baldwin Rd.":
                assert better_name == "Baldwin Road"

if __name__ == "__main__":
    test()

5.数据清洗

目标数据的结构

node节点需要[id,user,uid,version,lat,lon,timestamp,changeset]

node节点下的tags子节点需要[id,key,value,type]

{'node': {'id': 757860928,
          'user': 'uboot',
          'uid': 26299,
       'version': '',
          'lat': 41.9747374,
          'lon': -87.6920102,
          'timestamp': '2010-07-22T16:16:51Z',
      'changeset': 5288876},
 'node_tags': [{'id': 757860928,
                'key': 'amenity',
                'value': 'fast_food',
                'type': 'regular'},
               {'id': 757860928,
                'key': 'cuisine',
                'value': 'sausage',
                'type': 'regular'},
               {'id': 757860928,
                'key': 'name',
                'value': "Shelly's Tasty Freeze",
                'type': 'regular'}]}

way节点需要[id,user,uid,version,timestamp,changeset]

way节点下的nodes子节点需要[id,node_id,position]

way节点下的tag子节点需要[id,key,value,type]

{'way': {'id': 209809850,
         'user': 'chicago-buildings',
         'uid': 674454,
         'version': '',
         'timestamp': '2013-03-13T15:58:04Z',
         'changeset': 15353317},
 'way_nodes': [{'id': 209809850, 'node_id': 2199822281, 'position': 0},
               {'id': 209809850, 'node_id': 2199822390, 'position': 1},
               {'id': 209809850, 'node_id': 2199822392, 'position': 2},
               {'id': 209809850, 'node_id': 2199822369, 'position': 3},
               {'id': 209809850, 'node_id': 2199822370, 'position': 4},
               {'id': 209809850, 'node_id': 2199822284, 'position': 5},
               {'id': 209809850, 'node_id': 2199822281, 'position': 6}],
 'way_tags': [{'id': 209809850,
               'key': 'housenumber',
               'type': 'addr',
               'value': ''},
              {'id': 209809850,
               'key': 'street',
               'type': 'addr',
               'value': 'West Lexington St.'},
              {'id': 209809850,
               'key': 'street:name',
               'type': 'addr',
               'value': 'Lexington'},
              {'id': '',
               'key': 'street:prefix',
               'type': 'addr',
               'value': 'West'},
              {'id': 209809850,
               'key': 'street:type',
               'type': 'addr',
               'value': 'Street'},
              {'id': 209809850,
               'key': 'building',
               'type': 'regular',
               'value': 'yes'},
              {'id': 209809850,
               'key': 'levels',
               'type': 'building',
               'value': ''},
              {'id': 209809850,
               'key': 'building_id',
               'type': 'chicago',
               'value': ''}]}

思路: 1.使用iterparse便利xml中每一个顶层标签

2.使用自定义函数将每个元素变成多个数据结构

3.利用架构和验证库保证数据格式的正确

4.将每个数据结构写入相应的csv文件

OSM_PATH = "example1.osm"

NODES_PATH = "nodes.csv"  #node标签生成的文件名
NODE_TAGS_PATH = "nodes_tags.csv"  #node下的tag标签生成的文件名
WAYS_PATH = "ways.csv"  #way标签生成的文件名
WAY_NODES_PATH = "ways_nodes.csv" #way标签下的node生成的文件名
WAY_TAGS_PATH = "ways_tags.csv" #way标签下的tag生成的文件名

LOWER_COLON = re.compile(r'^([a-z]|_)+:([a-z]|_)+') #字符串中有冒号和小写字母的标记
PROBLEMCHARS = re.compile(r'[=\+/&<>;\'"\?%#$@\,\. \t\r\n]')#字符存在问题的标记
SCHEMA = schema.schema #模板文件

# Make sure the fields order in the csvs matches the column order in the sql table schema
#每一个生成的文件的表头
NODE_FIELDS = ['id', 'lat', 'lon', 'user', 'uid', 'version', 'changeset', 'timestamp']
NODE_TAGS_FIELDS = ['id', 'key', 'value', 'type']
WAY_FIELDS = ['id', 'user', 'uid', 'version', 'changeset', 'timestamp']
WAY_TAGS_FIELDS = ['id', 'key', 'value', 'type']
WAY_NODES_FIELDS = ['id', 'node_id', 'position']

def shape_element(element, node_attr_fields=NODE_FIELDS, way_attr_fields=WAY_FIELDS,
                  problem_chars=PROBLEMCHARS, default_tag_type='regular'):
    """Clean and shape node or way XML element to Python dict"""

    node_attribs = {}  #存放生成node的key和value,key作表头,value作内容
    way_attribs = {}  #存放生成way的key和value,key作表头,value作内容
    way_nodes = []    #存放生成way标签下的nd子标签的值,[{...},{...}]
    tags = []  #存放node和way下的tag子标签的值 ,[{...},{...},]# Handle secondary tags the same way for both node and way elements

    # YOUR CODE HERE
    #先提取node字段
    if element.tag == 'node':
        #1.循环node_field表头,如果element中有key所对应的属性,则放入到node_attribs字典中
        for key in NODE_FIELDS:
            node_attribs[key] = element.attrib[key]
        #2.循环子节点,获取tags元素的值
        for child in element:
            Node_Tags = {} 
            #匹配字母和冒号
            colon = re.match(LOWER_COLON,child.attrib['k'])
            #匹配异常字符
            problem = re.match(PROBLEMCHARS,child.attrib['k'])
            #异常字符直接跳过,进行下一次查找
            if problem:
                continue
            #如果tag是包含字母和冒号<tag k="addr:housenumber" v="1412"/>需要解析成{'id': 12345, 'key': 'housenumber', 'value': '1412', 'type': 'addr'}
            elif colon:
                #从父节点获取id属性的值
                Node_Tags['id'] = element.attrib['id']
                #获取k="addr:housenumber"的值,以:拆分,第一个值为type的值
                type_value = child.attrib['k'].split(':',1)[0]
                Node_Tags['type'] = type_value
                #获取k="addr:housenumber"的值,以:拆分,第二个值为key的值
                Node_Tags['key'] = child.attrib['k'].split(':',1)[1]
                #获取v=1412的值,为value的值
                Node_Tags['value'] = child.attrib['v']
                #将处理后的数据加入到字典中
                tags.append(Node_Tags)
            #tag不包含冒号<tag k="building" v="yes"/>
            else:
                #从父节点获取id属性的值
                Node_Tags['id'] = element.attrib['id']
                #type的值是 regular
                Node_Tags['type'] = 'regular'
                #获取k=building的值,为key的值
                Node_Tags['key'] = child.attrib['k']
                #获取v=yes的值,为value的值
                Node_Tags['value'] = child.attrib['v']
                #将处理后的数据加入到字典中
                tags.append(Node_Tags)
        #返回node处理之后的结果
        return {'node': node_attribs, 'node_tags': tags}
    #在提取way字段
    elif element.tag == 'way':
        1.循环way_field表头,如果element中有key所对应的属性,则放入到way_attribs字典中
        for key in WAY_FIELDS:
            way_attribs[key] = element.attrib[key]
        counter = 0 #计数,用于填充way下面nd子标签的position的值
        #循环父节点下的子节点
        for child in element:
            Way_Nodes = {} #存放nd子标签
            Way_Tags = {}  #存放tag子标签
            #处理nd子标签
            if child.tag == 'nd':
                #从父节点获取id属性的值
                Way_Nodes['id'] = element.attrib['id']
                #从自身的ref,来获取该属性的值
                Way_Nodes['node_id'] = child.attrib['ref']
                #获取position的值,每循环一次nd,counter + 1
                Way_Nodes['position'] = counter
                counter += 1
                将处理后的nd子节点数据加入到字典中
                way_nodes.append(Way_Nodes)
            #处理tag子标签
            elif child.tag == 'tag':
                #同处理node下的tag子节点
                colon = re.match(LOWER_COLON,child.attrib['k'])
                problem = re.match(PROBLEMCHARS,child.attrib['k'])
                if problem:
                    continue
                elif colon:
                    Way_Tags['id'] = element.attrib['id']
                    type_value = child.attrib['k'].split(':',1)[0]
                    Way_Tags['key'] = child.attrib['k'].split(':',1)[1]
                    Way_Tags['type'] = type_value
                    Way_Tags['value'] = child.attrib['v']
                    tags.append(Way_Tags)
                else:
                    Way_Tags['id'] = element.attrib['id']
                    Way_Tags['key'] = child.attrib['k']
                    Way_Tags['type'] = 'regular'
                    Way_Tags['value'] = child.attrib['v']
                    tags.append(Way_Tags)
        return {'way': way_attribs, 'way_nodes': way_nodes, 'way_tags': tags}

def get_element(osm_file, tags=('node', 'way', 'relation')):
    """Yield element if it is the right type of tag"""
    """如果是正确的类型时,返回标签中的tag"""
    context = ET.iterparse(osm_file, events=('start', 'end'))
    _, root = next(context)
    for event, elem in context:
        if event == 'end' and elem.tag in tags:
            yield elem
            root.clear()

def validate_element(element, validator, schema=SCHEMA):
    """Raise ValidationError if element does not match schema"""
    """当和schema的数据格式不匹配时,抛出异常"""
    if validator.validate(element, schema) is not True:
        field, errors = next(validator.errors.iteritems())
        message_string = "\nElement of type '{0}' has the following errors:\n{1}"
        error_string = pprint.pformat(errors)

        raise Exception(message_string.format(field, error_string))

class UnicodeDictWriter(csv.DictWriter, object):
    """Extend csv.DictWriter to handle Unicode input"""
    """扩展csv下的DictWriter方法的去支持Unicode输入"""
    def writerow(self, row):
        super(UnicodeDictWriter, self).writerow({
            k: (v.encode('utf-8') if isinstance(v, unicode) else v) for k, v in row.iteritems()
        })

    def writerows(self, rows):
        for row in rows:
            self.writerow(row)

def process_map(file_in, validate):
    """Iteratively process each XML element and write to csv(s)"""
    """将处理好的xml文件写入到csv中"""
    with codecs.open(NODES_PATH, 'w') as nodes_file, \
         codecs.open(NODE_TAGS_PATH, 'w') as nodes_tags_file, \
         codecs.open(WAYS_PATH, 'w') as ways_file, \
         codecs.open(WAY_NODES_PATH, 'w') as way_nodes_file, \
         codecs.open(WAY_TAGS_PATH, 'w') as way_tags_file:

        nodes_writer = UnicodeDictWriter(nodes_file, NODE_FIELDS)
        node_tags_writer = UnicodeDictWriter(nodes_tags_file, NODE_TAGS_FIELDS)
        ways_writer = UnicodeDictWriter(ways_file, WAY_FIELDS)
        way_nodes_writer = UnicodeDictWriter(way_nodes_file, WAY_NODES_FIELDS)
        way_tags_writer = UnicodeDictWriter(way_tags_file, WAY_TAGS_FIELDS)

        nodes_writer.writeheader()
        node_tags_writer.writeheader()
        ways_writer.writeheader()
        way_nodes_writer.writeheader()
        way_tags_writer.writeheader()

        validator = cerberus.Validator()

        for element in get_element(file_in, tags=('node', 'way')):
            el = shape_element(element)
            if el:
                if validate is True:
                    validate_element(el, validator)

                if element.tag == 'node':
                    nodes_writer.writerow(el['node'])
                    node_tags_writer.writerows(el['node_tags'])
                elif element.tag == 'way':
                    ways_writer.writerow(el['way'])
                    way_nodes_writer.writerows(el['way_nodes'])
                    way_tags_writer.writerows(el['way_tags'])

第二种方法

高阶解法

def shape_tag(el, tag): 
    #tag标签返回的格式(el是父节点标签指的是node标签,tag是子节点指的是tag标签)
    tag = {
        'id'   : el.attrib['id'],
        'key'  : tag.attrib['k'],
        'value': tag.attrib['v'],
        'type' : 'regular'
    }

    if LOWER_COLON.match(tag['key']):
        #如果tag的key中出现冒号<tag k="addr:housenumber" v="1412"/>,则根据:进行拆分,获取type和key
        tag['type'], _, tag['key'] = tag['key'].partition(':')
    return tag

def shape_way_node(el, i, nd):
    #way下的nd标签返回的格式(el是父节点标签指的是way标签,nd是子节点指的是nd标签)
    return {
        'id'       : el.attrib['id'],
        'node_id'  : nd.attrib['ref'],
        'position' : i
    }

def shape_element(element, node_attr_fields=NODE_FIELDS, way_attr_fields=WAY_FIELDS,
                  problem_chars=PROBLEMCHARS, default_tag_type='regular'):
    """Clean and shape node or way XML element to Python dict"""

    node_attribs = {}
    way_attribs = {}
    way_nodes = []
    #直接获取所有的tag子标签
    tags = [shape_tag(element, t) for t in element.iter('tag')]  # Handle secondary tags the same way for both node and way elements

    # YOUR CODE HERE
    if element.tag == 'node':
        node_attribs = {f: element.attrib[f] for f in node_attr_fields}
        return {'node': node_attribs, 'node_tags': tags}
    elif element.tag == 'way':
        way_attribs = {f: element.attrib[f] for f in way_attr_fields}
        #获取way标签下nd标签的各个值
        way_nodes = [shape_way_node(element, i, nd)
                     for i, nd
                     in enumerate(element.iter('nd'))]
        return {'way': way_attribs, 'way_nodes': way_nodes, 'way_tags': tags}

6.数据清洗(准备数据库 MonogoDB版本)

处理数据并将数据形状变成我们之前提到的模型。输出应该是字典列表，如下所示

{
"id": "",
"type: "node",
"visible":"true",
"created": {
          "version":"",
          "changeset":"",
          "timestamp":"2013-08-03T16:43:42Z",
          "user":"linuxUser16",
          "uid":""
        },
"pos": [41.9757030, -87.6921867],
"address": {
          "housenumber": "",
          "postcode": "",
          "street": "North Lincoln Ave"
        },
"amenity": "restaurant",
"cuisine": "mexican",
"name": "La Cabana De Don Luis",
"phone": "1 (773)-271-5176"
}

要求:

1.node下的tag子标签处理如下

<tag k="addr:housenumber" v=""/>
<tag k="addr:street" v="North Lincoln Avenue"/>
<tag k="addr:street:name" v="Lincoln"/>
<tag k="addr:street:prefix" v="North"/>
<tag k="addr:street:type" v="Avenue"/>
<tag k="amenity" v="pharmacy"/>

应该改写为：

{...
"address": {
    "housenumber": 5158,
    "street": "North Lincoln Avenue"
}
"amenity": "pharmacy",
...
}

2.way标签下的子标签的处理应该改为

<nd ref=""/>
<nd ref=""/>
应该改为
"node_refs": ["", ""]

lower = re.compile(r'^([a-z]|_)*$')
lower_colon = re.compile(r'^([a-z]|_)*:([a-z]|_)*$')
problemchars = re.compile(r'[=\+/&<>;\'"\?%#$@\,\. \t\r\n]')

CREATED = [ "version", "changeset", "timestamp", "user", "uid"]

def shape_element(element):
    node = {}
    if element.tag == "node" or element.tag == "way" :
        # YOUR CODE HERE

        #1.获取非父子节点的key和value
        node = {tag.attrib['k']:tag.attrib['v']
               for tag in element.iter('tag')
               if not tag.attrib['k'].startswith('addr:')
               and not problemchars.search(tag.attrib['k'])}
        #2.获取address节点的key和value
        #例:<tag k="addr:street:name" v="Lincoln"/>
        #1)将addr:后面的字符作为key,v的值作为value
        #2)如果标签中的key值以addr:开头,且:的数量等于1
        #3)且没有特殊字符的存在
        node['address'] = {tag.attrib['k'][5:]: tag.attrib['v']
                           for tag in element.iter('tag')
                           if tag.attrib['k'].startswith('addr:')
                           and tag.attrib['k'].count(':') == 1
                           and not problemchars.search(tag.attrib['k'])}
        #3.获取element节点的属性
　　　　 attr = element.attrib
        node['id'] = attr['id'] #获取id
        node['type'] = element.tag #获取类型type
        node['visible'] = attr.get('visible') #获取visible

　　　　 #4.获取created节点中的key和value
        node['created'] = {c:attr[c] for c in CREATED}
        #5.如果标签的类型是way,则从该标签获取ref的值封装到node_refs中,否则直接从node中获取lat,lon的值填充到pos中
        if element.tag == 'way':
            node['node_refs'] = [nd.attrib['ref'] for nd in element.iter('nd')]
        else:
            node['pos'] = [float(attr['lat']),float(attr['lon'])]
        return node
    else:
        return None

def process_map(file_in, pretty = False):
    # You do not need to change this file
    file_out = "{0}.json".format(file_in)
    data = []
    with codecs.open(file_out, "w") as fo:
        for _, element in ET.iterparse(file_in):
            el = shape_element(element)
            if el:
                data.append(el)
                if pretty:
                    fo.write(json.dumps(el, indent=2)+"\n")
                else:
                    fo.write(json.dumps(el) + "\n")
    return data

def test():
    #如果测试代码不出错,则结果正确
    correct_first_elem = {
        "id": "",
        "visible": "true",
        "type": "node",
        "pos": [41.9730791, -87.6866303],
        "created": {
            "changeset": "",
            "user": "bbmiller",
            "version": "",
            "uid": "",
            "timestamp": "2012-03-28T18:31:23Z"
        }
    }
    assert data[0] == correct_first_elem
    assert data[-1]["address"] == {
                                    "street": "West Lexington St.",
                                    "housenumber": ""
                                      }
    assert data[-1]["node_refs"] == [ "", "",  "", "",
                                    "", "", ""]

if __name__ == "__main__":
    test()