前面已经介绍过使用scrapy爬取百度图片内容，这里介绍一下通过百度地图提供的接口爬取POI数据，并且保存到postgresql数据库中。图片爬虫与数据爬虫处理的方式还是有一些不一样的。

在百度地图开放平台上可以找到相关的地点检索的文档，关于接口的使用方法有很详细的描述，主要还是通过关键词和POI分类进行检测，注意不同的关键词可能得到的数据有重复，注意通过uid来去重。

具体的创建项目的过程，前面的博文里已经讲过，这里只展示一下核心代码

请求数据代码farm.py

import scrapy
import json

from catchFarm.items import CatchfarmItem

print("构造")


class FarmSpider(scrapy.Spider):
    name = 'farm'
    allowed_domains = ['api.map.baidu.com']
    url = 'https://api.map.baidu.com/place/v2/search?query=牧业&tag=公司企业&region={}&output=json&ak=你申请的ak&coord_type=1'
    print("开始")
    # 每次请求一个市的，市列表在这
    areas = [
        {
            "name": '郑州市',
            "code": '268'
        }, {
            "name": '驻马店市',
            "code": '269'
        }, {
            "name": '安阳市',
            "code": '267'
        }, {
            "name": '新乡市',
            "code": '152'
        }, {
            "name": '洛阳市',
            "code": '153'
        }, {
            "name": '商丘市',
            "code": '154'
        }, {
            "name": '许昌市',
            "code": '155'
        }, {
            "name": '濮阳市',
            "code": '209'
        },
        {
            "name": '开封市',
            "code": '210'
        }, {
            "name": '焦作市',
            "code": '211'
        }, {
            "name": '三门峡市',
            "code": '212'
        }, {
            "name": '平顶山市',
            "code": '213'
        }, {
            "name": '信阳市',
            "code": '214'
        }, {
            "name": '鹤壁市',
            "code": '215'
        }, {
            "name": '周口市',
            "code": '308'
        },
        {
            "name": '南阳市',
            "code": '309'
        },
        {
            "name": '漯河市',
            "code": '344'
        }
    ]
    index = 0
    start_urls = [url.format(areas[0]['code'])]
    print(start_urls)

    def parse(self, response):
        print(response.text)
        print("第{}次请求".format(str(self.index)))

        results = json.loads(response.text)
        results = results["results"]
        for result in results:
            item = CatchfarmItem()
            # 异常数据处理
            if 'location' not in result.keys():
                continue
            # 解析数据    
            item['name'] = result['name']
            item['lat'] = result['location']['lat']
            item['lng'] = result['location']['lng']
            item['address'] = result['address']
            item['province'] = result['province']
            item['city'] = result['city']
            item['area'] = result['detail']
            item['uid'] = result['uid']
            yield item

        self.index = self.index + 1
        if self.index >= len(self.areas):
            return
        yield scrapy.Request(self.update_url(),
                             callback=self.parse)

    def update_url(self):
        print(self.url.format(self.areas[self.index]['code']))
        return self.url.format(self.areas[self.index]['code'])

item.py

import scrapy


class CatchfarmItem(scrapy.Item):
    # define the fields for your item here like:
    name = scrapy.Field()
    lat = scrapy.Field()
    lng = scrapy.Field()
    address = scrapy.Field()
    province = scrapy.Field()
    city = scrapy.Field()
    area = scrapy.Field()
    detail = scrapy.Field()
    uid = scrapy.Field()

    pass

pipelines.py

import psycopg2

# useful for handling different item types with a single interface
from itemadapter import ItemAdapter


class CatchfarmPipeline(object):

    def __init__(self):
        self.cursor = None
        self.conn = None
        self.conn = psycopg2.connect(database="postgres",
                                     user="postgres",
                                     password="postgres",
                                     host="127.0.0.1",
                                     port="5432")

        self.cursor = self.conn.cursor()
    def open_spider(self, spider):
        pass

    def process_item(self, item, spider):
        # id存在则不插入
        sql = "INSERT INTO postgres.farm_baidu(name, lat, lng, address, province, city, area, uid, geom) " \
              "VALUES ('{}', {}, {},'{}', '{}', '{}', '{}', '{}',st_point({},{},4326) ) on conflict(uid)  do nothing"
        sql = sql.format(item['name'], item['lat'], item['lng'], item['address'], item['province'], item['city'],
                         item['area'], item['uid'], item['lng'], item['lat'])
        print(sql)
        self.cursor.execute(sql)

        return item

    def close_spider(self, spider):
        self.conn.commit()
        self.cursor.close()
        # 关闭数据库连接
        self.conn.close()

settings.py

BOT_NAME = 'catchFarm'

SPIDER_MODULES = ['catchFarm.spiders']
NEWSPIDER_MODULE = 'catchFarm.spiders'




# Obey robots.txt rules
ROBOTSTXT_OBEY = False

# Configure maximum concurrent requests performed by Scrapy (default: 16)
CONCURRENT_REQUESTS = 1


DOWNLOAD_DELAY = 3
# 设置延迟

ITEM_PIPELINES = {
   'catchFarm.pipelines.CatchfarmPipeline': 300,
}

爬取结果