前面讲的了如何分析一批gps数据一年的使用情况(所在经纬度是哪个城市);其实这个需求是前公司提给一个python数据分析的。需求蛮多，包括最大速度啊、平均速度啊、有没急刹车啊。我是负责配合数据生成报告的，但是我为了学习因此我也尝试分析了少量。

很多时候我的能力就是这样慢慢锻炼出来的，看到一些新的技术点，自己都想尝试做一下，慢慢的就变成全栈了。分析一段时间后，我发现我分析的数据准确率要超过我们的数据分析员，原来我们分析员毕竟不是开发人员，没有接口池的概念，并发问题导致接口异常产生了异常数据。分析人员的主要优势是数学分析逻辑，接口完全没有能力处理。于是后来协调，我来给他提供接口池。于是我独立开发的接口池马上派上了用场，效果非常nice。

好了，聊完了别人聊自己。一个分析程序跑n天，总感觉慢，于是我想进一步优化。而且我意识到了一个最严重的问题，还是接口调用接口池的问题。有没办法做一个本地化的接口，这样就能做一劳永逸的方法。于是我决定自己创建个数据库提供服务。思路很简单，把经纬度按照0.01的差额全部掉用接口取到放进数据库，总共有500多w条数据，1G多，数据库分表优化就足够了，甚至不需要高速缓存就能满足一般调用。

好了，努力后来有回报了，马上有一个新的功能需求，如下:

就是点击地图上某个区域，要知道是点的哪个区域同时是高亮选中区域。这个需求的难点是这个区域是不规则的，这个时候我取到的经纬度数据就派的上用场了，首先根据球形的一些公式把经纬度与我们的坐标xy之间进行转换，然后点击坐标xy我们就知道点的哪些区域了。

当然 前端的话，我们还是得进行优化。把0.01精度的按照1精度取值，这样前端需要的数据就缩小了10000倍。然后发现还不够，还得自创格式。如用“50-76”代替“50,51,52,...,76”,减少代码体积，于是得到一个优化格式的json

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不到4kb，非常nice。