新加坡NEA的JSON API
2016年04月05日 新加坡 数据 社会 天气 API 行 添加评论新加坡的天气数据由气象局公布，官方有XML的接口，做得其实很好了。如果直接做网页的话，有些时候希望是json的api，所以我就山寨了一个，把NEA的XML数据直接转换成了JSON格式。项目托管在GAE上，有兴趣使用的读者可以测试一下，不过可能受到GAE每天的额度的限制不一定好使哦。
接口解释如下：
https://bt201504.appspot.com/nea?dataset=[dataset]&pretty=[pretty?]
可用的dataset如下：（和NEA的名字一样也可以使用，不然会使用下列映射表映射到NEA的官方名称）
{'2hr': '2hr_nowcast',
'2hr': '2hr_nowcast',
'24hr': '24hrs_forecast',
'24h': '24hrs_forecast',
'4d': '4days_outlook',
'hvr': 'heavy_rain_warning',
'uv': 'uvi',
'dz': 'earthquake',
'psi': 'psi_update',
'pm25': 'pm2.5_update'}例如：
查询PM2.5的值可以用如下网址（pretty可以去掉，只是为了打印得好看些）
https://bt201504.appspot.com/nea?dataset=pm25&pretty=1
{
"channel": {
"source": "Airviro",
"item": {
"region": [
{
"latitude": "1.41803",
"record": {
"reading": {
"type": "PM25_RGN_1HR",
"value": "8"
}
},
"id": "rNO",
"longitude": "103.82000"
},
{
"latitude": "1.35735",
"record": {
"reading": {
"type": "PM25_RGN_1HR",
"value": "13"
}
},
"id": "rCE",
"longitude": "103.82000"
},
{
"latitude": "1.35735",
"record": {
"reading": {
"type": "PM25_RGN_1HR",
"value": "22"
}
},
"id": "rEA",
"longitude": "103.94000"
},
{
"latitude": "1.35735",
"record": {
"reading": {
"type": "PM25_RGN_1HR",
"value": "6"
}
},
"id": "rWE",
"longitude": "103.70000"
},
{
"latitude": "1.29587",
"record": {
"reading": {
"type": "PM25_RGN_1HR",
"value": "19"
}
},
"id": "rSO",
"longitude": "103.82000"
}
]
},
"title": "PM2.5 Update"
}
}大雨预警还有一个直接的接口 https://bt201504.appspot.com/hvr.json，这个返回的结果中不包含base64之后的卫星图像和降雨分布，需要的话用上面的接口查询。
另外，PM2.5和PSI还有历史数据可供程序，接口如下：
https://bt201504.appspot.com/air?dataset=[pm25 or psi]&start=[%Y-%m-%d]&pretty=1
例如：今天的PM2.5记录可以用如下参数查询：
https://bt201504.appspot.com/air?dataset=pm25&pretty=1&start=2016-04-05
结果是这样的：
{
"count": 12,
"data": [
{
"event_time": "2016-04-05T11:00:00.000Z",
"regions": {
"central": 13,
"east": 22,
"north": 8,
"south": 19,
"west": 6
},
"timestamp": "2016-04-05T11:00:00.000Z"
},
{
"event_time": "2016-04-05T10:00:00.000Z",
"regions": {
"central": 22,
"east": 14,
"north": 20,
"south": 18,
"west": 18
},
"timestamp": "2016-04-05T10:00:00.000Z"
},
{
"event_time": "2016-04-05T09:00:00.000Z",
"regions": {
"central": 15,
"east": 17,
"north": 10,
"south": 19,
"west": 28
},
"timestamp": "2016-04-05T09:00:00.000Z"
},
{
"event_time": "2016-04-05T08:00:00.000Z",
"regions": {
"central": 10,
"east": 20,
"north": 9,
"south": 25,
"west": 28
},
"timestamp": "2016-04-05T08:00:00.000Z"
},
{
"event_time": "2016-04-05T07:00:00.000Z",
"regions": {
"central": 14,
"east": 11,
"north": 7,
"south": 15,
"west": 19
},
"timestamp": "2016-04-05T07:00:00.000Z"
},
{
"event_time": "2016-04-05T06:00:00.000Z",
"regions": {
"central": 13,
"east": 14,
"north": 13,
"south": 16,
"west": 16
},
"timestamp": "2016-04-05T06:00:00.000Z"
},
{
"event_time": "2016-04-05T05:00:00.000Z",
"regions": {
"central": 16,
"east": 19,
"north": 9,
"south": 16,
"west": 15
},
"timestamp": "2016-04-05T05:00:00.000Z"
},
{
"event_time": "2016-04-05T04:00:00.000Z",
"regions": {
"central": 14,
"east": 15,
"north": 10,
"south": 11,
"west": 17
},
"timestamp": "2016-04-05T04:00:00.000Z"
},
{
"event_time": "2016-04-05T03:00:00.000Z",
"regions": {
"central": 14,
"east": 16,
"north": 21,
"south": 10,
"west": 31
},
"timestamp": "2016-04-05T03:00:00.000Z"
},
{
"event_time": "2016-04-05T02:00:00.000Z",
"regions": {
"central": 26,
"east": 17,
"north": 18,
"south": 25,
"west": 22
},
"timestamp": "2016-04-05T02:00:00.000Z"
},
{
"event_time": "2016-04-05T01:00:00.000Z",
"regions": {
"central": 21,
"east": 12,
"north": 19,
"south": 16,
"west": 24
},
"timestamp": "2016-04-05T01:00:00.000Z"
},
{
"event_time": "2016-04-05T00:00:00.000Z",
"regions": {
"central": 15,
"east": 9,
"north": 10,
"south": 10,
"west": 9
},
"timestamp": "2016-04-05T00:00:00.000Z"
}
],
"end": "2016-04-05T15:59:59.999Z",
"start": "2016-04-05T00:00:00.000Z",
"status": true
}有兴趣玩玩新加坡天气数据的小伙伴们可以起来嗨喽！
