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新加坡NEA的JSON API

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!

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