---

title: ‘（五）GeosparkViz 可視化’
date: 2029-01-01 00:00:00
tags: [scala]
published: true
hideInList: false
#feature: /post-images/hello-gridea.png
isTop: false

---

上一節(jié)我們使用GeoSpark SQL簡(jiǎn)單進(jìn)行了空間的一些操作，本節(jié)我們繼續(xù)利用GeoSpark SQL以及GeoSparkViz將我們的結(jié)果進(jìn)行渲染展示。下面是我們今天要用到的新的6個(gè)SQL函數(shù)

GeosparkViz 可視化

GeoSparkViz 可視化

上一節(jié)我們使用GeoSpark SQL簡(jiǎn)單進(jìn)行了空間的一些操作，本節(jié)我們繼續(xù)利用GeoSpark SQL以及GeoSparkViz將我們的結(jié)果進(jìn)行渲染展示。下面是我們今天要用到的新的6個(gè)SQL函數(shù)：

CAST ( expression AS type ):這是PostGresql原生提供的函數(shù)，用于字段類型轉(zhuǎn)換

ST_Point (X:decimal, Y:decimal, UUID1, UUID2, ...)：從給定的X和Y生成一個(gè)點(diǎn)要素。

ST_Pixelize (A:geometry, ResolutionX:int, ResolutionY:int, Boundary:geometry)：將給定的geometry轉(zhuǎn)為圖片中的像素。

ST_Colorize (weight:Double, maxWeight:Double, mandatory color: string (Optional))：給定一個(gè)像素的權(quán)重，返回相應(yīng)的顏色。權(quán)重可以是任意含義的數(shù)值，比如溫度和濕度等觀測(cè)值。

ST_Render (A:pixel, B:color)：給定像素和顏色，生成Java中的BufferedImage對(duì)象，代表了一副圖片。

ST_Envelope_Aggr (A:geometryColumn)：返回A的最小外接矩形

加載數(shù)據(jù)

SparkSession spark = SparkSession.builder().config("spark.serializer","org.apache.spark.serializer.KryoSerializer").config("spark.kryo.registrator", "org.datasyslab.geospark.serde.GeoSparkKryoRegistrator").master("local[*]").appName("Learn05").getOrCreate();GeoSparkSQLRegistrator.registerAll(spark); GeoSparkVizRegistrator.registerAll(spark);// 加載CSV文件,CSV中的第一列為WKT格式 String inputCSVPath = Learn04.class.getResource("/checkin.csv").toString(); Dataset rawDF = spark.read().format("csv").option("delimiter", ",").option("header", "false").load(inputCSVPath); rawDF.createOrReplaceTempView("pointtable");

構(gòu)建幾何圖形（Geometry）

// 創(chuàng)建Geometry列 String sqlText = "select ST_Point(cast(_c0 as Decimal(24,20)), cast(_c1 as Decimal(24,20))) AS shape, _c2 from pointtable"; Dataset spatialDf = spark.sql(sqlText); spatialDf.createOrReplaceTempView("pointtable"); spatialDf.show(); +--------------------+----------+ | shape| _c2| +--------------------+----------+ |POINT (-88.331492...| hotel| |POINT (-88.175933...| gas| |POINT (-88.388954...| bar| |POINT (-88.221102...|restaurant| +--------------------+----------+

渲染

接下來(lái)就要對(duì)上面四個(gè)點(diǎn)進(jìn)行展示，首先我們要將地理位置轉(zhuǎn)為屏幕上的像素坐標(biāo)，首先使用ST_Pixelize，但是ST_Pixelize要求首先提供一個(gè)邊界范圍，所以我們先用ST_Envelope_Aggr來(lái)生成bound。

sqlText = "SELECT ST_Envelope_Aggr(shape) as bound FROM pointtable"; spatialDf = spark.sql(sqlText); spatialDf.createOrReplaceTempView("boundtable"); spatialDf.show(); +--------------------+ | bound| +--------------------+ |POLYGON ((-88.388...| +--------------------+

生成像素

sqlText = "SELECT pixel, shape FROM pointtable " +"LATERAL VIEW ST_Pixelize(ST_Transform(shape, 'epsg:4326','epsg:3857'), 256, 256, (SELECT ST_Transform(bound, 'epsg:4326','epsg:3857') FROM boundtable)) AS pixel"; spatialDf = spark.sql(sqlText); spatialDf.createOrReplaceTempView("pixels"); spatialDf.show(); +--------------------+--------------------+ | pixel| shape| +--------------------+--------------------+ |Pixel(x=69.0, y=0...|POINT (-88.331492...| |Pixel(x=255.0, y=...|POINT (-88.175933...| |Pixel(x=0.0, y=23...|POINT (-88.388954...| |Pixel(x=201.0, y=...|POINT (-88.221102...| +--------------------+--------------------+

生成顏色

本次僅僅是展示點(diǎn)，因此可以給每個(gè)點(diǎn)固定的顏色，所以這里權(quán)重就填1。

sqlText = "SELECT ST_Colorize(1, 1, 'red') as color, pixel FROM pixels"; spatialDf = spark.sql(sqlText); spatialDf.createOrReplaceTempView("pixelaggregates"); spatialDf.show(false); +------+----------------------------------------------------------------------------+ |color |pixel | +------+----------------------------------------------------------------------------+ |-65536|Pixel(x=69.0, y=0.0, width=256, height=256, isDuplicate=false, tileId=-1) | |-65536|Pixel(x=255.0, y=255.0, width=256, height=256, isDuplicate=false, tileId=-1)| |-65536|Pixel(x=0.0, y=230.0, width=256, height=256, isDuplicate=false, tileId=-1) | |-65536|Pixel(x=201.0, y=186.0, width=256, height=256, isDuplicate=false, tileId=-1)| +------+----------------------------------------------------------------------------+

渲染

在得到像素點(diǎn)和顏色后，就可以調(diào)用ST_Render生成圖片了

sqlText = "SELECT ST_Render(pixel, color) AS image, (SELECT ST_AsText(bound) FROM boundtable) AS boundary FROM pixelaggregates" ; spatialDf = spark.sql(sqlText); spatialDf.createOrReplaceTempView("images"); spatialDf.show(); +--------------------------+--------------------+ | image| boundary| +--------------------------+--------------------+ |Image(width=256height=256)|POLYGON ((-88.388...| +--------------------------+--------------------+

保存圖片

Dataset<org.apache.spark.sql.Row> images = spark.table("images"); Row[] take = (Row[])images.take(1); ImageSerializableWrapper image = (ImageSerializableWrapper)take[0].get(0); new ImageGenerator().SaveRasterImageAsLocalFile(image.getImage(),System.getProperty("user.home") + "/point", ImageType.PNG);

參考鏈接：https://www.jianshu.com/p/9a0e7b151f49

總結(jié)

以上是生活随笔為你收集整理的GeosparkViz 可视化的全部?jī)?nèi)容，希望文章能夠幫你解決所遇到的問(wèn)題。

如果覺(jué)得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。