💖💖作者：计算机毕业设计江挽 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
基于大数据的江西景区数据可视化分析系统介绍
旅游业数字化转型：基于Hadoop的江西景区大数据可视化分析系统是一个运用现代大数据技术构建的综合性数据分析平台，该系统以Hadoop分布式存储架构为基础，结合Spark大数据计算引擎，对江西省内各大景区的海量数据进行深度挖掘和智能分析。系统采用SpringBoot作为后端开发框架，配合MySQL数据库进行结构化数据存储，前端运用Vue框架结合ElementUI组件库和Echarts可视化图表库，为用户提供直观友好的操作界面和丰富的数据展示效果。平台主要包含系统首页、我的信息、系统管理和数据可视化四大核心模块，通过Spark SQL进行复杂数据查询和计算，利用Pandas和NumPy进行数据预处理和统计分析，能够实现对景区游客流量、消费行为、季节性变化、地域分布等多维度数据的实时监控和趋势预测，为景区管理者提供科学的决策支持，同时为游客提供个性化的旅游推荐服务，有效推动江西旅游业的数字化升级和智慧化发展。
基于大数据的江西景区数据可视化分析系统演示视频
基于大数据的江西景区数据可视化分析系统演示图片
基于大数据的江西景区数据可视化分析系统代码展示
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.springframework.stereotype.Service;
import java.util.*;
@Service
public class BigDataAnalysisService {
private SparkSession spark = SparkSession.builder()
.appName("JiangxiScenicDataAnalysis")
.master("local[*]")
.config("spark.sql.adaptive.enabled", "true")
.config("spark.sql.adaptive.coalescePartitions.enabled", "true")
.getOrCreate();
public Map<String, Object> processScenicAreaData(String dataPath) {
Dataset<Row> rawData = spark.read()
.option("header", "true")
.option("inferSchema", "true")
.csv(dataPath);
rawData.createOrReplaceTempView("scenic_data");
Dataset<Row> processedData = spark.sql(
"SELECT scenic_name, visitor_count, revenue, visit_date, " +
"MONTH(visit_date) as visit_month, " +
"DAYOFWEEK(visit_date) as day_of_week, " +
"CASE WHEN visitor_count > 1000 THEN 'high' " +
"WHEN visitor_count > 500 THEN 'medium' " +
"ELSE 'low' END as traffic_level " +
"FROM scenic_data WHERE visit_date >= date_sub(current_date(), 365)"
);
long totalRecords = processedData.count();
double avgVisitorCount = processedData.agg(functions.avg("visitor_count")).first().getDouble(0);
double totalRevenue = processedData.agg(functions.sum("revenue")).first().getDouble(0);
Dataset<Row> monthlyStats = processedData.groupBy("visit_month")
.agg(functions.sum("visitor_count").alias("monthly_visitors"),
functions.avg("revenue").alias("avg_monthly_revenue"))
.orderBy("visit_month");
List<Row> monthlyData = monthlyStats.collectAsList();
Dataset<Row> scenicRanking = processedData.groupBy("scenic_name")
.agg(functions.sum("visitor_count").alias("total_visitors"),
functions.sum("revenue").alias("total_revenue"))
.orderBy(functions.desc("total_visitors"))
.limit(10);
List<Row> rankingData = scenicRanking.collectAsList();
Map<String, Object> result = new HashMap<>();
result.put("totalRecords", totalRecords);
result.put("avgVisitorCount", avgVisitorCount);
result.put("totalRevenue", totalRevenue);
result.put("monthlyStats", monthlyData);
result.put("scenicRanking", rankingData);
result.put("processTime", System.currentTimeMillis());
return result;
}
public Map<String, Object> generateVisualizationData(String scenicName, String timeRange) {
String timeCondition = "";
switch(timeRange) {
case "week":
timeCondition = "visit_date >= date_sub(current_date(), 7)";
break;
case "month":
timeCondition = "visit_date >= date_sub(current_date(), 30)";
break;
case "year":
timeCondition = "visit_date >= date_sub(current_date(), 365)";
break;
default:
timeCondition = "visit_date >= date_sub(current_date(), 30)";
}
String querySQL = String.format(
"SELECT visit_date, visitor_count, revenue, weather_condition, " +
"temperature, humidity, " +
"LAG(visitor_count, 1) OVER (ORDER BY visit_date) as prev_day_visitors, " +
"LEAD(visitor_count, 1) OVER (ORDER BY visit_date) as next_day_visitors " +
"FROM scenic_data WHERE scenic_name = '%s' AND %s ORDER BY visit_date",
scenicName, timeCondition
);
Dataset<Row> timeSeriesData = spark.sql(querySQL);
Dataset<Row> trendAnalysis = spark.sql(String.format(
"SELECT visit_date, visitor_count, " +
"AVG(visitor_count) OVER (ORDER BY visit_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) as moving_avg, " +
"(visitor_count - LAG(visitor_count, 1) OVER (ORDER BY visit_date)) / " +
"LAG(visitor_count, 1) OVER (ORDER BY visit_date) * 100 as growth_rate " +
"FROM scenic_data WHERE scenic_name = '%s' AND %s ORDER BY visit_date",
scenicName, timeCondition
));
Dataset<Row> correlationData = spark.sql(String.format(
"SELECT weather_condition, " +
"AVG(visitor_count) as avg_visitors, " +
"COUNT(*) as days_count, " +
"STDDEV(visitor_count) as visitor_stddev " +
"FROM scenic_data WHERE scenic_name = '%s' AND %s " +
"GROUP BY weather_condition ORDER BY avg_visitors DESC",
scenicName, timeCondition
));
List<Row> timeSeriesList = timeSeriesData.collectAsList();
List<Row> trendList = trendAnalysis.collectAsList();
List<Row> correlationList = correlationData.collectAsList();
double maxVisitors = timeSeriesData.agg(functions.max("visitor_count")).first().getDouble(0);
double minVisitors = timeSeriesData.agg(functions.min("visitor_count")).first().getDouble(0);
double avgRevenue = timeSeriesData.agg(functions.avg("revenue")).first().getDouble(0);
Map<String, Object> visualData = new HashMap<>();
visualData.put("timeSeriesData", timeSeriesList);
visualData.put("trendAnalysis", trendList);
visualData.put("weatherCorrelation", correlationList);
visualData.put("maxVisitors", maxVisitors);
visualData.put("minVisitors", minVisitors);
visualData.put("avgRevenue", avgRevenue);
visualData.put("scenicName", scenicName);
visualData.put("timeRange", timeRange);
return visualData;
}
public Map<String, Object> calculateScenicStatistics(List<String> scenicNames, String startDate, String endDate) {
String scenicCondition = "'" + String.join("','", scenicNames) + "'";
String baseQuery = String.format(
"SELECT * FROM scenic_data WHERE scenic_name IN (%s) " +
"AND visit_date BETWEEN '%s' AND '%s'",
scenicCondition, startDate, endDate
);
Dataset<Row> filteredData = spark.sql(baseQuery);
filteredData.createOrReplaceTempView("filtered_scenic_data");
Dataset<Row> dailyStats = spark.sql(
"SELECT visit_date, " +
"SUM(visitor_count) as total_daily_visitors, " +
"SUM(revenue) as total_daily_revenue, " +
"COUNT(DISTINCT scenic_name) as active_scenic_count, " +
"AVG(visitor_count) as avg_scenic_visitors " +
"FROM filtered_scenic_data GROUP BY visit_date ORDER BY visit_date"
);
Dataset<Row> scenicComparison = spark.sql(
"SELECT scenic_name, " +
"SUM(visitor_count) as total_visitors, " +
"SUM(revenue) as total_revenue, " +
"AVG(visitor_count) as avg_daily_visitors, " +
"MAX(visitor_count) as peak_day_visitors, " +
"MIN(visitor_count) as lowest_day_visitors, " +
"COUNT(*) as operating_days " +
"FROM filtered_scenic_data GROUP BY scenic_name ORDER BY total_visitors DESC"
);
Dataset<Row> peakAnalysis = spark.sql(
"SELECT scenic_name, visit_date, visitor_count, " +
"ROW_NUMBER() OVER (PARTITION BY scenic_name ORDER BY visitor_count DESC) as visitor_rank, " +
"ROW_NUMBER() OVER (PARTITION BY scenic_name ORDER BY revenue DESC) as revenue_rank " +
"FROM filtered_scenic_data"
).filter("visitor_rank <= 3 OR revenue_rank <= 3");
Dataset<Row> weekdayAnalysis = spark.sql(
"SELECT DAYOFWEEK(visit_date) as day_of_week, " +
"CASE DAYOFWEEK(visit_date) " +
"WHEN 1 THEN 'Sunday' WHEN 2 THEN 'Monday' WHEN 3 THEN 'Tuesday' " +
"WHEN 4 THEN 'Wednesday' WHEN 5 THEN 'Thursday' WHEN 6 THEN 'Friday' " +
"WHEN 7 THEN 'Saturday' END as day_name, " +
"AVG(visitor_count) as avg_visitors, " +
"AVG(revenue) as avg_revenue, " +
"COUNT(*) as data_points " +
"FROM filtered_scenic_data GROUP BY DAYOFWEEK(visit_date) ORDER BY day_of_week"
);
long totalDataPoints = filteredData.count();
double overallAvgVisitors = filteredData.agg(functions.avg("visitor_count")).first().getDouble(0);
double overallTotalRevenue = filteredData.agg(functions.sum("revenue")).first().getDouble(0);
Map<String, Object> statistics = new HashMap<>();
statistics.put("dailyStatistics", dailyStats.collectAsList());
statistics.put("scenicComparison", scenicComparison.collectAsList());
statistics.put("peakAnalysis", peakAnalysis.collectAsList());
statistics.put("weekdayAnalysis", weekdayAnalysis.collectAsList());
statistics.put("totalDataPoints", totalDataPoints);
statistics.put("overallAvgVisitors", overallAvgVisitors);
statistics.put("overallTotalRevenue", overallTotalRevenue);
statistics.put("analysisTimeRange", startDate + " to " + endDate);
statistics.put("analyzedScenicAreas", scenicNames);
return statistics;
}
}
基于大数据的江西景区数据可视化分析系统文档展示
💖💖作者：计算机毕业设计江挽 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
