💖💖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
基于大数据的北京旅游景点可视化分析系统介绍
基于Hadoop的北京旅游景点可视化分析系统是一个综合运用大数据技术的智能分析平台，该系统充分利用Hadoop分布式存储和Spark大数据处理能力，对北京地区旅游景点进行深度数据挖掘与可视化展示。系统采用前后端分离架构，后端基于Spring Boot框架构建RESTful API服务，前端运用Vue框架配合ElementUI组件库和Echarts图表库实现用户交互界面，数据存储依托MySQL数据库管理结构化信息。系统核心功能涵盖北京旅游景点基础信息管理、热度与口碑智能分析、消费成本统计分析、空间地理分布可视化、主题特色深度挖掘等模块，通过Spark SQL进行复杂数据查询和统计计算，运用Pandas和NumPy进行数据预处理和数学运算。系统还提供可视化大屏功能，通过Echarts生成多维度图表展示分析结果，为旅游管理部门、景区运营商和游客提供数据支撑和决策参考，实现了传统旅游数据管理向智能化大数据分析的转型升级。
基于大数据的北京旅游景点可视化分析系统演示视频
基于大数据的北京旅游景点可视化分析系统演示图片
基于大数据的北京旅游景点可视化分析系统代码展示
spark = SparkSession.builder().appName("BeijingTourismAnalysis").config("spark.sql.adaptive.enabled", true).getOrCreate();
public AnalysisResult analyzeHotnessAndReputation(String timeRange, String region) {
Dataset<Row> reviewData = spark.sql("SELECT scenic_spot_id, rating, review_count, visit_date FROM tourism_reviews WHERE visit_date >= '" + timeRange + "'");
Dataset<Row> visitData = spark.sql("SELECT scenic_spot_id, visitor_count, search_volume FROM tourism_visits WHERE date_range = '" + timeRange + "'");
Dataset<Row> joinedData = reviewData.join(visitData, "scenic_spot_id");
Dataset<Row> hotnessScore = joinedData.groupBy("scenic_spot_id").agg(
sum("visitor_count").alias("total_visitors"),
avg("rating").alias("avg_rating"),
sum("review_count").alias("total_reviews"),
avg("search_volume").alias("avg_search")
);
Dataset<Row> normalizedData = hotnessScore.withColumn("hotness_index",
col("total_visitors").multiply(0.4).plus(col("avg_search").multiply(0.3)).plus(col("total_reviews").multiply(0.3)));
Dataset<Row> reputationData = normalizedData.withColumn("reputation_level",
when(col("avg_rating").geq(4.5), "excellent")
.when(col("avg_rating").geq(4.0), "good")
.when(col("avg_rating").geq(3.5), "average")
.otherwise("poor"));
List<Row> results = reputationData.orderBy(desc("hotness_index")).limit(20).collectAsList();
AnalysisResult analysisResult = new AnalysisResult();
for (Row row : results) {
ScenicSpotAnalysis spot = new ScenicSpotAnalysis();
spot.setScenicSpotId(row.getString(0));
spot.setTotalVisitors(row.getLong(1));
spot.setAvgRating(row.getDouble(2));
spot.setHotnessIndex(row.getDouble(6));
spot.setReputationLevel(row.getString(7));
analysisResult.addSpotAnalysis(spot);
}
return analysisResult;
}
public CostAnalysisResult analyzeCostAndConsumption(String seasonType, String spotCategory) {
Dataset<Row> consumptionData = spark.sql("SELECT scenic_spot_id, ticket_price, avg_meal_cost, avg_transport_cost, avg_shopping_cost, visitor_count FROM tourism_consumption WHERE season = '" + seasonType + "'");
Dataset<Row> categoryData = spark.sql("SELECT scenic_spot_id, category, difficulty_level FROM scenic_spots WHERE category LIKE '%" + spotCategory + "%'");
Dataset<Row> mergedData = consumptionData.join(categoryData, "scenic_spot_id");
Dataset<Row> costCalculation = mergedData.withColumn("total_avg_cost",
col("ticket_price").plus(col("avg_meal_cost")).plus(col("avg_transport_cost")).plus(col("avg_shopping_cost")));
Dataset<Row> costByCategory = costCalculation.groupBy("category").agg(
avg("total_avg_cost").alias("avg_total_cost"),
avg("ticket_price").alias("avg_ticket_price"),
avg("avg_meal_cost").alias("avg_meal_cost"),
sum("visitor_count").alias("category_visitors"),
count("scenic_spot_id").alias("spot_count"));
Dataset<Row> costDistribution = costCalculation.withColumn("cost_level",
when(col("total_avg_cost").leq(100), "budget")
.when(col("total_avg_cost").leq(300), "moderate")
.when(col("total_avg_cost").leq(500), "premium")
.otherwise("luxury"));
Dataset<Row> distributionStats = costDistribution.groupBy("cost_level").agg(
count("scenic_spot_id").alias("spot_count"),
avg("total_avg_cost").alias("avg_cost_in_level"));
List<Row> categoryResults = costByCategory.collectAsList();
List<Row> distributionResults = distributionStats.collectAsList();
CostAnalysisResult result = new CostAnalysisResult();
for (Row row : categoryResults) {
CategoryCostInfo info = new CategoryCostInfo();
info.setCategory(row.getString(0));
info.setAvgTotalCost(row.getDouble(1));
info.setAvgTicketPrice(row.getDouble(2));
info.setCategoryVisitors(row.getLong(4));
result.addCategoryInfo(info);
}
return result;
}
public GeoDistributionResult analyzeGeographicDistribution(String districtFilter, String radiusKm) {
Dataset<Row> locationData = spark.sql("SELECT scenic_spot_id, latitude, longitude, district, address FROM scenic_locations");
Dataset<Row> popularityData = spark.sql("SELECT scenic_spot_id, visitor_density, accessibility_score FROM tourism_popularity");
Dataset<Row> geoData = locationData.join(popularityData, "scenic_spot_id");
Dataset<Row> filteredData = geoData.filter(col("district").like("%" + districtFilter + "%"));
Dataset<Row> districtStats = filteredData.groupBy("district").agg(
count("scenic_spot_id").alias("spot_count"),
avg("visitor_density").alias("avg_density"),
avg("accessibility_score").alias("avg_accessibility"));
Dataset<Row> densityAnalysis = filteredData.withColumn("density_level",
when(col("visitor_density").geq(1000), "high_density")
.when(col("visitor_density").geq(500), "medium_density")
.otherwise("low_density"));
Dataset<Row> clusterAnalysis = densityAnalysis.withColumn("lat_group",
floor(col("latitude").multiply(100)).divide(100))
.withColumn("lng_group",
floor(col("longitude").multiply(100)).divide(100));
Dataset<Row> spatialClusters = clusterAnalysis.groupBy("lat_group", "lng_group", "density_level").agg(
count("scenic_spot_id").alias("cluster_size"),
avg("visitor_density").alias("cluster_avg_density"));
List<Row> districtResults = districtStats.orderBy(desc("spot_count")).collectAsList();
List<Row> clusterResults = spatialClusters.filter(col("cluster_size").geq(2)).collectAsList();
GeoDistributionResult result = new GeoDistributionResult();
for (Row row : districtResults) {
DistrictInfo district = new DistrictInfo();
district.setDistrictName(row.getString(0));
district.setSpotCount(row.getLong(1));
district.setAvgDensity(row.getDouble(2));
district.setAvgAccessibility(row.getDouble(3));
result.addDistrictInfo(district);
}
return result;
}
基于大数据的北京旅游景点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
