💖💖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
基于大数据的旅游上榜景点数据可视化分析系统介绍
基于大数据的旅游上榜景点数据可视化分析系统是一个综合运用现代大数据技术栈的数据分析平台，该系统采用Hadoop分布式存储架构结合Spark大数据处理引擎，实现对海量旅游景点数据的高效采集、存储和分析处理。系统后端基于Python的Django框架构建RESTful API接口，前端采用Vue.js结合ElementUI组件库打造现代化的用户界面，通过Echarts图表库实现丰富的数据可视化效果。系统核心功能涵盖旅游景点基础信息管理、多维度统计分析、动态图表展示等模块，能够对不同地区、不同类型的上榜景点进行深度数据挖掘，生成热门景点排行、地域分布统计、游客满意度分析等多类型报表。通过Spark SQL进行大数据查询优化，结合Pandas和NumPy进行数据清洗与预处理，确保分析结果的准确性和实时性，为旅游行业决策提供科学的数据支撑。
基于大数据的旅游上榜景点数据可视化分析系统演示视频
基于大数据的旅游上榜景点数据可视化分析系统演示图片
基于大数据的旅游上榜景点数据可视化分析系统 代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
spark = SparkSession.builder.appName("TourismBigDataAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@csrf_exempt
def scenic_spot_data_processing(request):
raw_data_path = "hdfs://localhost:9000/tourism_data/scenic_spots/"
df = spark.read.option("header", "true").option("inferSchema", "true").csv(raw_data_path)
cleaned_df = df.filter(col("spot_name").isNotNull() & col("location").isNotNull() & col("rating").isNotNull())
cleaned_df = cleaned_df.withColumn("rating", when(col("rating") > 5.0, 5.0).when(col("rating") < 0.0, 0.0).otherwise(col("rating")))
cleaned_df = cleaned_df.withColumn("visitor_count", when(col("visitor_count") < 0, 0).otherwise(col("visitor_count")))
processed_df = cleaned_df.withColumn("popularity_score", col("rating") * 0.4 + (col("visitor_count") / 10000) * 0.6)
processed_df = processed_df.withColumn("category", when(col("spot_type").contains("自然"), "自然景观").when(col("spot_type").contains("文化"), "文化景观").when(col("spot_type").contains("主题"), "主题公园").otherwise("其他类型"))
processed_df = processed_df.withColumn("level_rank", when(col("level") == "5A", 5).when(col("level") == "4A", 4).when(col("level") == "3A", 3).otherwise(1))
aggregated_df = processed_df.groupBy("province", "category").agg(count("spot_name").alias("spot_count"), avg("rating").alias("avg_rating"), sum("visitor_count").alias("total_visitors"), avg("popularity_score").alias("avg_popularity"))
final_result = aggregated_df.orderBy(desc("avg_popularity")).limit(100)
pandas_df = final_result.toPandas()
result_data = pandas_df.to_dict('records')
processed_df.write.mode("overwrite").option("header", "true").csv("hdfs://localhost:9000/tourism_data/processed_spots/")
spark.sql("CREATE OR REPLACE TEMPORARY VIEW scenic_spots_view AS SELECT * FROM processed_spots")
return JsonResponse({"status": "success", "processed_count": processed_df.count(), "top_spots": result_data[:20]})
@csrf_exempt
def tourism_statistics_analysis(request):
spots_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/tourism_data/processed_spots/")
monthly_stats = spots_df.withColumn("visit_month", month(col("last_visit_date"))).groupBy("visit_month", "province").agg(sum("visitor_count").alias("monthly_visitors"), avg("rating").alias("monthly_avg_rating"), count("spot_name").alias("active_spots"))
seasonal_analysis = monthly_stats.withColumn("season", when(col("visit_month").isin([12, 1, 2]), "冬季").when(col("visit_month").isin([3, 4, 5]), "春季").when(col("visit_month").isin([6, 7, 8]), "夏季").otherwise("秋季"))
seasonal_stats = seasonal_analysis.groupBy("season", "province").agg(sum("monthly_visitors").alias("seasonal_visitors"), avg("monthly_avg_rating").alias("seasonal_rating"))
regional_ranking = spots_df.groupBy("province").agg(count("spot_name").alias("total_spots"), avg("rating").alias("province_avg_rating"), sum("visitor_count").alias("province_total_visitors"), avg("popularity_score").alias("province_popularity"))
regional_ranking = regional_ranking.withColumn("comprehensive_score", col("province_avg_rating") * 0.3 + (col("province_total_visitors") / 1000000) * 0.4 + col("province_popularity") * 0.3)
top_provinces = regional_ranking.orderBy(desc("comprehensive_score")).limit(10)
category_performance = spots_df.groupBy("category").agg(count("spot_name").alias("category_count"), avg("rating").alias("category_rating"), sum("visitor_count").alias("category_visitors"), avg("popularity_score").alias("category_popularity"))
growth_trend = spots_df.withColumn("visit_year", year(col("last_visit_date"))).groupBy("visit_year").agg(sum("visitor_count").alias("yearly_visitors"), avg("rating").alias("yearly_rating"))
growth_trend = growth_trend.withColumn("growth_rate", (col("yearly_visitors") - lag(col("yearly_visitors")).over(Window.orderBy("visit_year"))) / lag(col("yearly_visitors")).over(Window.orderBy("visit_year")) * 100)
correlation_analysis = spots_df.select(corr("rating", "visitor_count").alias("rating_visitor_corr"), corr("level_rank", "popularity_score").alias("level_popularity_corr"))
seasonal_pandas = seasonal_stats.toPandas()
regional_pandas = top_provinces.toPandas()
category_pandas = category_performance.toPandas()
trend_pandas = growth_trend.toPandas()
return JsonResponse({"status": "success", "seasonal_data": seasonal_pandas.to_dict('records'), "regional_ranking": regional_pandas.to_dict('records'), "category_analysis": category_pandas.to_dict('records'), "growth_trend": trend_pandas.to_dict('records')})
@csrf_exempt
def data_visualization_generator(request):
analysis_type = request.POST.get('analysis_type', 'comprehensive')
spots_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/tourism_data/processed_spots/")
if analysis_type == "geographic_distribution":
geo_data = spots_df.groupBy("province", "city").agg(count("spot_name").alias("spot_count"), avg("rating").alias("avg_rating"), sum("visitor_count").alias("total_visitors"))
geo_data = geo_data.withColumn("bubble_size", col("total_visitors") / 10000)
geo_result = geo_data.orderBy(desc("spot_count")).toPandas()
chart_data = {"type": "geo_map", "provinces": geo_result['province'].tolist(), "spot_counts": geo_result['spot_count'].tolist(), "ratings": geo_result['avg_rating'].tolist(), "bubble_sizes": geo_result['bubble_size'].tolist()}
elif analysis_type == "rating_distribution":
rating_bins = spots_df.withColumn("rating_range", when(col("rating") < 2.0, "1-2分").when(col("rating") < 3.0, "2-3分").when(col("rating") < 4.0, "3-4分").when(col("rating") < 4.5, "4-4.5分").otherwise("4.5-5分"))
rating_stats = rating_bins.groupBy("rating_range").agg(count("spot_name").alias("count"), avg("visitor_count").alias("avg_visitors"))
rating_result = rating_stats.orderBy("rating_range").toPandas()
chart_data = {"type": "bar_chart", "categories": rating_result['rating_range'].tolist(), "counts": rating_result['count'].tolist(), "avg_visitors": rating_result['avg_visitors'].tolist()}
elif analysis_type == "popularity_trend":
trend_data = spots_df.withColumn("popularity_level", when(col("popularity_score") > 4.0, "极高人气").when(col("popularity_score") > 3.0, "高人气").when(col("popularity_score") > 2.0, "中等人气").otherwise("一般人气"))
popularity_stats = trend_data.groupBy("category", "popularity_level").agg(count("spot_name").alias("spot_count"))
popularity_pivot = popularity_stats.groupBy("category").pivot("popularity_level").sum("spot_count").fillna(0)
trend_result = popularity_pivot.toPandas()
chart_data = {"type": "stacked_bar", "categories": trend_result['category'].tolist(), "extreme_high": trend_result.get('极高人气', [0]*len(trend_result)).tolist(), "high": trend_result.get('高人气', [0]*len(trend_result)).tolist(), "medium": trend_result.get('中等人气', [0]*len(trend_result)).tolist(), "normal": trend_result.get('一般人气', [0]*len(trend_result)).tolist()}
comprehensive_stats = spots_df.agg(count("spot_name").alias("total_spots"), avg("rating").alias("overall_rating"), sum("visitor_count").alias("total_visitors"), max("popularity_score").alias("max_popularity"))
stats_result = comprehensive_stats.collect()[0]
summary_data = {"total_spots": stats_result['total_spots'], "overall_rating": round(stats_result['overall_rating'], 2), "total_visitors": stats_result['total_visitors'], "max_popularity": round(stats_result['max_popularity'], 2)}
return JsonResponse({"status": "success", "chart_data": chart_data, "summary": summary_data, "analysis_type": analysis_type})
基于大数据的旅游上榜景点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
