【大数据】旅游景点数据分析与可视化系统计算机毕业设计项目 Hadoop+Spark环境配置数据科学与大数据技术附源码+文档+讲解

前言

💖💖作者：计算机程序员小杨 💙💙个人简介：我是一名计算机相关专业的从业者，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。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。热爱技术，喜欢钻研新工具和框架，也乐于通过代码解决实际问题，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！ 💕💕文末获取源码联系计算机程序员小杨 💜💜 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计算机毕业设计选题 💜💜

一.开发工具简介

大数据框架：Hadoop+Spark（本次没用Hive，支持定制）开发语言：Python+Java（两个版本都支持）后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持）前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery 详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy 数据库：MySQL

二.系统内容简介

《旅游景点数据分析与可视化系统》是一个基于大数据技术构建的旅游行业数据分析平台，采用Hadoop分布式存储和Spark计算引擎处理海量旅游景点数据。系统以Python为核心开发语言，后端采用Django框架构建RESTful API服务，前端基于Vue.js结合ElementUI组件库和Echarts可视化库实现用户交互界面。在数据处理层面，系统运用Spark SQL进行分布式数据查询，结合Pandas和NumPy进行数据清洗与统计分析，数据持久化存储在MySQL数据库中。系统具备完整的用户权限管理体系，支持旅游景点基础信息的增删改查操作，能够从宏观分布、商业价值、区域特征、游客偏好等多个维度对旅游数据进行深度挖掘分析，并通过可视化大屏以图表形式直观展示分析结果，为旅游行业的决策提供数据支撑。

三.系统功能演示

旅游景点数据分析与可视化系统

四.系统界面展示

在这里插入图片描述

五.系统源码展示


from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, sum, desc, asc
from django.http import JsonResponse
from django.views import View
import pandas as pd
import numpy as np
from .models import TouristSpot, VisitorData, RegionInfo
import json

spark = SparkSession.builder.appName("TouristDataAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()

class MacroDistributionAnalysis(View):
    def post(self, request):
        tourist_spots = list(TouristSpot.objects.values('name', 'province', 'city', 'category', 'rating', 'visitor_count'))
        spots_df = spark.createDataFrame(tourist_spots)
        province_distribution = spots_df.groupBy('province').agg(count('*').alias('spot_count'), avg('rating').alias('avg_rating'), sum('visitor_count').alias('total_visitors')).orderBy(desc('spot_count'))
        category_distribution = spots_df.groupBy('category').agg(count('*').alias('spot_count'), avg('visitor_count').alias('avg_visitors')).orderBy(desc('avg_visitors'))
        rating_distribution = spots_df.groupBy('rating').agg(count('*').alias('count')).orderBy(asc('rating'))
        province_data = [row.asDict() for row in province_distribution.collect()]
        category_data = [row.asDict() for row in category_distribution.collect()]
        rating_data = [row.asDict() for row in rating_distribution.collect()]
        city_ranking = spots_df.groupBy('city').agg(count('*').alias('spot_count'), sum('visitor_count').alias('total_visitors')).orderBy(desc('total_visitors')).limit(20)
        city_data = [row.asDict() for row in city_ranking.collect()]
        density_analysis = spots_df.groupBy('province', 'city').agg(count('*').alias('density')).orderBy(desc('density'))
        density_data = [row.asDict() for row in density_analysis.collect()]
        visitor_trend = spots_df.select('visitor_count').rdd.map(lambda x: x[0]).collect()
        visitor_array = np.array(visitor_trend)
        percentiles = np.percentile(visitor_array, [25, 50, 75, 90, 95])
        trend_analysis = {'mean': float(np.mean(visitor_array)), 'median': float(np.median(visitor_array)), 'std': float(np.std(visitor_array)), 'percentiles': percentiles.tolist()}
        return JsonResponse({'province_distribution': province_data, 'category_distribution': category_data, 'rating_distribution': rating_data, 'city_ranking': city_data, 'density_analysis': density_data, 'trend_analysis': trend_analysis})

class BusinessValueAnalysis(View):
    def post(self, request):
        request_data = json.loads(request.body)
        time_range = request_data.get('time_range', 30)
        visitor_data = list(VisitorData.objects.filter(visit_date__gte=timezone.now() - timedelta(days=time_range)).values('spot_id', 'visitor_count', 'revenue', 'visit_date'))
        spots_data = list(TouristSpot.objects.values('id', 'name', 'ticket_price', 'operating_cost', 'category'))
        visitor_df = spark.createDataFrame(visitor_data)
        spots_df = spark.createDataFrame(spots_data)
        business_df = visitor_df.join(spots_df, visitor_df.spot_id == spots_df.id, 'inner')
        revenue_analysis = business_df.groupBy('spot_id', 'name').agg(sum('revenue').alias('total_revenue'), sum('visitor_count').alias('total_visitors'), avg('revenue').alias('avg_daily_revenue'))
        profit_analysis = business_df.withColumn('daily_profit', col('revenue') - col('operating_cost')).groupBy('spot_id', 'name').agg(sum('daily_profit').alias('total_profit'), avg('daily_profit').alias('avg_daily_profit'))
        roi_analysis = revenue_analysis.join(profit_analysis, 'spot_id').withColumn('roi_ratio', col('total_profit') / col('total_revenue') * 100).orderBy(desc('roi_ratio'))
        category_performance = business_df.groupBy('category').agg(sum('revenue').alias('category_revenue'), avg('visitor_count').alias('avg_visitors'), count('*').alias('spot_count')).withColumn('revenue_per_spot', col('category_revenue') / col('spot_count'))
        visitor_value = business_df.withColumn('per_visitor_revenue', col('revenue') / col('visitor_count')).groupBy('spot_id', 'name').agg(avg('per_visitor_revenue').alias('avg_visitor_value')).orderBy(desc('avg_visitor_value'))
        seasonal_revenue = business_df.withColumn('month', month(col('visit_date'))).groupBy('month').agg(sum('revenue').alias('monthly_revenue'), avg('visitor_count').alias('avg_monthly_visitors'))
        peak_performance = business_df.orderBy(desc('revenue')).limit(10)
        market_share = revenue_analysis.withColumn('market_share', col('total_revenue') / revenue_analysis.agg(sum('total_revenue')).collect()[0][0] * 100)
        roi_data = [row.asDict() for row in roi_analysis.collect()]
        category_data = [row.asDict() for row in category_performance.collect()]
        visitor_value_data = [row.asDict() for row in visitor_value.collect()]
        seasonal_data = [row.asDict() for row in seasonal_revenue.collect()]
        peak_data = [row.asDict() for row in peak_performance.collect()]
        market_data = [row.asDict() for row in market_share.collect()]
        return JsonResponse({'roi_analysis': roi_data, 'category_performance': category_data, 'visitor_value_analysis': visitor_value_data, 'seasonal_trends': seasonal_data, 'peak_performers': peak_data, 'market_share': market_data})

class VisitorPreferenceAnalysis(View):
    def post(self, request):
        request_data = json.loads(request.body)
        analysis_type = request_data.get('analysis_type', 'comprehensive')
        visitor_data = list(VisitorData.objects.select_related('spot').values('spot__name', 'spot__category', 'spot__rating', 'spot__province', 'visitor_age_group', 'visitor_gender', 'visit_duration', 'satisfaction_score', 'repeat_visit'))
        spots_info = list(TouristSpot.objects.values('id', 'name', 'category', 'rating', 'ticket_price', 'features'))
        visitor_df = spark.createDataFrame(visitor_data)
        spots_df = spark.createDataFrame(spots_info)
        age_preference = visitor_df.groupBy('visitor_age_group', 'spot__category').agg(count('*').alias('visit_count'), avg('satisfaction_score').alias('avg_satisfaction')).orderBy(desc('visit_count'))
        gender_preference = visitor_df.groupBy('visitor_gender', 'spot__category').agg(count('*').alias('visit_count'), avg('visit_duration').alias('avg_duration')).orderBy(desc('visit_count'))
        rating_preference = visitor_df.groupBy('spot__rating').agg(count('*').alias('visitor_count'), avg('satisfaction_score').alias('avg_satisfaction'), avg('visit_duration').alias('avg_duration')).orderBy(asc('spot__rating'))
        repeat_analysis = visitor_df.filter(col('repeat_visit') == True).groupBy('spot__name', 'spot__category').agg(count('*').alias('repeat_count')).orderBy(desc('repeat_count'))
        satisfaction_analysis = visitor_df.groupBy('spot__category').agg(avg('satisfaction_score').alias('avg_satisfaction'), count('*').alias('total_visitors')).withColumn('satisfaction_weight', col('avg_satisfaction') * col('total_visitors')).orderBy(desc('satisfaction_weight'))
        duration_analysis = visitor_df.groupBy('spot__category').agg(avg('visit_duration').alias('avg_duration'), count('*').alias('visitor_count')).orderBy(desc('avg_duration'))
        province_preference = visitor_df.groupBy('visitor_age_group', 'spot__province').agg(count('*').alias('visit_count')).orderBy(desc('visit_count'))
        cross_category = visitor_df.crossJoin(visitor_df.select('spot__category').distinct().withColumnRenamed('spot__category', 'target_category')).filter(col('spot__category') != col('target_category')).groupBy('spot__category', 'target_category').agg(count('*').alias('cross_visit_count'))
        preference_clustering = visitor_df.groupBy('visitor_age_group', 'visitor_gender').agg(collect_list('spot__category').alias('preferred_categories'), avg('satisfaction_score').alias('group_satisfaction'))
        high_value_segments = visitor_df.filter(col('satisfaction_score') >= 4.0).groupBy('visitor_age_group', 'spot__category').agg(count('*').alias('high_satisfaction_count')).orderBy(desc('high_satisfaction_count'))
        age_data = [row.asDict() for row in age_preference.collect()]
        gender_data = [row.asDict() for row in gender_preference.collect()]
        rating_data = [row.asDict() for row in rating_preference.collect()]
        repeat_data = [row.asDict() for row in repeat_analysis.collect()]
        satisfaction_data = [row.asDict() for row in satisfaction_analysis.collect()]
        duration_data = [row.asDict() for row in duration_analysis.collect()]
        province_data = [row.asDict() for row in province_preference.collect()]
        cross_data = [row.asDict() for row in cross_category.collect()]
        cluster_data = [row.asDict() for row in preference_clustering.collect()]
        segment_data = [row.asDict() for row in high_value_segments.collect()]
        return JsonResponse({'age_preferences': age_data, 'gender_preferences': gender_data, 'rating_preferences': rating_data, 'repeat_visitors': repeat_data, 'satisfaction_analysis': satisfaction_data, 'duration_analysis': duration_data, 'province_preferences': province_data, 'cross_category_analysis': cross_data, 'preference_clusters': cluster_data, 'high_value_segments': segment_data})

六.系统文档展示