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国内旅游景点游客数据分析系统 - 简介
本系统是一套面向国内旅游行业的大数据分析平台，采用Hadoop分布式存储框架结合Django Web开发框架构建而成。系统通过HDFS存储海量旅游景点游客数据，利用Spark和Spark SQL进行高效的数据清洗、转换和分析计算，前端采用Vue+ElementUI+Echarts技术栈实现数据可视化呈现。系统核心功能涵盖游客多维画像分析、旅游消费行为分析、景点吸引力与满意度分析、旅游时序与环境影响分析以及全国区域旅游市场格局分析五大模块，共计23项具体分析维度。通过对游客年龄段、性别、客源地、旅游方式、消费水平、景点类型、满意度评分、天气状况、住宿类型等多维度数据进行深度挖掘，系统能够生成游客来源地TOP10排行、热门景点销量榜单、各省份旅游热力图、月度游客流量趋势、游客价值聚类等30余种可视化图表，为旅游管理部门、景区运营方和旅游企业提供全面的数据决策支持，帮助其优化资源配置、制定精准营销策略和提升服务质量。
国内旅游景点游客数据分析系统 -技术
开发语言：java或Python
数据库：MySQL
系统架构：B/S
前端：Vue+ElementUI+HTML+CSS+JavaScript+jQuery+Echarts
大数据框架：Hadoop+Spark+Hive
后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)
国内旅游景点游客数据分析系统 - 背景
选题背景 近年来国内旅游产业快速发展,各类旅游景点每年接待的游客数量呈现持续增长态势,随之产生的游客行为数据、消费数据、评价数据也呈现出爆发式增长的特点。这些数据蕴含着游客的出行偏好、消费习惯、满意度反馈等宝贵信息,但传统的数据库系统在面对TB级甚至PB级的海量旅游数据时,往往会遇到存储瓶颈和计算性能不足的问题。与此同时,旅游管理部门和景区运营方迫切需要从这些庞杂的数据中挖掘出有价值的信息,比如哪些地区是核心客源市场、不同年龄段游客的消费能力差异、哪些景点类型更受欢迎、天气变化对游客出行的影响程度等,用于指导旅游产品设计、营销渠道选择和服务质量改进。在这样的背景下,如何利用大数据技术高效地存储、处理和分析海量旅游数据,并将分析结果以直观的可视化形式呈现出来,成为旅游行业信息化建设的重要课题,也是本系统开发的现实需求所在。
选题意义 本系统的开发具有多方面的实际意义。从技术层面来看,系统通过引入Hadoop分布式存储框架和Spark计算引擎,能够较好地解决传统单机数据库在处理大规模旅游数据时的性能瓶颈,通过HDFS实现数据的分布式存储,利用Spark SQL进行并行化的数据分析计算,为海量数据的高效处理提供了一种可行的技术方案。从应用层面来讲,系统设计的23项数据分析维度覆盖了游客画像、消费行为、景点评价、时间规律、区域分布等多个方面,能够帮助旅游从业者更全面地了解市场现状和游客需求。比如通过分析客源地分布可以明确重点营销区域,通过对比不同年龄段的旅游方式偏好可以设计更有针对性的旅游产品,通过监测各月份游客流量趋势可以提前做好淡旺季的资源调配。另外,系统采用Vue+Echarts实现的数据可视化功能,能够将复杂的分析结果转化为直观易懂的图表形式,降低了数据解读的门槛。作为一个毕业设计项目,本系统的开发也是对大数据技术栈的一次实践应用,能够加深对Hadoop生态组件、Spark分布式计算、Django Web开发等技术的理解和掌握。
国内旅游景点游客数据分析系统 -图片展示
国内旅游景点游客数据分析系统 -代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, sum, when, dense_rank, desc, year, month, dayofmonth
from pyspark.sql.window import Window
from sklearn.cluster import KMeans
import numpy as np
import pandas as pd
from django.http import JsonResponse
from django.views import View
spark = SparkSession.builder.appName("TourismDataAnalysis").master("local[*]").config("spark.sql.shuffle.partitions", "4").config("spark.driver.memory", "2g").getOrCreate()
class VisitorSourceAnalysisView(View):
def get(self, request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/tourism_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "visitor_records").option("user", "root").option("password", "123456").load()
df.createOrReplaceTempView("visitor_data")
province_stats = spark.sql("SELECT source_province, COUNT(*) as visitor_count, AVG(consumption_amount) as avg_consumption FROM visitor_data GROUP BY source_province ORDER BY visitor_count DESC LIMIT 10")
province_list = province_stats.collect()
result_data = []
for row in province_list:
province_name = row['source_province']
visitor_num = row['visitor_count']
avg_consume = round(row['avg_consumption'], 2)
result_data.append({'province': province_name, 'count': visitor_num, 'avgConsumption': avg_consume})
gender_analysis = df.groupBy('source_province').agg(count(when(col('visitor_gender') == '男', 1)).alias('male_count'),count(when(col('visitor_gender') == '女', 1)).alias('female_count'))
gender_list = gender_analysis.collect()
gender_ratio_data = []
for item in gender_list:
total = item['male_count'] + item['female_count']
if total > 0:
male_ratio = round((item['male_count'] / total) * 100, 2)
female_ratio = round((item['female_count'] / total) * 100, 2)
gender_ratio_data.append({'province': item['source_province'],'maleRatio': male_ratio,'femaleRatio': female_ratio})
age_distribution = df.withColumn('age_group',when(col('visitor_age') < 25, '青年').when((col('visitor_age') >= 25) & (col('visitor_age') < 45), '中年').otherwise('老年'))
age_stats = age_distribution.groupBy('age_group').agg(count('*').alias('count'),avg('consumption_amount').alias('avg_consumption'))
age_result = age_stats.collect()
age_data = []
for age_row in age_result:
age_data.append({'ageGroup': age_row['age_group'],'count': age_row['count'],'avgConsumption': round(age_row['avg_consumption'], 2)})
return JsonResponse({'status': 'success','provinceRank': result_data,'genderRatio': gender_ratio_data,'ageDistribution': age_data})
class AttractionSatisfactionAnalysisView(View):
def get(self, request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/tourism_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "visitor_records").option("user", "root").option("password", "123456").load()
df.createOrReplaceTempView("attraction_data")
sales_ranking = spark.sql("SELECT attraction_name, attraction_type, SUM(attraction_sales) as total_sales, AVG(satisfaction_score) as avg_score FROM attraction_data GROUP BY attraction_name, attraction_type ORDER BY total_sales DESC LIMIT 15")
sales_list = sales_ranking.collect()
sales_result = []
for rank_idx, sales_row in enumerate(sales_list, 1):
sales_result.append({'rank': rank_idx,'attractionName': sales_row['attraction_name'],'attractionType': sales_row['attraction_type'],'totalSales': sales_row['total_sales'],'avgScore': round(sales_row['avg_score'], 2)})
type_satisfaction = df.groupBy('attraction_type').agg(avg('satisfaction_score').alias('avg_satisfaction'),count('*').alias('visitor_count'))
type_list = type_satisfaction.collect()
type_result = []
for type_row in type_list:
type_result.append({'type': type_row['attraction_type'],'avgSatisfaction': round(type_row['avg_satisfaction'], 2),'visitorCount': type_row['visitor_count']})
price_satisfaction_df = df.select('ticket_price', 'satisfaction_score').filter(col('ticket_price').isNotNull())
price_satisfaction_df = price_satisfaction_df.withColumn('price_level',when(col('ticket_price') < 50, '低价').when((col('ticket_price') >= 50) & (col('ticket_price') < 150), '中价').otherwise('高价'))
price_stats = price_satisfaction_df.groupBy('price_level').agg(avg('satisfaction_score').alias('avg_score'),count('*').alias('count'))
price_list = price_stats.collect()
price_result = []
for price_row in price_list:
price_result.append({'priceLevel': price_row['price_level'],'avgScore': round(price_row['avg_score'], 2),'count': price_row['count']})
rating_satisfaction = df.groupBy('attraction_rating').agg(avg('satisfaction_score').alias('avg_satisfaction'),count('*').alias('count')).orderBy(desc('attraction_rating'))
rating_list = rating_satisfaction.collect()
rating_result = []
for rating_row in rating_list:
rating_result.append({'rating': rating_row['attraction_rating'],'avgSatisfaction': round(rating_row['avg_satisfaction'], 2),'count': rating_row['count']})
sentiment_distribution = df.groupBy('sentiment_polarity').agg(count('*').alias('count'))
sentiment_list = sentiment_distribution.collect()
sentiment_result = []
total_sentiment = sum([s['count'] for s in sentiment_list])
for sentiment_row in sentiment_list:
percentage = round((sentiment_row['count'] / total_sentiment) * 100, 2)
sentiment_result.append({'sentiment': sentiment_row['sentiment_polarity'],'count': sentiment_row['count'],'percentage': percentage})
return JsonResponse({'status': 'success','salesRanking': sales_result,'typeSatisfaction': type_result,'priceSatisfaction': price_result,'ratingSatisfaction': rating_result,'sentimentDistribution': sentiment_result})
class VisitorValueClusteringView(View):
def post(self, request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/tourism_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "visitor_records").option("user", "root").option("password", "123456").load()
df.createOrReplaceTempView("clustering_data")
rfm_df = spark.sql("SELECT visitor_id, MAX(play_date) as last_visit, COUNT(*) as frequency, SUM(consumption_amount) as monetary FROM clustering_data GROUP BY visitor_id")
from datetime import datetime
current_date = datetime.now()
rfm_pandas = rfm_df.toPandas()
rfm_pandas['last_visit'] = pd.to_datetime(rfm_pandas['last_visit'])
rfm_pandas['recency'] = (current_date - rfm_pandas['last_visit']).dt.days
rfm_features = rfm_pandas[['recency', 'frequency', 'monetary']].fillna(0)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
rfm_scaled = scaler.fit_transform(rfm_features)
kmeans = KMeans(n_clusters=3, random_state=42, max_iter=300)
rfm_pandas['cluster'] = kmeans.fit_predict(rfm_scaled)
cluster_analysis = rfm_pandas.groupby('cluster').agg({'visitor_id': 'count','recency': 'mean','frequency': 'mean','monetary': 'mean'}).reset_index()
cluster_analysis.columns = ['cluster', 'visitor_count', 'avg_recency', 'avg_frequency', 'avg_monetary']
cluster_analysis['avg_recency'] = cluster_analysis['avg_recency'].round(2)
cluster_analysis['avg_frequency'] = cluster_analysis['avg_frequency'].round(2)
cluster_analysis['avg_monetary'] = cluster_analysis['avg_monetary'].round(2)
cluster_result = []
for idx, row in cluster_analysis.iterrows():
if row['avg_monetary'] > cluster_analysis['avg_monetary'].mean() and row['avg_frequency'] > cluster_analysis['avg_frequency'].mean():
cluster_label = '高价值客户'
elif row['avg_monetary'] < cluster_analysis['avg_monetary'].mean() and row['avg_frequency'] < cluster_analysis['avg_frequency'].mean():
cluster_label = '低价值客户'
else:
cluster_label = '中价值客户'
cluster_result.append({'cluster': int(row['cluster']),'label': cluster_label,'visitorCount': int(row['visitor_count']),'avgRecency': float(row['avg_recency']),'avgFrequency': float(row['avg_frequency']),'avgMonetary': float(row['avg_monetary'])})
visitor_detail = rfm_pandas[['visitor_id', 'cluster', 'recency', 'frequency', 'monetary']].to_dict('records')
for visitor in visitor_detail:
cluster_id = visitor['cluster']
matching_cluster = next((c for c in cluster_result if c['cluster'] == cluster_id), None)
if matching_cluster:
visitor['clusterLabel'] = matching_cluster['label']
return JsonResponse({'status': 'success','clusterAnalysis': cluster_result,'visitorDetail': visitor_detail[:100]})
```
```
# **国内旅游景点游客数据分析系统** **-结语**
2026年85%导师认可的大数据毕设：基于Hadoop+Spark的国内旅游景点游客数据分析系统
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