💖💖作者：计算机编程小咖 💙💙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
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基于大数据的江西景区数据可视化分析系统介绍
《基于大数据的江西景区数据可视化分析系统》是一套完整的旅游大数据处理与分析平台，该系统采用Hadoop+Spark大数据框架作为核心架构，通过HDFS分布式文件系统存储海量江西景区相关数据，利用Spark SQL进行高效的数据处理和分析计算，结合Python的Pandas、NumPy等数据科学库实现复杂的数据挖掘算法，后端基于Django框架构建RESTful API接口，前端采用Vue+ElementUI+Echarts技术栈打造现代化的用户交互界面，实现了景区分布概览分析、景区价格洞察分析、景区热度排行分析、综合专题透视分析等核心功能模块。系统通过大数据技术对江西省内各大景区的地理分布、票价变动、游客流量、季节性热度等多维度数据进行深度挖掘，生成直观的可视化图表和分析报告，帮助旅游管理部门、景区运营方以及游客群体更好地了解江西旅游市场的发展趋势和规律特征，同时系统还提供完善的用户管理功能，包括个人信息管理、密码修改、系统管理等基础模块，确保平台的安全性和易用性，整体架构采用前后端分离设计，支持高并发访问和大规模数据处理，是一套技术先进、功能完善的旅游大数据分析解决方案。
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
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, sum, desc, asc, when, isnan, isnull
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("JiangxiTourismAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@csrf_exempt
def scenic_distribution_analysis(request):
scenic_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/tourism_db").option("dbtable", "scenic_spots").option("user", "root").option("password", "password").load()
scenic_df.createOrReplaceTempView("scenic_spots")
city_distribution = spark.sql("SELECT city, COUNT(*) as spot_count, AVG(rating) as avg_rating FROM scenic_spots GROUP BY city ORDER BY spot_count DESC")
city_pandas_df = city_distribution.toPandas()
level_distribution = spark.sql("SELECT level, COUNT(*) as count, ROUND(COUNT(*) * 100.0 / (SELECT COUNT(*) FROM scenic_spots), 2) as percentage FROM scenic_spots GROUP BY level ORDER BY count DESC")
level_pandas_df = level_distribution.toPandas()
type_distribution = spark.sql("SELECT type, COUNT(*) as count FROM scenic_spots GROUP BY type ORDER BY count DESC")
type_pandas_df = type_distribution.toPandas()
region_stats = spark.sql("SELECT region, COUNT(*) as total_spots, AVG(area_size) as avg_area, SUM(annual_visitors) as total_visitors FROM scenic_spots GROUP BY region")
region_pandas_df = region_stats.toPandas()
popular_cities = city_pandas_df.head(10).to_dict('records')
level_stats = level_pandas_df.to_dict('records')
type_stats = type_pandas_df.to_dict('records')
region_analysis = region_pandas_df.to_dict('records')
distribution_summary = {"total_spots": int(scenic_df.count()), "total_cities": int(city_distribution.count()), "avg_rating_overall": float(scenic_df.agg(avg("rating")).collect()[0][0]), "most_popular_city": popular_cities[0]["city"] if popular_cities else ""}
return JsonResponse({"status": "success", "data": {"city_distribution": popular_cities, "level_distribution": level_stats, "type_distribution": type_stats, "region_analysis": region_analysis, "summary": distribution_summary}})
@csrf_exempt
def scenic_price_insight_analysis(request):
price_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/tourism_db").option("dbtable", "scenic_prices").option("user", "root").option("password", "password").load()
price_df.createOrReplaceTempView("scenic_prices")
price_trends = spark.sql("SELECT month, AVG(adult_price) as avg_adult_price, AVG(child_price) as avg_child_price, AVG(senior_price) as avg_senior_price FROM scenic_prices GROUP BY month ORDER BY month")
price_trends_df = price_trends.toPandas()
level_price_analysis = spark.sql("SELECT sp.level, AVG(pr.adult_price) as avg_price, MIN(pr.adult_price) as min_price, MAX(pr.adult_price) as max_price, COUNT(*) as spot_count FROM scenic_spots sp JOIN scenic_prices pr ON sp.id = pr.scenic_id GROUP BY sp.level ORDER BY avg_price DESC")
level_price_df = level_price_analysis.toPandas()
seasonal_pricing = spark.sql("SELECT CASE WHEN month IN (12,1,2) THEN 'winter' WHEN month IN (3,4,5) THEN 'spring' WHEN month IN (6,7,8) THEN 'summer' ELSE 'autumn' END as season, AVG(adult_price) as avg_price, COUNT(*) as record_count FROM scenic_prices GROUP BY season ORDER BY avg_price DESC")
seasonal_df = seasonal_pricing.toPandas()
price_range_distribution = spark.sql("SELECT CASE WHEN adult_price < 50 THEN 'low' WHEN adult_price BETWEEN 50 AND 100 THEN 'medium' WHEN adult_price BETWEEN 100 AND 200 THEN 'high' ELSE 'premium' END as price_range, COUNT(*) as count, ROUND(AVG(rating), 2) as avg_rating FROM scenic_spots sp JOIN scenic_prices pr ON sp.id = pr.scenic_id GROUP BY price_range ORDER BY count DESC")
price_range_df = price_range_distribution.toPandas()
city_price_comparison = spark.sql("SELECT sp.city, AVG(pr.adult_price) as avg_price, COUNT(*) as spot_count FROM scenic_spots sp JOIN scenic_prices pr ON sp.id = pr.scenic_id GROUP BY sp.city HAVING COUNT(*) >= 3 ORDER BY avg_price DESC LIMIT 15")
city_price_df = city_price_comparison.toPandas()
price_correlation = spark.sql("SELECT sp.rating, AVG(pr.adult_price) as avg_price FROM scenic_spots sp JOIN scenic_prices pr ON sp.id = pr.scenic_id GROUP BY sp.rating ORDER BY sp.rating")
correlation_df = price_correlation.toPandas()
overall_stats = {"avg_adult_price": float(price_df.agg(avg("adult_price")).collect()[0][0]), "max_price": float(price_df.agg({"adult_price": "max"}).collect()[0][0]), "min_price": float(price_df.agg({"adult_price": "min"}).collect()[0][0])}
return JsonResponse({"status": "success", "data": {"price_trends": price_trends_df.to_dict('records'), "level_price_analysis": level_price_df.to_dict('records'), "seasonal_pricing": seasonal_df.to_dict('records'), "price_range_distribution": price_range_df.to_dict('records'), "city_price_comparison": city_price_df.to_dict('records'), "price_correlation": correlation_df.to_dict('records'), "overall_stats": overall_stats}})
@csrf_exempt
def scenic_popularity_ranking_analysis(request):
visitor_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/tourism_db").option("dbtable", "visitor_records").option("user", "root").option("password", "password").load()
scenic_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/tourism_db").option("dbtable", "scenic_spots").option("user", "root").option("password", "password").load()
visitor_df.createOrReplaceTempView("visitor_records")
scenic_df.createOrReplaceTempView("scenic_spots")
monthly_popularity = spark.sql("SELECT vr.scenic_id, sp.name, sp.city, SUM(vr.visitor_count) as total_visitors, AVG(vr.visitor_count) as avg_monthly_visitors, sp.rating FROM visitor_records vr JOIN scenic_spots sp ON vr.scenic_id = sp.id GROUP BY vr.scenic_id, sp.name, sp.city, sp.rating ORDER BY total_visitors DESC LIMIT 20")
monthly_df = monthly_popularity.toPandas()
seasonal_popularity = spark.sql("SELECT vr.scenic_id, sp.name, CASE WHEN vr.month IN (12,1,2) THEN 'winter' WHEN vr.month IN (3,4,5) THEN 'spring' WHEN vr.month IN (6,7,8) THEN 'summer' ELSE 'autumn' END as season, SUM(vr.visitor_count) as seasonal_visitors FROM visitor_records vr JOIN scenic_spots sp ON vr.scenic_id = sp.id GROUP BY vr.scenic_id, sp.name, season ORDER BY seasonal_visitors DESC")
seasonal_df = seasonal_popularity.toPandas()
city_popularity_ranking = spark.sql("SELECT sp.city, SUM(vr.visitor_count) as total_city_visitors, COUNT(DISTINCT vr.scenic_id) as spot_count, AVG(sp.rating) as avg_city_rating FROM visitor_records vr JOIN scenic_spots sp ON vr.scenic_id = sp.id GROUP BY sp.city ORDER BY total_city_visitors DESC LIMIT 15")
city_ranking_df = city_popularity_ranking.toPandas()
growth_trend_analysis = spark.sql("SELECT vr.scenic_id, sp.name, vr.year, SUM(vr.visitor_count) as yearly_visitors, LAG(SUM(vr.visitor_count)) OVER (PARTITION BY vr.scenic_id ORDER BY vr.year) as prev_year_visitors FROM visitor_records vr JOIN scenic_spots sp ON vr.scenic_id = sp.id GROUP BY vr.scenic_id, sp.name, vr.year ORDER BY vr.scenic_id, vr.year")
growth_df = growth_trend_analysis.toPandas()
growth_df['growth_rate'] = ((growth_df['yearly_visitors'] - growth_df['prev_year_visitors']) / growth_df['prev_year_visitors'] * 100).round(2)
top_growing_spots = growth_df.dropna().nlargest(10, 'growth_rate')
rating_vs_popularity = spark.sql("SELECT sp.rating, AVG(total_visitors) as avg_visitors, COUNT(*) as spot_count FROM (SELECT scenic_id, SUM(visitor_count) as total_visitors FROM visitor_records GROUP BY scenic_id) vr_agg JOIN scenic_spots sp ON vr_agg.scenic_id = sp.id GROUP BY sp.rating ORDER BY sp.rating")
rating_popularity_df = rating_vs_popularity.toPandas()
peak_months_analysis = spark.sql("SELECT month, SUM(visitor_count) as monthly_total, AVG(visitor_count) as monthly_avg FROM visitor_records GROUP BY month ORDER BY monthly_total DESC")
peak_months_df = peak_months_analysis.toPandas()
overall_popularity_stats = {"total_annual_visitors": int(visitor_df.agg(sum("visitor_count")).collect()[0][0]), "most_popular_month": int(peak_months_df.iloc[0]['month']), "average_monthly_visitors": float(visitor_df.agg(avg("visitor_count")).collect()[0][0])}
return JsonResponse({"status": "success", "data": {"monthly_ranking": monthly_df.to_dict('records'), "seasonal_popularity": seasonal_df.to_dict('records'), "city_ranking": city_ranking_df.to_dict('records'), "growth_trends": top_growing_spots.to_dict('records'), "rating_vs_popularity": rating_popularity_df.to_dict('records'), "peak_months": peak_months_df.to_dict('records'), "overall_stats": overall_popularity_stats}})
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：计算机编程小咖 💙💙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
