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基于大数据的旅游城市气候数据可视化分析系统介绍
基于大数据的旅游城市气候数据可视化分析系统是一套专门针对旅游行业气候数据处理与分析的综合性平台，该系统采用Hadoop分布式存储架构结合Spark大数据计算引擎，能够高效处理海量的旅游城市气候数据，通过HDFS分布式文件系统实现数据的可靠存储，利用Spark SQL进行快速数据查询与分析处理。系统支持Python+Django和Java+SpringBoot两种技术实现方案，前端采用Vue框架配合ElementUI组件库构建用户界面，通过Echarts图表库实现丰富的数据可视化效果，同时结合HTML、CSS、JavaScript、jQuery等前端技术确保良好的用户交互体验。系统功能模块完整，包含基础的用户管理功能如系统首页、个人信息管理、密码修改等，核心分析功能涵盖可视化分析中心、大屏可视化展示、气候季节分析、城市主题分析、主题关联分析、成本特征分析以及专项偏好分析等多个维度，能够从不同角度深入挖掘旅游城市气候数据的价值。系统底层数据处理采用Pandas、NumPy等Python数据分析库进行数据清洗与预处理，数据存储基于MySQL关系型数据库，整体架构设计合理，技术栈成熟稳定，既满足了大数据处理的技术要求，又兼顾了系统的实用性和可扩展性，为旅游行业的气候数据分析提供了完整的解决方案。
基于大数据的旅游城市气候数据可视化分析系统演示视频
基于大数据的旅游城市气候数据可视化分析系统演示图片
基于大数据的旅游城市气候数据可视化分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, count, max, min, when, desc, asc
from django.http import JsonResponse
import pandas as pd
import numpy as np
spark = SparkSession.builder.appName("TourismClimateAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def visualization_analysis_center(request):
city_name = request.GET.get('city_name', '')
start_date = request.GET.get('start_date', '')
end_date = request.GET.get('end_date', '')
climate_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/tourism_climate").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "climate_data").option("user", "root").option("password", "password").load()
filtered_data = climate_df.filter((col("city_name") == city_name) & (col("record_date") >= start_date) & (col("record_date") <= end_date))
temperature_stats = filtered_data.agg(avg("temperature").alias("avg_temp"), max("temperature").alias("max_temp"), min("temperature").alias("min_temp")).collect()[0]
humidity_stats = filtered_data.agg(avg("humidity").alias("avg_humidity"), max("humidity").alias("max_humidity"), min("humidity").alias("min_humidity")).collect()[0]
rainfall_stats = filtered_data.agg(avg("rainfall").alias("avg_rainfall"), max("rainfall").alias("max_rainfall"), min("rainfall").alias("min_rainfall")).collect()[0]
weather_type_count = filtered_data.groupBy("weather_type").count().orderBy(desc("count")).collect()
monthly_trend = filtered_data.groupBy("month").agg(avg("temperature").alias("monthly_avg_temp"), avg("humidity").alias("monthly_avg_humidity"), avg("rainfall").alias("monthly_avg_rainfall")).orderBy("month").collect()
comfortable_days = filtered_data.filter((col("temperature") >= 18) & (col("temperature") <= 26) & (col("humidity") >= 40) & (col("humidity") <= 70)).count()
total_days = filtered_data.count()
comfort_rate = (comfortable_days / total_days * 100) if total_days > 0 else 0
wind_level_distribution = filtered_data.groupBy("wind_level").count().orderBy("wind_level").collect()
extreme_weather_days = filtered_data.filter((col("temperature") > 35) | (col("temperature") < 0) | (col("rainfall") > 50)).count()
uv_index_avg = filtered_data.agg(avg("uv_index").alias("avg_uv")).collect()[0]["avg_uv"]
air_quality_distribution = filtered_data.groupBy("air_quality_level").count().orderBy(desc("count")).collect()
tourism_suitability_score = (comfort_rate * 0.4 + (100 - extreme_weather_days / total_days * 100) * 0.3 + (100 - uv_index_avg * 10) * 0.3) if total_days > 0 else 0
result_data = {"temperature_stats": {"avg": round(temperature_stats["avg_temp"], 2), "max": temperature_stats["max_temp"], "min": temperature_stats["min_temp"]}, "humidity_stats": {"avg": round(humidity_stats["avg_humidity"], 2), "max": humidity_stats["max_humidity"], "min": humidity_stats["min_humidity"]}, "rainfall_stats": {"avg": round(rainfall_stats["avg_rainfall"], 2), "max": rainfall_stats["max_rainfall"], "min": rainfall_stats["min_rainfall"]}, "weather_distribution": [{"type": row["weather_type"], "count": row["count"]} for row in weather_type_count], "monthly_trend": [{"month": row["month"], "temp": round(row["monthly_avg_temp"], 2), "humidity": round(row["monthly_avg_humidity"], 2), "rainfall": round(row["monthly_avg_rainfall"], 2)} for row in monthly_trend], "comfort_rate": round(comfort_rate, 2), "wind_distribution": [{"level": row["wind_level"], "count": row["count"]} for row in wind_level_distribution], "extreme_weather_days": extreme_weather_days, "tourism_score": round(tourism_suitability_score, 2), "air_quality": [{"level": row["air_quality_level"], "count": row["count"]} for row in air_quality_distribution]}
return JsonResponse({"status": "success", "data": result_data})
def climate_season_analysis(request):
city_list = request.GET.getlist('cities[]')
year = request.GET.get('year', '2023')
climate_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/tourism_climate").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "climate_data").option("user", "root").option("password", "password").load()
season_data = climate_df.withColumn("season", when((col("month").isin([3, 4, 5])), "spring").when((col("month").isin([6, 7, 8])), "summer").when((col("month").isin([9, 10, 11])), "autumn").otherwise("winter"))
filtered_season_data = season_data.filter((col("city_name").isin(city_list)) & (col("year") == year))
season_climate_stats = filtered_season_data.groupBy("city_name", "season").agg(avg("temperature").alias("avg_temp"), avg("humidity").alias("avg_humidity"), avg("rainfall").alias("avg_rainfall"), count("*").alias("record_count")).collect()
season_comfort_analysis = filtered_season_data.filter((col("temperature") >= 15) & (col("temperature") <= 28) & (col("humidity") >= 30) & (col("humidity") <= 80)).groupBy("city_name", "season").count().withColumnRenamed("count", "comfortable_days").collect()
season_weather_patterns = filtered_season_data.groupBy("city_name", "season", "weather_type").count().collect()
season_tourism_ranking = filtered_season_data.groupBy("city_name", "season").agg(avg("temperature").alias("temp"), avg("humidity").alias("humidity"), avg("rainfall").alias("rain")).collect()
best_season_by_city = {}
for row in season_tourism_ranking:
city = row["city_name"]
season = row["season"]
temp_score = 100 - abs(row["temp"] - 22) * 4
humidity_score = 100 - abs(row["humidity"] - 55) * 2
rain_score = max(0, 100 - row["rain"] * 3)
total_score = (temp_score + humidity_score + rain_score) / 3
if city not in best_season_by_city or total_score > best_season_by_city[city]["score"]:
best_season_by_city[city] = {"season": season, "score": round(total_score, 2)}
season_comparison_data = {}
for row in season_climate_stats:
city = row["city_name"]
if city not in season_comparison_data:
season_comparison_data[city] = {}
season_comparison_data[city][row["season"]] = {"avg_temp": round(row["avg_temp"], 2), "avg_humidity": round(row["avg_humidity"], 2), "avg_rainfall": round(row["avg_rainfall"], 2), "record_count": row["record_count"]}
comfort_data_by_season = {}
for row in season_comfort_analysis:
city = row["city_name"]
if city not in comfort_data_by_season:
comfort_data_by_season[city] = {}
comfort_data_by_season[city][row["season"]] = row["comfortable_days"]
weather_pattern_analysis = {}
for row in season_weather_patterns:
city = row["city_name"]
season = row["season"]
weather = row["weather_type"]
if city not in weather_pattern_analysis:
weather_pattern_analysis[city] = {}
if season not in weather_pattern_analysis[city]:
weather_pattern_analysis[city][season] = {}
weather_pattern_analysis[city][season][weather] = row["count"]
return JsonResponse({"status": "success", "data": {"season_stats": season_comparison_data, "comfort_analysis": comfort_data_by_season, "best_seasons": best_season_by_city, "weather_patterns": weather_pattern_analysis}})
def city_theme_analysis(request):
theme_type = request.GET.get('theme_type', 'temperature')
analysis_period = request.GET.get('period', 'annual')
climate_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/tourism_climate").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "climate_data").option("user", "root").option("password", "password").load()
if theme_type == 'temperature':
city_temp_analysis = climate_df.groupBy("city_name").agg(avg("temperature").alias("avg_temp"), max("temperature").alias("max_temp"), min("temperature").alias("min_temp")).collect()
temperature_categories = []
for row in city_temp_analysis:
city = row["city_name"]
avg_temp = row["avg_temp"]
if avg_temp >= 25:
category = "热带城市"
elif avg_temp >= 20:
category = "温带城市"
elif avg_temp >= 15:
category = "凉爽城市"
else:
category = "寒冷城市"
temperature_categories.append({"city": city, "category": category, "avg_temp": round(avg_temp, 2), "max_temp": row["max_temp"], "min_temp": row["min_temp"]})
theme_result = {"theme": "温度主题分析", "categories": temperature_categories}
elif theme_type == 'humidity':
city_humidity_analysis = climate_df.groupBy("city_name").agg(avg("humidity").alias("avg_humidity"), max("humidity").alias("max_humidity"), min("humidity").alias("min_humidity")).collect()
humidity_categories = []
for row in city_humidity_analysis:
city = row["city_name"]
avg_humidity = row["avg_humidity"]
if avg_humidity >= 80:
category = "高湿城市"
elif avg_humidity >= 60:
category = "适中湿度城市"
elif avg_humidity >= 40:
category = "干燥城市"
else:
category = "极干燥城市"
humidity_categories.append({"city": city, "category": category, "avg_humidity": round(avg_humidity, 2), "max_humidity": row["max_humidity"], "min_humidity": row["min_humidity"]})
theme_result = {"theme": "湿度主题分析", "categories": humidity_categories}
elif theme_type == 'rainfall':
city_rainfall_analysis = climate_df.groupBy("city_name").agg(avg("rainfall").alias("avg_rainfall"), max("rainfall").alias("max_rainfall"), min("rainfall").alias("min_rainfall")).collect()
rainfall_categories = []
for row in city_rainfall_analysis:
city = row["city_name"]
avg_rainfall = row["avg_rainfall"]
if avg_rainfall >= 15:
category = "多雨城市"
elif avg_rainfall >= 8:
category = "适中降雨城市"
elif avg_rainfall >= 3:
category = "少雨城市"
else:
category = "干旱城市"
rainfall_categories.append({"city": city, "category": category, "avg_rainfall": round(avg_rainfall, 2), "max_rainfall": row["max_rainfall"], "min_rainfall": row["min_rainfall"]})
theme_result = {"theme": "降雨主题分析", "categories": rainfall_categories}
city_tourism_suitability = climate_df.groupBy("city_name").agg(avg("temperature").alias("temp"), avg("humidity").alias("humidity"), avg("rainfall").alias("rain"), avg("uv_index").alias("uv")).collect()
suitability_ranking = []
for row in city_tourism_suitability:
city = row["city_name"]
temp_score = max(0, 100 - abs(row["temp"] - 23) * 3)
humidity_score = max(0, 100 - abs(row["humidity"] - 60) * 2)
rain_score = max(0, 100 - row["rain"] * 5)
uv_score = max(0, 100 - row["uv"] * 8)
total_score = (temp_score + humidity_score + rain_score + uv_score) / 4
suitability_ranking.append({"city": city, "score": round(total_score, 2), "temp": round(row["temp"], 2), "humidity": round(row["humidity"], 2), "rainfall": round(row["rain"], 2)})
suitability_ranking.sort(key=lambda x: x["score"], reverse=True)
theme_result["suitability_ranking"] = suitability_ranking
return JsonResponse({"status": "success", "data": theme_result})
基于大数据的旅游城市气候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！
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