【大数据】旅游城市气候数据可视化分析系统 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

《旅游城市气候数据可视化分析系统》是一套基于大数据技术栈的智能分析平台，采用Hadoop+Spark分布式计算框架处理海量气候数据，结合Django后端框架和Vue+ElementUI+Echarts前端技术实现数据的深度挖掘与可视化展示。系统通过Spark SQL和Pandas、NumPy等数据处理工具，对全国各旅游城市的气候特征进行多维度分析，涵盖气候季节分析、城市主题分析、主题关联分析、成本特征分析和专项偏好分析五大核心功能模块。平台将复杂的气候数据转化为直观的图表和可视化大屏，为旅游规划、城市发展和气候研究提供数据支撑，实现了从原始气候数据到智能分析结果的完整处理链路，帮助用户快速获取城市气候特征洞察。

三.系统功能演示

旅游城市气候数据可视化分析系统

四.系统界面展示

五.系统源码展示

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, count, when, desc, asc, sum as spark_sum
from django.http import JsonResponse
import pandas as pd
import numpy as np

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

def climate_season_analysis(city_list, year_range):
    climate_df = spark.read.parquet("hdfs://climate_data/")
    filtered_df = climate_df.filter(col("city").isin(city_list)).filter(col("year").between(year_range[0], year_range[1]))
    season_df = filtered_df.withColumn("season", when(col("month").isin([12, 1, 2]), "winter").when(col("month").isin([3, 4, 5]), "spring").when(col("month").isin([6, 7, 8]), "summer").otherwise("autumn"))
    season_stats = season_df.groupBy("city", "season").agg(avg("temperature").alias("avg_temp"), avg("humidity").alias("avg_humidity"), avg("rainfall").alias("avg_rainfall"), count("*").alias("record_count"))
    comfort_df = season_stats.withColumn("comfort_index", (col("avg_temp") * 0.4 + (100 - col("avg_humidity")) * 0.3 + (100 - col("avg_rainfall")) * 0.3))
    result_df = comfort_df.withColumn("season_rank", col("comfort_index").desc()).orderBy("city", col("comfort_index").desc())
    optimal_seasons = result_df.groupBy("city").agg({"season": "first", "comfort_index": "max"}).withColumnRenamed("first(season)", "best_season").withColumnRenamed("max(comfort_index)", "max_comfort")
    final_result = result_df.join(optimal_seasons, "city").select("city", "season", "avg_temp", "avg_humidity", "avg_rainfall", "comfort_index", "best_season")
    pandas_result = final_result.toPandas()
    return pandas_result.to_dict('records')

def city_theme_analysis(theme_preferences, weight_config):
    theme_df = spark.read.parquet("hdfs://tourism_data/")
    climate_df = spark.read.parquet("hdfs://climate_data/")
    merged_df = theme_df.join(climate_df, ["city", "month"], "inner")
    theme_filtered = merged_df.filter(col("theme_type").isin(theme_preferences))
    weighted_df = theme_filtered.withColumn("beach_score", when(col("theme_type") == "beach", col("temperature") * weight_config.get("temp_weight", 0.5) + (100 - col("rainfall")) * weight_config.get("rain_weight", 0.3)).otherwise(0))
    weighted_df = weighted_df.withColumn("mountain_score", when(col("theme_type") == "mountain", (30 - abs(col("temperature") - 20)) * weight_config.get("temp_weight", 0.4) + col("air_quality") * weight_config.get("air_weight", 0.4)).otherwise(0))
    weighted_df = weighted_df.withColumn("cultural_score", when(col("theme_type") == "cultural", col("visibility") * weight_config.get("visibility_weight", 0.6) + (100 - col("humidity")) * weight_config.get("humidity_weight", 0.4)).otherwise(0))
    aggregated_df = weighted_df.groupBy("city", "theme_type").agg(avg("beach_score").alias("avg_beach_score"), avg("mountain_score").alias("avg_mountain_score"), avg("cultural_score").alias("avg_cultural_score"), count("*").alias("data_points"))
    final_score_df = aggregated_df.withColumn("final_theme_score", col("avg_beach_score") + col("avg_mountain_score") + col("avg_cultural_score"))
    ranked_df = final_score_df.withColumn("rank", col("final_theme_score").desc()).orderBy("theme_type", col("final_theme_score").desc())
    top_cities = ranked_df.groupBy("theme_type").agg({"city": "first", "final_theme_score": "max"}).withColumnRenamed("first(city)", "top_city").withColumnRenamed("max(final_theme_score)", "highest_score")
    result_with_top = ranked_df.join(top_cities, "theme_type").select("city", "theme_type", "final_theme_score", "top_city", "highest_score", "data_points")
    pandas_result = result_with_top.toPandas()
    return pandas_result.to_dict('records')

def cost_feature_analysis(budget_ranges, city_categories):
    cost_df = spark.read.parquet("hdfs://cost_data/")
    climate_df = spark.read.parquet("hdfs://climate_data/")
    category_df = spark.read.parquet("hdfs://city_category/")
    merged_cost_climate = cost_df.join(climate_df, ["city", "month"], "inner")
    full_merged = merged_cost_climate.join(category_df, "city", "inner")
    budget_filtered = full_merged.filter(col("city_category").isin(city_categories))
    budget_categorized = budget_filtered.withColumn("budget_category", when(col("total_cost") <= budget_ranges.get("low", 2000), "low_budget").when(col("total_cost") <= budget_ranges.get("medium", 5000), "medium_budget").otherwise("high_budget"))
    climate_cost_corr = budget_categorized.withColumn("temp_cost_factor", col("temperature") * col("accommodation_cost") / 1000)
    climate_cost_corr = climate_cost_corr.withColumn("weather_premium", when(col("rainfall") < 50, col("total_cost") * 1.1).otherwise(col("total_cost") * 0.95))
    seasonal_cost = climate_cost_corr.withColumn("season", when(col("month").isin([12, 1, 2]), "winter").when(col("month").isin([3, 4, 5]), "spring").when(col("month").isin([6, 7, 8]), "summer").otherwise("autumn"))
    cost_stats = seasonal_cost.groupBy("city", "budget_category", "season").agg(avg("total_cost").alias("avg_total_cost"), avg("accommodation_cost").alias("avg_accommodation"), avg("food_cost").alias("avg_food"), avg("transport_cost").alias("avg_transport"), avg("weather_premium").alias("avg_weather_premium"))
    cost_efficiency = cost_stats.withColumn("cost_efficiency_score", (col("avg_weather_premium") / col("avg_total_cost")) * 100)
    value_analysis = cost_efficiency.withColumn("value_rating", when(col("cost_efficiency_score") >= 105, "excellent").when(col("cost_efficiency_score") >= 100, "good").when(col("cost_efficiency_score") >= 95, "fair").otherwise("poor"))
    final_cost_df = value_analysis.orderBy("budget_category", col("cost_efficiency_score").desc())
    pandas_result = final_cost_df.toPandas()
    return pandas_result.to_dict('records')

六.系统文档展示

结束

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