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基于大数据的气候驱动的疾病传播可视化分析系统-功能介绍
基于大数据的气候驱动的疾病传播可视化分析系统是一个集成了Hadoop分布式存储、Spark大数据处理和前端可视化展示的综合性分析平台。该系统采用Python作为主要开发语言，后端基于Django框架构建RESTful API接口，前端运用Vue.js结合ElementUI和Echarts实现交互式数据可视化界面。系统核心功能围绕四大分析维度展开：全局时序分析通过24年历史数据揭示气候与疾病的长期演变规律，地理空间分析识别全球疾病传播热点区域，核心驱动因素分析量化温度、降水等气候变量对疟疾和登革热传播的影响程度，多指标复合风险评估通过气候风险象限划分和高风险国家特征画像提供综合性风险评估。系统利用Spark SQL进行大规模数据聚合计算，通过Pandas和NumPy进行统计分析，最终将分析结果以折线图、热力图、地理分布图等多种可视化形式呈现，为公共卫生决策提供数据支撑。
基于大数据的气候驱动的疾病传播可视化分析系统-选题背景意义
选题背景 全球气候变化正在深刻影响着人类健康，特别是蚊媒传播疾病的分布模式和发病规律。温度升高和降水模式改变为蚊虫繁殖创造了更适宜的环境条件，导致疟疾、登革热等传染病的传播范围不断扩大，发病时间窗口延长。世界卫生组织的监测数据显示，气候敏感性疾病的发病趋势与全球气温变化存在显著相关性，这种关联性在热带和亚热带地区表现得尤为明显。传统的疾病监测和预警系统往往基于单一的医学统计数据，缺乏对气候因素的系统性考量，难以准确预测疾病暴发的时空分布规律。随着大数据技术的快速发展，利用海量的气候监测数据和疾病发病数据进行关联分析成为可能，通过Hadoop和Spark等分布式计算技术可以高效处理跨越多年的全球气候与健康数据。当前迫切需要建立一个能够整合气候数据和疾病数据的智能分析平台，为公共卫生部门提供科学的决策依据。 选题意义 本课题的研究意义主要体现在技术实践和应用价值两个层面。在技术层面，该系统为计算机专业学生提供了一个完整的大数据技术栈实践机会，涵盖了从数据存储、处理到可视化的全流程技术应用，有助于深化对Hadoop生态系统和Spark计算引擎的理解和掌握。系统集成了多种主流技术框架，包括Django后端开发、Vue前端构建和Echarts数据可视化，为学生积累企业级项目开发经验打下基础。在应用价值方面，该系统能够为公共卫生研究人员和政策制定者提供直观的气候与疾病关联性分析工具，通过可视化展示帮助识别疾病传播的高风险区域和时间窗口。虽然作为毕业设计项目在数据规模和算法复杂度上存在一定局限性，但其设计理念和技术架构具有良好的可扩展性，为后续的深入研究和系统优化奠定了技术基础。该系统的研究成果也可以为相关领域的学术研究提供数据分析工具和可视化支持，推动跨学科合作研究的开展。
基于大数据的气候驱动的疾病传播可视化分析系统-技术选型
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
基于大数据的气候驱动的疾病传播可视化分析系统-视频展示
基于大数据的气候驱动的疾病传播可视化分析系统-图片展示
基于大数据的气候驱动的疾病传播可视化分析系统-代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, sum, count, year, month, when, percentile_approx
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("ClimateDiseaseBigDataAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
climate_disease_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://climate_disease_data.csv")
@csrf_exempt
def global_temporal_trend_analysis(request):
yearly_aggregated = climate_disease_df.groupBy(year(col("date")).alias("year")).agg(
avg("avg_temp_c").alias("avg_temperature"),
avg("precipitation_mm").alias("avg_precipitation"),
sum("malaria_cases").alias("total_malaria_cases"),
sum("dengue_cases").alias("total_dengue_cases"),
avg("air_quality_index").alias("avg_air_quality"),
avg("uv_index").alias("avg_uv_index")
).orderBy("year")
yearly_trends = yearly_aggregated.collect()
trend_data = []
for row in yearly_trends:
yearly_climate_impact_score = (row.avg_temperature * 0.4 + row.avg_precipitation * 0.3 + row.avg_air_quality * 0.2 + row.avg_uv_index * 0.1)
total_disease_burden = row.total_malaria_cases + row.total_dengue_cases
climate_disease_correlation = np.corrcoef([yearly_climate_impact_score], [total_disease_burden])[0,1] if total_disease_burden > 0 else 0
trend_data.append({
"year": row.year,
"temperature": round(row.avg_temperature, 2),
"precipitation": round(row.avg_precipitation, 2),
"malaria_cases": row.total_malaria_cases,
"dengue_cases": row.total_dengue_cases,
"climate_impact_score": round(yearly_climate_impact_score, 2),
"correlation_coefficient": round(climate_disease_correlation, 4)
})
seasonal_aggregated = climate_disease_df.groupBy(month(col("date")).alias("month")).agg(
avg("malaria_cases").alias("avg_monthly_malaria"),
avg("dengue_cases").alias("avg_monthly_dengue"),
avg("avg_temp_c").alias("avg_monthly_temp"),
avg("precipitation_mm").alias("avg_monthly_precipitation")
).orderBy("month")
seasonal_data = []
for row in seasonal_aggregated.collect():
seasonal_risk_index = (row.avg_monthly_malaria + row.avg_monthly_dengue) / (row.avg_monthly_temp + row.avg_monthly_precipitation + 1)
seasonal_data.append({
"month": row.month,
"malaria_avg": round(row.avg_monthly_malaria, 2),
"dengue_avg": round(row.avg_monthly_dengue, 2),
"temp_avg": round(row.avg_monthly_temp, 2),
"precipitation_avg": round(row.avg_monthly_precipitation, 2),
"seasonal_risk_index": round(seasonal_risk_index, 4)
})
return JsonResponse({"yearly_trends": trend_data, "seasonal_patterns": seasonal_data, "analysis_status": "success"})
@csrf_exempt
def geographic_spatial_hotspot_analysis(request):
country_aggregated = climate_disease_df.groupBy("country", "region").agg(
sum("malaria_cases").alias("total_malaria"),
sum("dengue_cases").alias("total_dengue"),
avg("avg_temp_c").alias("avg_country_temp"),
avg("precipitation_mm").alias("avg_country_precipitation"),
avg("population_density").alias("avg_population_density"),
avg("healthcare_budget").alias("avg_healthcare_budget"),
count("*").alias("data_points_count")
)
country_data = country_aggregated.collect()
country_analysis = []
total_disease_counts = [row.total_malaria + row.total_dengue for row in country_data]
disease_burden_percentiles = np.percentile(total_disease_counts, [50, 75, 90])
for row in country_data:
total_disease_burden = row.total_malaria + row.total_dengue
if total_disease_burden >= disease_burden_percentiles[2]:
risk_level = "极高风险"
elif total_disease_burden >= disease_burden_percentiles[1]:
risk_level = "高风险"
elif total_disease_burden >= disease_burden_percentiles[0]:
risk_level = "中等风险"
else:
risk_level = "低风险"
climate_suitability_score = (row.avg_country_temp / 35.0) * 0.6 + (row.avg_country_precipitation / 300.0) * 0.4
healthcare_effectiveness_ratio = row.avg_healthcare_budget / (total_disease_burden + 1) if total_disease_burden > 0 else 0
country_analysis.append({
"country": row.country,
"region": row.region,
"total_malaria": row.total_malaria,
"total_dengue": row.total_dengue,
"total_disease_burden": total_disease_burden,
"risk_level": risk_level,
"avg_temperature": round(row.avg_country_temp, 2),
"avg_precipitation": round(row.avg_country_precipitation, 2),
"population_density": round(row.avg_population_density, 2),
"climate_suitability_score": round(climate_suitability_score, 4),
"healthcare_effectiveness": round(healthcare_effectiveness_ratio, 4)
})
region_aggregated = climate_disease_df.groupBy("region").agg(
sum("malaria_cases").alias("region_total_malaria"),
sum("dengue_cases").alias("region_total_dengue"),
avg("avg_temp_c").alias("region_avg_temp"),
avg("precipitation_mm").alias("region_avg_precipitation")
)
regional_comparison = []
for row in region_aggregated.collect():
regional_disease_density = (row.region_total_malaria + row.region_total_dengue) / 1000
regional_comparison.append({
"region": row.region,
"total_malaria": row.region_total_malaria,
"total_dengue": row.region_total_dengue,
"disease_density": round(regional_disease_density, 2),
"avg_temp": round(row.region_avg_temp, 2),
"avg_precipitation": round(row.region_avg_precipitation, 2)
})
return JsonResponse({"country_hotspots": country_analysis, "regional_comparison": regional_comparison, "analysis_status": "success"})
@csrf_exempt
def climate_correlation_factor_analysis(request):
correlation_df = climate_disease_df.select("avg_temp_c", "precipitation_mm", "air_quality_index", "uv_index", "malaria_cases", "dengue_cases", "population_density", "healthcare_budget")
pandas_correlation_df = correlation_df.toPandas()
correlation_matrix = pandas_correlation_df.corr()
correlation_results = {}
for i, col1 in enumerate(correlation_matrix.columns):
correlation_results[col1] = {}
for j, col2 in enumerate(correlation_matrix.columns):
correlation_results[col1][col2] = round(correlation_matrix.iloc[i, j], 4)
temp_ranges = [(0, 15), (15, 20), (20, 25), (25, 30), (30, 50)]
temperature_impact_analysis = []
for min_temp, max_temp in temp_ranges:
temp_filtered_df = climate_disease_df.filter((col("avg_temp_c") >= min_temp) & (col("avg_temp_c") < max_temp))
temp_stats = temp_filtered_df.agg(
avg("malaria_cases").alias("avg_malaria"),
avg("dengue_cases").alias("avg_dengue"),
count("*").alias("record_count")
).collect()[0]
if temp_stats.record_count > 0:
temperature_disease_efficiency = (temp_stats.avg_malaria + temp_stats.avg_dengue) / (max_temp - min_temp)
temperature_impact_analysis.append({
"temp_range": f"{min_temp}-{max_temp}°C",
"avg_malaria_cases": round(temp_stats.avg_malaria, 2),
"avg_dengue_cases": round(temp_stats.avg_dengue, 2),
"record_count": temp_stats.record_count,
"temperature_efficiency": round(temperature_disease_efficiency, 4)
})
precipitation_ranges = [(0, 50), (50, 150), (150, 250), (250, 500)]
precipitation_impact_analysis = []
for min_precip, max_precip in precipitation_ranges:
precip_filtered_df = climate_disease_df.filter((col("precipitation_mm") >= min_precip) & (col("precipitation_mm") < max_precip))
precip_stats = precip_filtered_df.agg(
avg("malaria_cases").alias("avg_malaria"),
avg("dengue_cases").alias("avg_dengue"),
count("*").alias("record_count")
).collect()[0]
if precip_stats.record_count > 0:
precipitation_disease_intensity = (precip_stats.avg_malaria + precip_stats.avg_dengue) / ((max_precip - min_precip) / 100)
precipitation_impact_analysis.append({
"precipitation_range": f"{min_precip}-{max_precip}mm",
"avg_malaria_cases": round(precip_stats.avg_malaria, 2),
"avg_dengue_cases": round(precip_stats.avg_dengue, 2),
"record_count": precip_stats.record_count,
"precipitation_intensity": round(precipitation_disease_intensity, 4)
})
return JsonResponse({
"correlation_matrix": correlation_results,
"temperature_analysis": temperature_impact_analysis,
"precipitation_analysis": precipitation_impact_analysis,
"analysis_status": "success"
})
基于大数据的气候驱动的疾病传播可视化分析系统-结语
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