计算机编程指导师
⭐⭐个人介绍：自己非常喜欢研究技术问题！专业做Java、Python、小程序、安卓、大数据、爬虫、Golang、大屏、爬虫、深度学习、机器学习、预测等实战项目。
⛽⛽实战项目：有源码或者技术上的问题欢迎在评论区一起讨论交流！
⚡⚡如果遇到具体的技术问题或计算机毕设方面需求，你也可以在主页上咨询我~~
⚡⚡获取源码主页--> space.bilibili.com/35463818075…
人口收入分析系统- 简介
基于大数据的人口普查收入数据分析与可视化系统是一个面向大规模人口统计数据处理与分析的综合性平台。该系统采用Hadoop分布式文件系统作为数据存储基础，结合Spark大数据处理引擎实现海量人口普查数据的快速计算与分析。系统后端基于Spring Boot框架构建RESTful API服务，集成MyBatis持久层框架进行MySQL数据库操作，前端采用Vue.js结合ElementUI组件库搭建用户界面，通过Echarts图表库实现丰富的数据可视化展示。系统核心功能涵盖人口收入分布统计、性别收入差异分析、种族收入对比、年龄段收入能力评估、教育背景收入关联度分析、职业收入水平对比、工作时长收入关系探索、婚姻状况收入影响分析以及资本收益结构分析等多个维度。通过Spark SQL进行复杂数据查询，利用Pandas和NumPy进行数据预处理，系统能够从多个角度深入挖掘人口普查数据中的收入规律，为政策制定者、社会学研究者以及经济分析师提供直观的数据洞察和决策支持。
人口收入分析系统-技术 框架
开发语言：Python或Java（两个版本都支持）
大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
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
人口收入分析系统- 背景
在当今社会经济快速发展的背景下，人口结构变化与收入分配问题日益成为政府部门和学术机构关注的焦点。人口普查作为国家掌握人口基本情况的重要手段，积累了大量涉及个体年龄、性别、教育背景、职业类型、工作状况以及收入水平等多维度的详细数据。这些数据蕴含着丰富的社会经济规律，能够反映不同群体的生活状况、就业特征以及收入差异，对于深入理解社会结构变迁和经济发展趋势具有重要价值。然而，传统的数据处理方式往往难以应对人口普查数据的海量特征和复杂结构，无法充分挖掘数据背后的深层次关联和规律。随着大数据技术的不断成熟，利用Hadoop、Spark等分布式计算框架处理大规模数据集已成为可能，这为人口普查数据的深度分析提供了技术基础和新的研究思路。
本课题的研究意义体现在理论探索和实践应用两个层面。从理论角度来看，通过构建基于大数据技术的人口收入分析系统，能够验证分布式计算在社会统计学数据处理中的有效性，为类似的大规模数据分析项目提供技术参考和实施经验。系统运用多维度数据挖掘方法，探索人口特征与收入水平之间的内在联系，有助于丰富人力资本理论和收入分配理论的实证研究基础。从实践应用角度来看，系统能够为相关部门提供直观的数据洞察工具，帮助理解不同群体的收入状况和影响因素，为制定更加精准的社会政策和经济措施提供数据支撑。同时，系统的可视化功能使复杂的统计结果更容易被理解和传播，有利于促进社会各界对收入分配问题的关注和讨论。此外，本课题作为大数据技术在社会科学领域的应用实践，也为计算机专业学生提供了一个较好的技术学习和能力锻炼平台，有助于培养跨学科的数据分析思维和解决实际问题的能力。
人口收入分析系统-视频展示
人口收入分析系统-图片展示
人口收入分析系统-代码展示
from pyspark.sql.functions import col, count, avg, when, sum as spark_sum
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.clustering import KMeans
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
@csrf_exempt
def income_distribution_analysis(request):
spark = SparkSession.builder.appName("IncomeDistributionAnalysis").master("local[*]").getOrCreate()
df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/census_data/adult.csv")
total_count = df.count()
high_income_count = df.filter(col("income") == ">50K").count()
low_income_count = df.filter(col("income") == "<=50K").count()
high_income_ratio = round((high_income_count / total_count) * 100, 2)
low_income_ratio = round((low_income_count / total_count) * 100, 2)
gender_income_stats = df.groupBy("sex", "income").count().collect()
gender_income_dict = {}
for row in gender_income_stats:
gender = row["sex"]
income_level = row["income"]
count_val = row["count"]
if gender not in gender_income_dict:
gender_income_dict[gender] = {}
gender_income_dict[gender][income_level] = count_val
race_income_stats = df.groupBy("race").agg(
count("*").alias("total_count"),
spark_sum(when(col("income") == ">50K", 1).otherwise(0)).alias("high_income_count")
).collect()
race_income_data = []
for row in race_income_stats:
race = row["race"]
total = row["total_count"]
high_income = row["high_income_count"]
high_income_percentage = round((high_income / total) * 100, 2) if total > 0 else 0
race_income_data.append({
"race": race,
"total_count": total,
"high_income_count": high_income,
"high_income_percentage": high_income_percentage
})
age_bins = [(17, 30), (31, 45), (46, 60), (61, 100)]
age_income_data = []
for min_age, max_age in age_bins:
age_group_df = df.filter((col("age") >= min_age) & (col("age") <= max_age))
total_in_group = age_group_df.count()
high_income_in_group = age_group_df.filter(col("income") == ">50K").count()
high_income_ratio_group = round((high_income_in_group / total_in_group) * 100, 2) if total_in_group > 0 else 0
age_income_data.append({
"age_group": f"{min_age}-{max_age}",
"total_count": total_in_group,
"high_income_count": high_income_in_group,
"high_income_ratio": high_income_ratio_group
})
result_data = {
"total_statistics": {
"total_count": total_count,
"high_income_count": high_income_count,
"low_income_count": low_income_count,
"high_income_ratio": high_income_ratio,
"low_income_ratio": low_income_ratio
},
"gender_income_stats": gender_income_dict,
"race_income_data": race_income_data,
"age_income_data": age_income_data
}
spark.stop()
return JsonResponse(result_data, safe=False)
@csrf_exempt
def occupation_work_analysis(request):
spark = SparkSession.builder.appName("OccupationWorkAnalysis").master("local[*]").getOrCreate()
df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/census_data/adult.csv")
workclass_income_stats = df.groupBy("workclass").agg(
count("*").alias("total_count"),
spark_sum(when(col("income") == ">50K", 1).otherwise(0)).alias("high_income_count"),
avg(when(col("income") == ">50K", 1).otherwise(0)).alias("high_income_ratio")
).orderBy(col("high_income_ratio").desc()).collect()
workclass_data = []
for row in workclass_income_stats:
workclass_data.append({
"workclass": row["workclass"],
"total_count": row["total_count"],
"high_income_count": row["high_income_count"],
"high_income_ratio": round(row["high_income_ratio"] * 100, 2)
})
occupation_income_stats = df.groupBy("occupation").agg(
count("*").alias("total_count"),
spark_sum(when(col("income") == ">50K", 1).otherwise(0)).alias("high_income_count")
).collect()
occupation_data = []
for row in occupation_income_stats:
total = row["total_count"]
high_income = row["high_income_count"]
high_income_percentage = round((high_income / total) * 100, 2) if total > 0 else 0
occupation_data.append({
"occupation": row["occupation"],
"total_count": total,
"high_income_count": high_income,
"high_income_percentage": high_income_percentage
})
occupation_data.sort(key=lambda x: x["high_income_percentage"], reverse=True)
hours_bins = [(1, 20), (21, 40), (41, 60), (61, 100)]
hours_income_data = []
for min_hours, max_hours in hours_bins:
hours_group_df = df.filter((col("hours_per_week") >= min_hours) & (col("hours_per_week") <= max_hours))
total_in_group = hours_group_df.count()
high_income_in_group = hours_group_df.filter(col("income") == ">50K").count()
avg_hours = hours_group_df.agg(avg("hours_per_week").alias("avg_hours")).collect()[0]["avg_hours"]
high_income_ratio_group = round((high_income_in_group / total_in_group) * 100, 2) if total_in_group > 0 else 0
hours_income_data.append({
"hours_range": f"{min_hours}-{max_hours}",
"total_count": total_in_group,
"high_income_count": high_income_in_group,
"high_income_ratio": high_income_ratio_group,
"avg_hours": round(avg_hours, 1) if avg_hours else 0
})
high_income_occupations = df.filter(col("income") == ">50K").groupBy("occupation").count().orderBy(col("count").desc()).collect()
high_income_occupation_composition = []
total_high_income = df.filter(col("income") == ">50K").count()
for row in high_income_occupations:
occupation_count = row["count"]
occupation_percentage = round((occupation_count / total_high_income) * 100, 2)
high_income_occupation_composition.append({
"occupation": row["occupation"],
"count": occupation_count,
"percentage": occupation_percentage
})
result_data = {
"workclass_analysis": workclass_data,
"occupation_analysis": occupation_data,
"hours_analysis": hours_income_data,
"high_income_composition": high_income_occupation_composition
}
spark.stop()
return JsonResponse(result_data, safe=False)
@csrf_exempt
def advanced_clustering_analysis(request):
spark = SparkSession.builder.appName("AdvancedClusteringAnalysis").master("local[*]").getOrCreate()
df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/census_data/adult.csv")
df_cleaned = df.na.drop()
numerical_features = ["age", "education_num", "hours_per_week", "capital_gain", "capital_loss"]
feature_assembler = VectorAssembler(inputCols=numerical_features, outputCol="features")
df_features = feature_assembler.transform(df_cleaned)
kmeans = KMeans(k=4, seed=42, featuresCol="features", predictionCol="cluster")
model = kmeans.fit(df_features)
clustered_df = model.transform(df_features)
cluster_stats = clustered_df.groupBy("cluster").agg(
count("*").alias("cluster_size"),
avg("age").alias("avg_age"),
avg("education_num").alias("avg_education"),
avg("hours_per_week").alias("avg_hours"),
avg("capital_gain").alias("avg_capital_gain"),
avg("capital_loss").alias("avg_capital_loss"),
spark_sum(when(col("income") == ">50K", 1).otherwise(0)).alias("high_income_count")
).collect()
cluster_analysis_results = []
for row in cluster_stats:
cluster_id = row["cluster"]
cluster_size = row["cluster_size"]
high_income_count = row["high_income_count"]
high_income_ratio = round((high_income_count / cluster_size) * 100, 2)
cluster_analysis_results.append({
"cluster_id": cluster_id,
"cluster_size": cluster_size,
"avg_age": round(row["avg_age"], 1),
"avg_education": round(row["avg_education"], 1),
"avg_hours": round(row["avg_hours"], 1),
"avg_capital_gain": round(row["avg_capital_gain"], 2),
"avg_capital_loss": round(row["avg_capital_loss"], 2),
"high_income_count": high_income_count,
"high_income_ratio": high_income_ratio
})
for cluster_id in range(4):
cluster_data = clustered_df.filter(col("cluster") == cluster_id)
occupation_distribution = cluster_data.groupBy("occupation").count().orderBy(col("count").desc()).limit(5).collect()
education_distribution = cluster_data.groupBy("education").count().orderBy(col("count").desc()).limit(3).collect()
marital_distribution = cluster_data.groupBy("marital_status").count().orderBy(col("count").desc()).limit(3).collect()
for result in cluster_analysis_results:
if result["cluster_id"] == cluster_id:
result["top_occupations"] = [{"occupation": row["occupation"], "count": row["count"]} for row in occupation_distribution]
result["top_educations"] = [{"education": row["education"], "count": row["count"]} for row in education_distribution]
result["top_marital_status"] = [{"marital_status": row["marital_status"], "count": row["count"]} for row in marital_distribution]
capital_gain_analysis = df.groupBy("income").agg(
avg("capital_gain").alias("avg_capital_gain"),
avg("capital_loss").alias("avg_capital_loss")
).collect()
capital_analysis_data = []
for row in capital_gain_analysis:
capital_analysis_data.append({
"income_level": row["income"],
"avg_capital_gain": round(row["avg_capital_gain"], 2),
"avg_capital_loss": round(row["avg_capital_loss"], 2)
})
result_data = {
"cluster_analysis": cluster_analysis_results,
"capital_analysis": capital_analysis_data,
"model_summary": {
"total_clusters": 4,
"features_used": numerical_features,
"clustering_algorithm": "K-Means"
}
}
spark.stop()
return JsonResponse(result_data, safe=False)
人口收入分析系统-结语
Hadoop+Spark技术不会用？基于大数据的人口收入分析系统手把手教你实现 基于大数据的人口收入分析系统计算机毕设：Python+Django+Spark完整技术栈 如果你觉得本文有用，一键三连（点赞、评论、转发）欢迎关注我，就是对我最大支持~~ 也期待在评论区或私信看到你的想法和建议，一起交流探讨！谢谢大家！
⚡⚡获取源码主页--> space.bilibili.com/35463818075…
⚡⚡如果遇到具体的技术问题或计算机毕设方面需求，你也可以在主页上咨询我~~
