💖💖作者：计算机毕业设计江挽 💙💙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
基于大数据的深圳一手房成交数据分析系统介绍
一手房成交数据分析系统是一个基于大数据技术架构的房产数据深度分析平台，该系统采用Hadoop分布式存储框架和Spark大数据计算引擎作为核心技术底座，通过Python数据科学库和Java企业级开发框架相结合的方式，构建了完整的数据采集、存储、计算和可视化分析链路。系统前端采用Vue.js响应式框架配合ElementUI组件库和ECharts图表库，为用户提供直观友好的交互界面，后端支持Django和SpringBoot两套技术方案，能够灵活适应不同的部署环境需求。系统核心功能涵盖房屋成交信息管理、房产交易时序分析、各区房产对比分析、房产用途结构分析、市场供需状况分析、关联性探索分析以及大屏可视化分析等七大模块，通过Spark SQL进行复杂数据查询和统计分析，结合Pandas和NumPy进行数据预处理和数值计算，最终将分析结果以多维度图表形式呈现，为房地产市场研究和决策支持提供数据驱动的科学依据。
基于大数据的深圳一手房成交数据分析系统演示视频
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基于大数据的深圳一手房成交数据分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, sum, avg, max, min, desc, asc, when, year, month, dayofmonth
from pyspark.sql.types import StructType, StructField, StringType, IntegerType, DoubleType, DateType
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import json
spark = SparkSession.builder.appName("HouseDataAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def house_transaction_time_analysis(start_date, end_date, region_filter=None):
house_df = spark.sql("SELECT * FROM house_transactions WHERE transaction_date BETWEEN '{}' AND '{}'".format(start_date, end_date))
if region_filter:
house_df = house_df.filter(col("region") == region_filter)
monthly_stats = house_df.groupBy(year("transaction_date").alias("year"), month("transaction_date").alias("month")).agg(
count("house_id").alias("transaction_count"),
avg("price").alias("avg_price"),
sum("area").alias("total_area"),
avg("area").alias("avg_area"),
max("price").alias("max_price"),
min("price").alias("min_price")
).orderBy("year", "month")
daily_trend = house_df.groupBy("transaction_date").agg(
count("house_id").alias("daily_count"),
avg("price").alias("daily_avg_price")
).orderBy("transaction_date")
price_trend_analysis = house_df.groupBy(year("transaction_date").alias("year"), month("transaction_date").alias("month")).agg(
avg("price").alias("monthly_avg_price")
).orderBy("year", "month")
price_changes = price_trend_analysis.collect()
growth_rates = []
for i in range(1, len(price_changes)):
prev_price = price_changes[i-1]["monthly_avg_price"]
curr_price = price_changes[i]["monthly_avg_price"]
growth_rate = ((curr_price - prev_price) / prev_price) * 100 if prev_price > 0 else 0
growth_rates.append({
"year": price_changes[i]["year"],
"month": price_changes[i]["month"],
"growth_rate": round(growth_rate, 2)
})
seasonal_analysis = house_df.groupBy(month("transaction_date").alias("month")).agg(
count("house_id").alias("seasonal_count"),
avg("price").alias("seasonal_avg_price")
).orderBy("month")
weekly_pattern = house_df.withColumn("day_of_week", dayofmonth("transaction_date") % 7).groupBy("day_of_week").agg(
count("house_id").alias("weekly_count"),
avg("price").alias("weekly_avg_price")
).orderBy("day_of_week")
result_data = {
"monthly_statistics": [row.asDict() for row in monthly_stats.collect()],
"daily_trends": [row.asDict() for row in daily_trend.collect()],
"price_growth_rates": growth_rates,
"seasonal_patterns": [row.asDict() for row in seasonal_analysis.collect()],
"weekly_patterns": [row.asDict() for row in weekly_pattern.collect()],
"summary": {
"total_transactions": house_df.count(),
"average_price": house_df.agg(avg("price")).collect()[0][0],
"price_volatility": house_df.agg(max("price") - min("price")).collect()[0][0]
}
}
return result_data
def regional_house_comparison_analysis(regions_list, analysis_period):
comparison_results = {}
base_query = "SELECT * FROM house_transactions WHERE transaction_date >= date_sub(current_date(), {})".format(analysis_period)
all_data = spark.sql(base_query)
for region in regions_list:
region_data = all_data.filter(col("region") == region)
basic_stats = region_data.agg(
count("house_id").alias("transaction_volume"),
avg("price").alias("average_price"),
avg("area").alias("average_area"),
avg("price_per_sqm").alias("average_unit_price"),
max("price").alias("highest_price"),
min("price").alias("lowest_price")
).collect()[0]
house_type_distribution = region_data.groupBy("house_type").agg(
count("house_id").alias("type_count"),
avg("price").alias("type_avg_price")
).collect()
price_ranges = region_data.withColumn(
"price_range",
when(col("price") < 1000000, "below_1M")
.when((col("price") >= 1000000) & (col("price") < 2000000), "1M_to_2M")
.when((col("price") >= 2000000) & (col("price") < 3000000), "2M_to_3M")
.otherwise("above_3M")
).groupBy("price_range").count().collect()
area_distribution = region_data.withColumn(
"area_range",
when(col("area") < 60, "small")
.when((col("area") >= 60) & (col("area") < 90), "medium")
.when((col("area") >= 90) & (col("area") < 120), "large")
.otherwise("extra_large")
).groupBy("area_range").agg(count("house_id").alias("area_count")).collect()
monthly_volume_trend = region_data.groupBy(
year("transaction_date").alias("year"),
month("transaction_date").alias("month")
).agg(count("house_id").alias("monthly_volume")).orderBy("year", "month").collect()
supply_demand_indicator = region_data.agg(
avg("days_on_market").alias("avg_days_on_market"),
count("house_id").alias("supply_volume")
).collect()[0]
comparison_results[region] = {
"basic_statistics": basic_stats.asDict(),
"house_type_analysis": [row.asDict() for row in house_type_distribution],
"price_distribution": [{"range": row["price_range"], "count": row["count"]} for row in price_ranges],
"area_distribution": [row.asDict() for row in area_distribution],
"volume_trends": [row.asDict() for row in monthly_volume_trend],
"market_indicators": supply_demand_indicator.asDict()
}
regional_ranking = []
for region, data in comparison_results.items():
ranking_score = (
data["basic_statistics"]["average_price"] * 0.3 +
data["basic_statistics"]["transaction_volume"] * 0.4 +
data["basic_statistics"]["average_unit_price"] * 0.3
)
regional_ranking.append({
"region": region,
"ranking_score": ranking_score,
"avg_price": data["basic_statistics"]["average_price"],
"volume": data["basic_statistics"]["transaction_volume"]
})
regional_ranking.sort(key=lambda x: x["ranking_score"], reverse=True)
return {
"regional_details": comparison_results,
"regional_ranking": regional_ranking,
"comparison_summary": {
"total_regions": len(regions_list),
"analysis_period_days": analysis_period,
"highest_volume_region": max(regional_ranking, key=lambda x: x["volume"])["region"],
"highest_price_region": max(regional_ranking, key=lambda x: x["avg_price"])["region"]
}
}
def market_correlation_analysis(correlation_factors):
house_data = spark.sql("SELECT * FROM house_transactions WHERE transaction_date >= date_sub(current_date(), 365)")
correlation_matrix = {}
numeric_columns = ["price", "area", "price_per_sqm", "floor_level", "building_age", "distance_to_subway"]
pandas_df = house_data.select(*numeric_columns).toPandas()
correlation_coefficients = pandas_df.corr()
for factor1 in correlation_factors:
correlation_matrix[factor1] = {}
for factor2 in correlation_factors:
if factor1 in correlation_coefficients.columns and factor2 in correlation_coefficients.columns:
correlation_matrix[factor1][factor2] = float(correlation_coefficients.loc[factor1, factor2])
price_correlation_analysis = house_data.select("price", "area", "floor_level", "building_age").toPandas()
price_area_correlation = np.corrcoef(price_correlation_analysis["price"], price_correlation_analysis["area"])[0, 1]
price_floor_correlation = np.corrcoef(price_correlation_analysis["price"], price_correlation_analysis["floor_level"])[0, 1]
price_age_correlation = np.corrcoef(price_correlation_analysis["price"], price_correlation_analysis["building_age"])[0, 1]
regional_correlation = house_data.groupBy("region").agg(
avg("price").alias("regional_avg_price"),
count("house_id").alias("regional_volume"),
avg("area").alias("regional_avg_area")
).toPandas()
region_price_volume_corr = np.corrcoef(regional_correlation["regional_avg_price"], regional_correlation["regional_volume"])[0, 1]
temporal_correlation = house_data.groupBy(month("transaction_date").alias("month")).agg(
avg("price").alias("monthly_avg_price"),
count("house_id").alias("monthly_volume")
).orderBy("month").toPandas()
time_price_correlation = np.corrcoef(temporal_correlation["month"], temporal_correlation["monthly_avg_price"])[0, 1]
time_volume_correlation = np.corrcoef(temporal_correlation["month"], temporal_correlation["monthly_volume"])[0, 1]
feature_importance_analysis = {}
for feature in ["area", "floor_level", "building_age", "distance_to_subway"]:
if feature in pandas_df.columns:
feature_price_corr = np.corrcoef(pandas_df["price"], pandas_df[feature])[0, 1]
feature_importance_analysis[feature] = {
"correlation_with_price": float(feature_price_corr),
"importance_level": "high" if abs(feature_price_corr) > 0.7 else "medium" if abs(feature_price_corr) > 0.4 else "low"
}
clustering_analysis = house_data.select("price", "area", "region").groupBy("region").agg(
avg("price").alias("cluster_avg_price"),
avg("area").alias("cluster_avg_area"),
count("house_id").alias("cluster_size")
).toPandas()
cluster_similarity_matrix = {}
regions = clustering_analysis["region"].tolist()
for i, region1 in enumerate(regions):
cluster_similarity_matrix[region1] = {}
for j, region2 in enumerate(regions):
price_diff = abs(clustering_analysis.iloc[i]["cluster_avg_price"] - clustering_analysis.iloc[j]["cluster_avg_price"])
area_diff = abs(clustering_analysis.iloc[i]["cluster_avg_area"] - clustering_analysis.iloc[j]["cluster_avg_area"])
similarity_score = 1 / (1 + price_diff/1000000 + area_diff/100)
cluster_similarity_matrix[region1][region2] = float(similarity_score)
return {
"correlation_matrix": correlation_matrix,
"key_correlations": {
"price_area_correlation": float(price_area_correlation),
"price_floor_correlation": float(price_floor_correlation),
"price_age_correlation": float(price_age_correlation),
"region_price_volume_correlation": float(region_price_volume_corr),
"time_price_correlation": float(time_price_correlation),
"time_volume_correlation": float(time_volume_correlation)
},
"feature_importance": feature_importance_analysis,
"cluster_analysis": cluster_similarity_matrix,
"analysis_summary": {
"strongest_correlation": max(correlation_matrix.values(), key=lambda x: max(x.values()) if x else 0),
"weakest_correlation": min(correlation_matrix.values(), key=lambda x: min(x.values()) if x else 0),
"total_correlations_analyzed": len(correlation_factors) ** 2
}
}
基于大数据的深圳一手房成交数据分析系统文档展示
💖💖作者：计算机毕业设计江挽 💙💙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
