基于Python爬虫的南京市二手房市场现状分析和热点研究

应用Python第三方库Requests来爬取二手房网站的房源数据,并利用Pandas库对爬取的数据进行结构化处理,运用Pyecharts库对二手房源数据进行多维度的深入分析及可视化呈现.从大量线上数据分析出南京市二手房源分布状况、市场热点及价格走势等信息,帮助购房者、房产中介等在市场活动中高效决策,为政府干预及监管二...     

基于Python的武汉二手房信息爬取及分析

文章采用Python爬虫工具对链家网武汉市各行政区在售二手房数据进行采集,并对爬取到的信息进行清洗,最后使用Matplotlib和Pyecharts库进行可视化分析。该方法实现了对二手房数据的综合性分析研判,有助于帮助买家做出更加科学合理的决策,同时分析二手房市场动向。     

基于Scrapy框架的网络爬虫分析与抓取实现

基于Scrapy框架设计网络爬虫程序,爬取某市二手房数据,并将分析结果通过统计图呈现出来。通过分析网站数据结构及Scrapy框架网络爬虫设计思路,详细展示了基于Scrapy框架进行数据爬取、数据分析的实现方法,并对网络爬虫存在的安全问题、法律问题进行了说明。     

基于Python爬虫的二手房信息数据可视化分析

利用Python网络爬虫技术对各大城市的二手房信息进行爬虫分析,通过数据可视化技术,直观地展示二手房价和房源数量的城市分布及市场热点,分析二手房源的面积、价格、户型情况等二手房市场现状,为政府干预房地产业提供参考,也为购房者提供帮助。     

基于ARIMA模型的二手房价格预测

 摘要： 近年来，我国一二线城市房价持续上涨，房屋成了人们日常生活讨论的热门话题，大家纷纷对未来的房价走势做出猜测。本文爬取国内某知名大型房产网站自2013年以来广州和深圳的二手房均价数据，采用ARIMA模型对未来的房价进行滚动预测，并使用RMSE对预测精度进行判断。结果表明，该模型可以对二手房均价进行持续预测，且预测精度较高，可为房屋买卖者提供参考。     

基于Python网络爬虫技术分析国内主流编程语言职位热度

每隔一段时间,国外多个专业网站会对编程语言进行排名,但这并不能反应国内市场对掌握不同编程语言技术人才的需求度.采用Python常用第三方库requests来爬取招聘网站信息,再通过pyecharts库生成数据图表,从而形象直观地看到我国不同城市间对主流编程语言职位热度.     

基于Hadoop平台的农产品价格数据爬取和存储系统的研究

目前许多大型农贸市场和农业信息商务平台都在实时发布每天各地区不同农产品的价格数据。针对数据更新快、数据量大、数据形式多样,使数据的爬取和存储以及后续的分析工作变得困难,提出基于Hadoop的农产品价格爬取及存储系统。利用HttpClient框架结合线程池通过多线程爬取,爬取结束后执行完整性检查,过滤出信息不完整的网页,进行二次爬取直到信息完整。对爬取到的网页使用正则表达式进行解析和清洗,提取有用的数据,以文本文件的形式存入HDFS(Hadoop Distributed File System),此后爬取到的数据以追加的方式写入HDFS文件中。实验表明HDFS的写入性能满足爬取数据不断递增的现状,副本数越少,数据块越大,写入性能越好。     

基于Python的电商网站服装数据的爬取与分析

电商网站上蕴藏着大量有价值的信息,同时中国的纺织服装产业消费市场非常大,因此,对电商网站上服装数据的爬取、分析非常有意义。为及时准确地获取当前服装产品的流行趋势、消费热点,以便于商家精准投放产品、消费者更理性消费,提出了针对电商网站服装数据的爬取算法fashionDataScrape。该算法将服装商品文字描述信息与图片信息的爬取相分离,具有一定的灵活性,同时能基于关键词爬取服装信息。给出了算法的详细设计类图。采用Python语言实现了该算法,其中主要使用了Requests和Beautiful Soup库,并用lxml作为HTML解析器。以“连衣裙女装新品”、“女装t恤”和“旗袍年轻版”为关键词分别爬取了相应的服装信息,对爬取结果和实际页面进行了人工对比,验证了算法的可行性和有效性。通过对爬取结果的商品描述分析、价格分析和图片的t-SNE聚类可视化分析,进一步验证了电商网站服装数据爬取的意义。     

基于Python的拉勾网数据爬取与分析

研究并设计实现了一个基于Python的爬虫,用来爬取拉勾网数据资源.针对想要的信息数据进行定向爬取,对爬取到的信息进行存储、数据清洗及可视化分析.最终得到全国范围内招聘Python工程师的公司以及相关的待遇及要求.     

基于Python的南京二手房数据爬取及分析

文章使用Python爬虫并结合数据分析技术,对链家网上南京二手房的所有房源数据进行采集、清洗,再对清洗后的数据进行可视化分析,研究隐藏在这些海量数据背后的规律.由此可获得南京二手房的基本属性特征以及二手房源的分布情况,起到购房决策辅助作用.     

Cost Sharing Warranty Policies for Second-hand Products

Warranties for second-hand products are becoming more important as consumers have started demanding protection against poor quality second-hand products and the high cost of product failures occurring shortly after purchase. In response to this consumer demand for protection, public policy makers have begun enacting laws requiring dealers to offer warranties and effectively service warranty claims. This is now apparent throughout the market place for cars, consumer durables and electrical and electronic goods. As a result, dealers of second-hand products need to do a proper cost analysis of alternative warranty policies before deciding on a warranty policy to be offered with the sale of a particular item. This paper deals with three new cost sharing warranty policies for second-hand products. Stochastic models have been developed and analysed for the cost of these warranty policies when offered with the sale of second-hand products.     

Modelling housing typologies for urban redevelopment scenario planning

Increasing levels of urbanization, combined with growing populations and a need to manage urban redevelopment more sustainably has prompted the need for new tools for urban regeneration in established urban areas. While significant activity is occurring in the areas of volumetric analysis and 3D visualization, utilising these technologies in the development of urban planning tools requires a data schema for defining precinct objects for performance assessment while simultaneously addressing the complexity and interconnected nature of issues relevant to the urban built environment. This paper presents the outcomes of the research and development of a web-based 3D precinct visualization and assessment system, Envision Scenario Planner (ESP), which uses a library of housing typologies to generate easy-to-use, bottom-up, precinct-scale reports on residential infill. The paper illustrates how, through the specification of a residential precinct object data schema and the provision of a set of housing typologies, end users can quickly, and without domain knowledge, generate visualizations and assessments for a variety of housing scenarios, which allows them to determine fit-for-purpose solutions that address a range of issues relevant to contemporary planners and policy makers.     

面向智慧民生领域的增量交互式数据集成方法

智慧民生作为智慧城市的重点领域,包含众多应用系统,积累了大量层次结构数据.为了形成城市范围完整数据集,需要集成并统一异构的数据模式,向用户提供统一的数据视图.针对智慧民生领域的领域知识宽泛、缺乏中文语义匹配支持、模式数量众多、元素标签缺失但实例数据丰富等几方面特点,提出了一种增量交互式模式集成方法.该方法采用增量迭代的方式逐步完成多模式集成任务,大幅降低集成计算量;在模式匹配阶段,综合利用模式信息和实例数据构造了多种适用于中文且能力互补的匹配器,并通过相似度熵来度量机器的决策置信度,适度引入人工干预;在中介模式生成阶段,处理模式间可能出现的各种冲突,最终输出全局统一的中介模式.利用从互联网爬取的多源二手房数据设计并完成实验,实验结果表明:此方法在人工干预程度足够小的前提下,具有较好的模式匹配准确性.     

基于数据仓库的高校教职工图书借阅多维分析

介绍构建高校图书借阅数据仓库的过程,建立多维图书借阅数据集,使用OLAP数据分析方法对教职工图书借阅进行多维分析,对结果进行认真的分析和解释,并指出今后高校教师应加强自身综合素质的提高才能适应高素质人才培养的要求。     

A hybrid fuzzy regression-fuzzy cognitive map algorithm for forecasting and optimization of housing market fluctuations

This paper presents a hybrid algorithm based on fuzzy linear regression (FLR) and fuzzy cognitive map (FCM) to deal with the problem of forecasting and optimization of housing market fluctuations. Due to the uncertainty and severe noise associated with the housing market, the application of crisp data for forecasting and optimization purposes is insufficient. Hence, in order to enable the decision-makers to make decisions with respect to imprecise/fuzzy data, FLR is used in the proposed hybrid algorithm. The best-fitted FLR model is then selected with respect to two indicators including Index of Confidence (IC) and Mean Absolute Percentage Error (MAPE). To achieve this objective, analysis of variance (ANOVA) for a randomized complete block design (RCBD) is employed. The primary objective of this study is to utilize imprecise/fuzzy data in order to improve the analysis of housing price fluctuations, in accordance with the factors obtained through the best-fitted FLR model. The secondary objective of this study is the exhibition of the resulted values in a schematic way via FCM. Hybridization of FLR and FCM provides a decision support system (DSS) for utilization of historical data to predict housing market fluctuation in the future and identify the influence of the other parameters. The proposed hybrid FLR-FCM algorithm enables the decision-makers to utilize imprecise and ambiguous data and represent the resulted values of the model more clearly. This is the first study that utilizes a hybrid intelligent approach for housing price and market forecasting and optimization.     

Residential property price time series forecasting with neural networks

The residential property market accounts for a substantial proportion of UK economic activity. Professional valuers estimate property values based on current bid prices (open market values). However, there is no reliable forecasting service for residential values with current bid prices being taken as the best indicator of future price movement. This approach has failed to predict the periodic market crises or to produce estimates of long-term sustainable value (a recent European Directive could be leading mortgage lenders towards the use of sustainable valuations in preference to the open market value). In this paper, we present artificial neural networks, trained using national housing transaction time series data, which forecasts future trends within the housing market.     

基于ASP.NET的网上二手交易平台的设计

该文以网上二手交易平台设计为主线,讨论了电子商务和网上二手交易平台的基本概念、国内外的发展状况．在ASP．NET相关技术和框架上,设计了网上二手交易平台的体系结构,功能模块等。     

基于ASP.NET的图书馆志愿者考勤系统

针对图书馆志愿者管理工作需要,利用ASP.NET开发平台和SQL后台数据库开发出一套便于操作和维护的图书馆志愿者考勤系统,系统从需求分析、柜架设计、数据库设计、数据流程图及模块的实现等方面进行介绍。     

基于二代身份证的房屋产权交易代理系统研究

针对当前手工录入个人基本信息效率低的问题,提出一种基于二代身份证的房屋产权交易代理系统。首先该系统采用USB接口连接二代身份证读卡器,然后在VB6开发平台中利用动态链接库技术,采取机读方法来读取二代身份证的个人基本信息,并快速有效的识别身份证真伪,最后实时存储在数据库中。实验结果表明,基于二代身份证的机读方法有效解决了个人基本信息录入的效率问题,能够满足系统对客户基本信息的录入验证要求,具有较好的市场应用前景。     

A multi-agent model of urban processes: Modelling relocation processes and price setting in housing markets

This paper introduces an agent-based micro-simulation model of housing market processes. The model describes aggregate housing market developments, such as price and turnover, as the outcome of households’ decisions to search for a new dwelling, accept an offered dwelling or sell their dwelling. An important feature of the model is that households’ decisions are based on perceptions of housing market probabilities. Households update these perceptions based on observed bargaining outcomes in the market. The model was tested in a simulation experiment and appeared to respond plausibly to different market settings in terms of prices and households’ perception of the market.