Design and implementation of Python Chongqing second-hand housing data visualization large-screen full-screen system (django framework)

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Design and implementation of Chongqing second-hand housing data visualization large-screen full-screen system (Django framework)

1. Research background and significance

With the advent of the digital age, data visualization has become an important means of information display in all walks of life. Especially in the real estate industry, data visualization can visually display market dynamics and help decision-makers make more informed decisions. However, for the Chongqing second-hand housing market, although there is a large amount of transaction data, there is a lack of a comprehensive, real-time, interactive data visualization platform to display these data. Therefore, designing and implementing such a system has very important practical significance.

2. Research status at home and abroad

There have been many studies on data visualization at home and abroad, especially in information display and decision support. However, there are few data visualization studies on specific fields such as Chongqing’s second-hand housing market. Although there are some general data visualization tools, such as Tableau and D3.js, they may not fully meet the special needs of Chongqing’s second-hand housing market. Therefore, we need to design and implement a large-screen full-screen data visualization system specifically targeted at the Chongqing second-hand housing market.

3. Research ideas and methods

This study will adopt the following research ideas and methods:

1. Data collection and analysis: Collect relevant data on Chongqing's second-hand housing market, including historical transaction data, real-time transaction data, regional price data, etc., and clean and analyze these data.
2. System design: Based on the requirements analysis results, design the overall architecture, interface layout, data flow, etc. of the system.
3. Technical implementation: Use the Django framework and related technologies, such as Python programming language, HTML5, CSS3, JavaScript, etc., to implement various functional modules of the system.
4. User feedback and optimization: During the system implementation process, we maintain close communication with users, collect user feedback, and continuously optimize the system according to user needs.

4. Research content and innovation points

This research will mainly complete the following contents:

1. Design and implement a large-screen full-screen data visualization system based on the Django framework.
2. Real-time updating and visual display of data, including comparison and analysis of historical data and real-time transaction data.
3. Realize the visual display of regional price data to help users understand the housing price trends and price distribution in different regions.
4. Implement interactive functions for user feedback and data, allowing users to understand and analyze data more deeply.

The innovative points of this study are:

1. Based on the characteristics of Chongqing's second-hand housing market, a specialized data visualization large-screen full-screen system was designed and implemented.
2. It combines real-time transaction data and historical data to achieve comprehensive analysis and display of data.
3. Advanced data visualization technologies, such as big data visualization, heat maps, etc., are used to make the data display more vivid and intuitive.
4. The interactive function of user feedback and data is implemented, allowing users to understand and analyze the data more deeply.

5. Detailed introduction of front and back functions

This system mainly includes two functional modules: front-end display and back-end management.

The front-end display module mainly includes the following functions:

1. Data visualization display: Various data on Chongqing’s second-hand housing market are displayed on a large full screen, such as historical transaction data, real-time transaction data, regional price data, etc. At the same time, it provides a variety of data visualization forms, such as bar charts, line charts, heat maps, etc.
2. Data interaction function: Users can operate through the touch screen or mouse to zoom, pan, select and other operations on the data in order to understand and analyze the data more deeply. At the same time, the system also provides some preset query conditions and filters, allowing users to quickly find the data they are interested in.
3. Data update notification: When there is new transaction data or regional price data updates, the system will automatically notify the user and prompt the user to perform corresponding operations. At the same time, the system will also recommend some data and information that may be of interest based on the user's operation history and learning records.

6. Research ideas, research methods, and feasibility

This study will adopt the following research ideas and methods:

1. Demand analysis and definition: First, conduct in-depth research on the Chongqing second-hand housing market, understand user needs and expectations, and clarify the function and performance requirements of the system.
2. Technical research: Learn and master the Django framework and other related technologies, such as Python programming language, HTML5, CSS3, JavaScript, etc., as well as data visualization technology and interaction design technology.
3. System design: Based on the results of demand analysis and definition, design the overall architecture, interface layout, data flow, etc. of the system, including the design of two functional modules, front-end display and back-end management.
4. System implementation: Use the Django framework and other related technologies to perform specific coding and implementation work according to the system design documents.
5. Testing and optimization: Test the implemented system, including functional testing, performance testing, compatibility testing, etc., and optimize and improve based on the test results.
6. User feedback and evaluation: After the system implementation and testing is completed, communicate and exchange with users, collect user feedback and evaluation opinions, and further optimize and improve the system.

The feasibility of this study lies in:

1. This research team has relevant technical reserves and experience accumulation, and has mastered certain programming technology and Web development skills.
2. In terms of market demand, there is a large amount of transaction data in the Chongqing second-hand housing market, and users have an urgent need for a comprehensive, real-time, and interactive data visualization platform.
3. In terms of research resources, this research team will make full use of existing software, hardware and network resources, including laboratory computers, network environments and various open source software.
4. In terms of time arrangement, the research team will reasonably plan the research progress, fully consider various possible factors and situations, and ensure that the research tasks are completed on time.

7. Research progress arrangement

This research will be conducted in the following stages:

1. The first stage (1-2 months): needs analysis and definition, technical research and learning.
2. The second stage (3-4 months): system design, including overall architecture, interface layout, data flow, etc.
3. The third stage (5-6 months): System implementation, including coding and testing.
4. The fourth stage (7-8 months): User feedback and evaluation, the system will be further optimized and improved based on user feedback and evaluation opinions.
5. The fifth stage (9-10 months): Write a thesis and summary report, and complete the graduation project task.

8. Thesis (design) writing outline

The following is the writing outline for the thesis (design) of this graduation project:

Chapter 1 Introduction

1. research background and meaning
2. Research purpose and tasks
3. Research methods and technical routes
4. Thesis structure arrangement

Chapter 2 Overview of Chongqing’s second-hand housing market

1. Chongqing second-hand housing market overview
2. Characteristics of Chongqing’s second-hand housing market
3. Problems and challenges in Chongqing’s second-hand housing market
4. The application prospects of data visualization in Chongqing’s second-hand housing market

Chapter 3 Overview of Data Visualization Technology

1. Basic concepts of data visualization
2. Classification and characteristics of data visualization technology
3. The role and value of data visualization in information display and decision support
4. Application and advantages of Django framework in data visualization

Chapter 4 System Requirements Analysis and Definition

1. User demand research and analysis
2. Definition of system functional requirements and performance requirements
3. Data flow and data processing requirements analysis
4. System interface layout and interaction design requirements analysis

Chapter 5 System Design

1. Overall system architecture design
2. Data storage and data processing module design
3. Data visualization module design
4. User interaction and feedback module design
5. System interface layout and style design
6. System testing and evaluation program design

Chapter 6 System Implementation

1. Introduction
   In this chapter, we will introduce in detail how to use the Django framework to implement a large-screen full-screen system for Chongqing second-hand housing data visualization.

2. Technology stack and tool selection

• Django: As a backend framework, Django can efficiently handle requests, database operations, and business logic.
• Python: As a powerful programming language, Python can facilitate data processing and visualization.
• HTML5, CSS3 and JavaScript: used to build user-friendly interfaces and implement interactive functions.
• Data visualization libraries: such as ECharts, Matplotlib, and Seaborn for graphically presenting data.
• Database: Select MySQL as the database to store Chongqing second-hand housing data.

1. System implementation process

• Database design and implementation: Based on demand analysis, design the database table structure, and use Python's SQLAlchemy library to implement database operations.
• Data collection and cleaning: collect data through API interface or other methods, and use Python for data cleaning and preprocessing.
• Data visualization: Use data visualization libraries to present data graphically, such as using the ECharts library to implement bar charts, line charts, heat maps, etc.
• User interaction: Use JavaScript and HTML5 to implement user interaction functions, such as zooming, panning, and selecting operations.
• Backend management: Use Django's backend management function to implement operations such as adding, deleting, modifying, and checking data.

1. Test and Optimize

• Functional testing: Test whether the various functions of the system are working properly.
• Performance testing: Test the response speed and load capacity of the system.
• Compatibility testing: Test the performance of the system on different browsers and devices.
• Optimization: Optimize based on test results, such as using cache to improve performance, optimizing interface layout, etc.

Chapter 7 User Feedback and Evaluation

1. Introduction
   In this chapter, we will introduce how to collect user feedback, evaluate system performance and effectiveness, and make optimization improvements based on feedback.

2. User feedback collection

• User research: Collect users’ opinions and suggestions on the system through questionnaires, face-to-face interviews, etc.
• Practical use: Let users use the system in a real environment and collect feedback.
• Competitive product analysis: Analyze competitors’ product features and usage to provide reference for system optimization.

1. System performance evaluation

• Response time: Evaluate system response time to ensure that the system can respond to user requests within a reasonable time.
• Concurrency: Test the performance of the system when processing multiple requests at the same time to ensure that the system can withstand high concurrent loads.
• Data accuracy: Verify the accuracy of data reports and visualizations generated by the system to ensure that system data is correct.

1. Optimization and improvement
   Optimize and improve the system based on user feedback and system performance evaluation results. Possible improvement directions include improving interface design, optimizing data processing processes, enhancing data visualization effects, etc. After each optimization improvement, it needs to be tested and evaluated again to ensure that the improvement effect meets expectations.

Chapter 8 Conclusion and Outlook

1. Research Conclusion
   In this chapter, we will summarize the main results and conclusions of this research, including the design and implementation of a large-screen full-screen system for Chongqing second-hand housing data visualization based on the Django framework , realizing real-time updating and visual display of data. At the same time, we will also discuss the shortcomings in the research and the directions for future improvements.

2. Research Outlook
   The research and implementation of the large-screen full-screen system for Chongqing second-hand housing market data visualization provides reference and reference for data visualization in other similar fields. In the future, the functions and application scope of the system can be further expanded, such as adding data mining and analysis functions, and promoting its application to other real estate markets. At the same time, with the continuous development of technology, you can try to introduce more advanced data visualization technology and interactive design technology to improve the display effect and user experience of the system.

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research background and meaning

With the continuous advancement of urbanization, the development of the real estate market has attracted more and more attention. In today's real estate market, the second-hand housing market is a part that cannot be ignored. Chongqing is a city with a large population and rapid economic development, and the second-hand housing market is also increasingly prosperous. How to better understand the trends and changes in Chongqing's second-hand housing market is of great significance to both real estate practitioners and ordinary residents.

Therefore, this article aims to design and implement a large-screen full-screen system for visualizing second-hand housing data in Chongqing through data visualization methods to help people better understand the situation of Chongqing’s second-hand housing market and thereby better invest and purchase houses.

Research status at home and abroad

In China, many scholars and data analysts have carried out data research on the second-hand housing market. For example, some studies focus on the distribution and prediction of housing prices, some on the characteristics and purchasing habits of home buyers, and some on the processes and laws and regulations of second-hand housing transactions.

There are many similar studies abroad. For example, companies such as Zillow and Redfin in the United States provide data-based second-hand housing market analysis and forecasting services. These companies use complex data analysis algorithms to comprehensively analyze market data from multiple angles to help users better understand market trends.

Although there have been many related studies, the research on the data visualization large-screen full-screen system for the second-hand housing market in Chongqing is not sufficient. Therefore, the research in this article has certain significance in attracting people's attention and understanding of this market.

Research ideas and methods

The research idea of ​​this article is based on data visualization method. We collect and organize various data on the second-hand housing market in Chongqing, such as house area, price, location, room type, transaction time, etc. Through visualization technology, these data are displayed in charts, maps, etc., to more intuitively display data characteristics and market trends. At the same time, we will also use machine learning technology to analyze and predict market data to predict market trends and housing price trends.

The research method of this article is web application development based on the Django framework. Django is a free open source Web application framework that adopts the MVC (Model View Controller) architecture pattern. It is a fast-developing, safe, stable, and easily scalable Web framework.

Research internal customers and innovation points

The internal client studied in this article is to design and implement a large-screen full-screen system for visualizing second-hand housing data in Chongqing. The system has the following innovations:

1. Data visualization: Display market data through visual methods such as charts and maps to present market conditions more intuitively.

2. Machine learning prediction: Use machine learning algorithms to analyze and predict market data, and accurately predict market trends and housing price trends.

3. Full-screen display: Using full-screen display to present data, users can more easily view and understand market conditions.

4. Responsive design: Using responsive design, it can be displayed adaptively on different devices, making it easier for users to view.

Detailed introduction of front and back functions

The front desk of the system mainly includes the following functions:

1. Home page: Displays market overview information, such as today's market conditions, transaction volume, average price, etc.

2. Data visualization: Display market data through visual methods such as charts and maps, such as house area, price, location, room type, transaction time, etc.

3. Machine learning prediction: Use machine learning algorithms to analyze and predict market data, and accurately predict market trends and housing price trends.

4. News and information: Display news and information about the second-hand housing market to facilitate users to understand the market situation.

5. Login and registration: Users can register and log in to the system to view more market information and data analysis results.

The backend of the system mainly includes the following functions:

1. Administrator login: Administrators can log in to the system backend to manage data and information in the system.

2. Data management: Administrators can upload, modify and delete data, including house area, price, location, room type, transaction time, etc.

3. User management: Administrators can manage user information, including registration information, purchase records, personal information, etc.

4. Data analysis: Administrators can use machine learning algorithms to analyze and predict market data, and accurately predict market trends and housing price trends.

Research ideas, research methods, feasibility

The research idea of ​​this article is based on the data visualization method, using machine learning technology to analyze and predict market data, and designing and implementing a large-screen full-screen system for visualizing second-hand housing data in Chongqing. The system is developed using the Django framework and has good scalability and maintainability, as well as excellent performance and security.

Research schedule

The estimated project schedule is as follows:

1. Requirements analysis and project planning (1 week)

2. Data collection and organization (2 weeks)

3. Data visualization design and implementation (3 weeks)

4. Machine learning algorithm implementation (4 weeks)

5. Front and backend development (6 weeks)

6. Testing and Optimization (2 weeks)

7. Writing papers and PPT (2 weeks)

Total of 16 weeks.

Thesis (design) writing outline

1. introduction

1.1 Background and significance of topic selection

1.2 Research status at home and abroad

1.3 Research ideas and methods

1.4 Internal customers and innovation points of the research

1.5 Paper structure

1. Requirements analysis and system design

2.1 Requirements analysis

2.2 System design

1. Data collection and organization

3.1 Data source

3.2 Data processing

1. Visual design and implementation

4.1 Visual design

4.2 Visual implementation

1. Machine learning prediction

5.1 Machine learning algorithm

5.2 Prediction Realization

1. Front and backend development

6.1 Front-end development

6.2 Backend development

1. System testing and optimization

7.1 System testing

7.2 System optimization

1. Summary and outlook

8.1 Work summary

8.2 Research prospects