UrbanFuncDelineator: POI Neural Network Embedding

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Presentation
Ammar Khairi
Project Owner

UrbanFuncDelineator: POI Neural Network Embedding

Funding Requested

$5,000 USD

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Overview

This project aims to develop an AI-powered approach to delineate urban functional use at a granular level using neural network embedding techniques on Points of Interest (POI) data. It will address the need for understanding the configuration and dynamics of disaggregated land/building uses in cities from a bottom-up perspective. The target is to provide a tool to assist urban planners, policymakers and city managers in monitoring urban functional patterns and changes, especially in flexible planning systems.

Proposal Description

Our Team

Ammar Khairi ML researcher with experience in urban & spatial analysis, efficent training of large language models (LLMs) and time series forecasting, in addition we have two final year computer science students who have experience in data science and software engineering.

View Team

Please explain how this future proposal will help our decentralized AI platform grow and how this ideation phase will contribute to that proposal.

By populating SingularityNET's platform with this urban analytics AI service, it contributes to the company's mission of creating a democratic and decentralized AI ecosystem. Urban planning and policymaking are key application areas that can benefit from AI, enhancing the diversity of use cases supported by the platform.

Clarify what outcomes (if any) will stop you from submitting a complete proposal in the next round.

There are no specific outcomes that would definitively stop me from submitting a complete proposal in the next round. However, potential challenges include:

1. Data limitations - Inability to acquire comprehensive, high-quality POI dataset.
2. Scalability issues - Computational bottlenecks in scaling models to very large urban areas. 
3. Interpretation difficulties - Struggling to find meaningful patterns or validate interpretations.
4. Misalignment with platform requirements - Technical gaps impacting integration with SingularityNET's decentralized platform.
5. Resource constraints - Limitations in computational power, personnel, or funding beyond the Ideation Pool.

Unless these risks materialize significantly, I expect to mitigate any emerging challenges and submit a complete proposal aligned with the New Projects Pool's requirements.

The core problem we are aiming to solve

The core problem I aim to solve is delineating and understanding urban functional areas in a granular, data-driven manner to support urban planning and policymaking.

Our specific solution to this problem

My specific solution is to develop an AI service that applies neural network embedding techniques on ubiquitous Points of Interest (POI) data to cluster urban regions into functional zones. By learning from the spatial patterns of POIs representing disaggregated land use, this AI service will provide a novel bottom-up approach to reveal the configuration and dynamics of functional areas within cities.

Project details

Urban areas are highly dynamic and complex systems, with intricate patterns of land use and human activities. Understanding the functional composition of cities at a granular level is crucial for urban planning, policymaking, and efficient resource allocation. However, traditional methods relying on remote sensing data or coarse-grained census information often fail to capture the disaggregated, mixed-use nature of contemporary urban environments.

The "UrbanFuncDelineator" project aims to address this challenge by developing an innovative AI service that delineates urban functional areas using neural network embedding techniques on ubiquitous Points of Interest (POI) data. POIs, such as restaurants, shops, parks, and other urban amenities, serve as a rich source of information about the actual utilization of urban spaces and buildings.

At the core of this project is the application of the Doc2Vec neural network model, which can simultaneously learn vector representations for both POI classes and urban spatial units (e.g., census tracts, neighborhoods) by capturing their functional context from the spatial distribution of POIs. These low-dimensional vectors encode the semantic relationships between POI types and the latent functional characteristics of urban areas.

By leveraging the learned vector representations, the AI service can compute functional similarities between POI classes, revealing how different land uses tend to cluster together in cities. Furthermore, it can group urban spatial units into distinct functional area clusters based on the vector similarities, effectively delineating zones with coherent functional identities.

To enhance interpretability, the project incorporates techniques such as topic modeling and enrichment analysis to annotate the identified functional area clusters with their dominant land use characteristics and interpretable labels (e.g., "Residential Suburbs," "Commercial District," "Educational Hub").

The "UrbanFuncDelineator" AI service provides a data-driven, bottom-up approach to understanding urban functional patterns, offering several key benefits:

1. Granular delineation of functional areas beyond traditional zoning classifications.
2. Ability to monitor changes in functional use over time, supporting adaptive urban management.
3. Integration of diverse data sources, including POIs from various providers and authoritative datasets.
4. Scalability to analyze functional patterns across multiple spatial scales and urban regions.
5. Interpretable outputs that can inform urban planning decisions, policy evaluations, and resource allocation strategies.

By populating SingularityNET's decentralized AI platform with this innovative service, the project aims to democratize access to advanced urban analytics capabilities, empowering urban planners, policymakers, researchers, and civic organizations to make more informed, data-driven decisions for sustainable and resilient city development.

Existing resources

  • High quality POI dataset with comprehensive categorical and spatial coverage.
  • Computational resources (GPUs/TPUs) for training large neural network models.

Proposal Video

DF Spotlight Day - DFR4 - Asim Nasr - UrbanFuncDelineator: POI Neural Network Embedding

3 June 2024
  • Total Milestones

    4

  • Total Budget

    $5,000 USD

  • Last Updated

    3 Jun 2024

Milestone 1 - Data preparation

Description

This milestone focuses on acquiring and preparing the dataset necessary for training the model. It involves collecting comprehensive data and ensuring it is clean and structured appropriately.

Deliverables

Acquire comprehensive POI dataset for the selected city/region. Pre-process and construct POI sequence data as model input.

Budget

$1,000 USD

Milestone 2 - Model Training

Description

This milestone involves training the Doc2Vec neural network model on the pre-processed POI data. The goal is to generate meaningful vector representations for each POI class and urban spatial unit.

Deliverables

Train the Doc2Vec neural network model on POI data. Obtain vector representations for POI classes and urban spatial units.

Budget

$1,000 USD

Milestone 3 - Functional Similarity Analysis and Clustering

Description

This milestone involves analyzing the trained model's outputs to identify functional similarities and clustering urban areas based on these similarities. It focuses on deriving meaningful groupings from the vector representations.

Deliverables

Calculate similarity scores between POI class vectors. Perform hierarchical clustering to identify functional POI groups. Cluster urban spatial units into functional areas using their vectors. Annotate functional area clusters through topic modelling and enrichment analysis.

Budget

$1,500 USD

Milestone 4 - Case Study Implementation and Documentation

Description

In this milestone, the methodology developed in the previous phases is implemented on a case study city, such as Greater London. The results are then validated, documented, and prepared for presentation and potential future funding.

Deliverables

Implement the methodology on a case study city like Greater London. Evaluate clustering performance against benchmarks. Validate functional area delineation with urban experts. Document the approach, findings and potential applications.

Budget

$1,500 USD

Join the Discussion (1)

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1 Comment
  • 0
    commentator-avatar
    CLEMENT
    Jun 1, 2024 | 1:22 PM

    I just quickly want to ask what would be your strategies for ensuring the fairness and accuracy of your proposed neural network embedding techniques, particularly in diverse and dynamic urban environments ? Well since you are in the ideation pool, you should have enough time to figure this out. 

Reviews & Rating

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9 ratings
  • 1
    user-icon
    Max1524
    Jun 10, 2024 | 3:48 AM

    Overall

    4

    • Feasibility 4
    • Viability 3
    • Desirabilty 4
    • Usefulness 4
    What is the key to technology?

    The team is presenting 4 milestones very well with a funding request of only $5000. This partly proves that the desire and enthusiasm is present in the team. The milestones have been divided into reasonable sections for the set goals. I commend this. I just suggest that the team should analyze more deeply the applied technology and the language used to increase the friendliness of the proposal to readers.

  • 0
    user-icon
    mivh1892
    May 23, 2024 | 12:48 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 5
    Harnessing AI for Detailed Urban Function

     The "UrbanFuncDelineator" project demonstrates strong potential in terms of feasibility, viability, desirability, and usefulness. The proposed AI service has the potential to revolutionize urban planning and policymaking by providing data-driven insights into urban function at a detailed level. By integrating this service into SingularityNET's decentralized AI platform, the project can contribute to a more democratic and accessible approach to urban planning and decision-making.

    Feasibility:

    • Technical Expertise: The project team possesses the necessary expertise in machine learning, spatial analysis, and urban planning, with experience in developing and deploying AI models for urban applications.
    • Data Availability: Access to high-quality POI datasets with comprehensive spatial coverage and classification is crucial for training and evaluating the proposed AI service.
    • Computational Resources: Training large neural network models requires significant computational resources (GPUs/TPUs) to ensure efficient processing and model performance.

    Viability:

    • Market Need: The demand for effective urban planning and policymaking tools is significant, and the proposed AI service addresses a critical gap in understanding and delineating urban function at a detailed level.
    • Integration with SingularityNET: Incorporating the AI service into SingularityNET's decentralized AI platform enhances its visibility, accessibility, and potential impact on urban planning and policy communities.
    • Sustainability: The project can explore potential revenue models, such as subscription plans or usage-based fees, to ensure long-term sustainability and support continuous development.

    Desirability:

    • Solving a Real Problem: The project tackles the challenge of accurately delineating and understanding urban functional areas, providing valuable insights for urban planners, policymakers, and researchers.
    • Data-Driven Approach: The AI service utilizes a data-driven approach, leveraging POI data to extract meaningful patterns and insights into urban function, making it more objective and reliable than traditional methods.
    • Scalability and Adaptability: The proposed methodology can be scaled to analyze urban function across different spatial scales and urban contexts, making it adaptable to diverse planning needs.

    Usefulness:

    • Detailed Functional Delineation: The AI service provides a more granular understanding of urban function compared to traditional zoning classifications, enabling more informed decision-making.
    • Monitoring Functional Change: The ability to track changes in urban function over time is crucial for adaptive urban management and responding to dynamic urban trends.
    • Multi-Source Data Integration: The project's ability to integrate diverse POI datasets and authoritative data sources enhances the comprehensiveness and accuracy of the functional analysis.

  • 1
    user-icon
    Tu Nguyen
    May 31, 2024 | 9:20 AM

    Overall

    4

    • Feasibility 3
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    POI Neural Network Embedding

    The core issue that this proposal wants to solve is to delineate and understand urban functional areas in detail, based on data to support urban planning and policy making. This proposal has the idea of ​​developing an AI service that applies neural network embedding techniques on popular Points of Interest data to cluster urban areas into functional zones.
    Personal opinion: They should share more details about each member, for example they should post members' social media links. Additionally, in the milestones section, they should identify the start and end times of the milestones.

  • 0
    user-icon
    Devbasrahtop
    May 20, 2024 | 1:02 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 5
    • Usefulness 5
    Decentralization in policy make with AI

    Overall

    The "UrbanFuncDelineator" concept presents an advanced and creative method of using AI to urban planning. It attempts to offer detailed insights into urban functioning patterns by utilizing neural network embedding techniques on Points of Interest (POI) data. The plan is coherent, includes precise deadlines, and supports SingularityNET's objective of democratizing AI. On the other hand, increased team experience and a more thorough risk mitigation plan would be beneficial for the project.

    Feasibility

    The project is feasible because it has a well-defined, step-by-step plan with attainable goals. The team possesses the required computer power and data, and their background in data science and machine learning is essential to the project's accomplishment. However, further information on mitigation techniques is required for potential problems such as data quality and scalability.

    Viability

    The project has a strong foundation in AI and urban analytics, making it viable. The milestones are clear, with specific deliverables and reasonable budgets. The plan to validate results with urban experts and document findings increases the project's credibility and potential for future funding. However, the proposal could provide more detail on long-term sustainability and potential scalability beyond the initial case study.

    Desirability 

    A key necessity in urban planning and policymaking is the project's ability to provide detailed, data-driven insights into urban functional zones. Urban planners, politicians, and academics will find it very appealing, as it may democratize access to advanced urban analytics by providing an AI service on SingularityNET's platform. Its desirability is further increased by the possibility of tracking changes over time and facilitating adaptive urban management.

    Usefulness

    The usefulness of this project is evident in its potential to provide detailed, actionable insights for urban planning and policymaking. The ability to delineate functional areas at a granular level and monitor changes over time aligns well with the goals of creating sustainable and resilient cities. By contributing to SingularityNET’s platform, the project enhances the diversity of AI services available, supporting the platform's growth and attracting a broader user base.

     

  • 1
    user-icon
    agdispenza
    May 20, 2024 | 1:40 AM

    Overall

    4

    • Feasibility 3
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Interesting analysis

    I think that it is an interesting proposal to carry out, with high value for local city governments. Regarding data collection, the method to be followed is not clear to me, and I believe it is a key activity for the platform to be enriched with relevant and useful data. Additionally, I think it is important to gain the trust of potential users to encourage their participation on the platform. Overall, I believe it is a highly useful proposal for cities, enabling local governments to make well-informed decisions. Good luck!

  • 0
    user-icon
    HieuTran
    Jun 1, 2024 | 10:29 AM

    Overall

    4

    • Feasibility 4
    • Viability 3
    • Desirabilty 4
    • Usefulness 4
    Using AI for Urban Planning and Policymaking

    Feasibility


    This idea especially advocates for the use of current resources, such as high-quality POI data with full geographic coverage and classification, as well as GPU/TPU for functional area design. Urban capacity. From there, distribute resources and create successful policies. These are strong points that make the plan extremely viable.

    Viability

    Budgets and milestones are carefully defined, with specified deliverables. The plan does not specify the time required to fulfil each milestone. In terms of human resources, Ammar Khairi is renowned as a project owner with the necessary skills and competence. However, Ammar Khairi is currently known as the project owner of a number of other ideas, like Next Prescription Predictor: GPT Medical Forecast, AI Satellite Insights for Sustainable Farming, and so on.If the project owner engages in proposals, being a funded member may have an impact on the proposal's implementation.

     

    Desirabilty

     

    Governments and administrators have a significant demand for market management and policymaking. AI technology that can link and update urban sites greatly assists policymakers in making decisions, especially when compared to today's traditional approaches. However, the project team must focus on one task: convincing administrators, governments, and planners to accept the adoption of this new technology.

    Usefulness

     
    Proposal to assist AI platforms in creating Doc2Vec model applications. This model can learn point-of-interest (POI) and urban spatial units. Furthermore, the team possesses GPU and TPU resources. These resources will help the AIMR platform expand in policymaking and urban management.

  • 0
    user-icon
    CLEMENT
    Jun 1, 2024 | 1:26 PM

    Overall

    4

    • Feasibility 3
    • Viability 3
    • Desirabilty 4
    • Usefulness 4
    This improves monitoring urban functional patterns

    From my viewpoint, this project addresses a critical need for understanding urban functional use at a granular level, providing valuable insights into the configuration and dynamics of land/building uses in cities. This can also contribute to the SNET AI Marketplace by offering a specialized solution for urban planning and management. 

     

  • 1
    user-icon
    Gombilla
    Jun 10, 2024 | 7:43 AM

    Overall

    4

    • Feasibility 4
    • Viability 3
    • Desirabilty 4
    • Usefulness 5
    Provides an understanding of land and building use

    I like the fact the this project has the potential to empowers urban planners, policymakers, and city managers with evidence-based insights to monitor and manage urban functional patterns and changes effectively.  However, the accuracy of your neural network embeddings heavily depends on the quality and completeness of POI data, which may vary across different regions. I hope the team can find a way around these potential data variations. Good luck to the team !

  • 0
    user-icon
    Joseph Gastoni
    May 22, 2024 | 8:55 AM

    Overall

    4

    • Feasibility 4
    • Viability 3
    • Desirabilty 4
    • Usefulness 5
    using POI data to understand urban functionality

    • This proposal outlines UrbanFuncDelineator, an AI service that uses POI data to understand urban functionality. Here's a breakdown of its strengths and weaknesses:

      Feasibility:

      • Moderate: The core concept (neural network embedding) is feasible, but requires expertise in AI development and access to high-quality POI data.
        • Strengths: Leverages existing AI techniques and potentially abundant POI data sources.
        • Weaknesses: Scaling the model to large cities and ensuring data quality and consistency across different regions could be challenging.

      Viability:

      • Moderate: Success depends on user adoption, addressing technical challenges, and a clear revenue model.
        • Strengths: The proposal offers a potentially valuable tool for urban planners and policymakers.
        • Weaknesses: The proposal lacks details on the competitive landscape, user acquisition strategy, and long-term sustainability.

      Desirability:

      • Moderate: For urban planners and policymakers concerned with data-driven decision making, this could be desirable.
        • Strengths: The proposal offers a potentially more granular and dynamic understanding of urban functionality compared to traditional methods.
        • Weaknesses: The proposal needs to address the interpretability of AI outputs and potential concerns about data privacy.

      Usefulness:

      • Moderate-High: The project has the potential to improve urban planning and policymaking, but its impact depends on the accuracy of the AI model, user adoption by relevant stakeholders, and the interpretability of results.
        • Strengths: The proposal offers a way to analyze urban functionality at a more granular level and monitor changes over time.
        • Weaknesses: The proposal lacks details on how the project will measure the effectiveness of the AI model in delineating functional areas and how it will be integrated into existing urban planning workflows.

      Ideation Phase Considerations:

      • Data Acquisition: Identify reliable sources of comprehensive and high-quality POI data for different urban regions.
      • Data Preprocessing: Develop strategies to clean, standardize, and integrate POI data from various sources.
      • Model Scalability: Test and refine the AI model to ensure it can handle large datasets and complex urban environments.
      • Interpretability of Results: Develop techniques to explain the rationale behind the AI model's functional area classifications.
      • Competitive Analysis: Research existing urban planning tools and identify UrbanFuncDelineator's unique value proposition.
      • Revenue Model Development: Define a clear plan for how the AI service will generate revenue and ensure its long-term sustainability.
      • User Adoption Strategy: Identify the target users (planners, policymakers) and develop a plan to educate them about the service's benefits.

Summary

Overall Community

4

from 9 reviews
  • 5
    0
  • 4
    9
  • 3
    0
  • 2
    0
  • 1
    0

Feasibility

3.7

from 9 reviews

Viability

3.4

from 9 reviews

Desirabilty

4.1

from 9 reviews

Usefulness

4.4

from 9 reviews

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