As information becomes increasingly abundant and collaboratively generated, platforms such as Wikipedia have enabled massive data sharing. However, none of these platforms provide intuitive ways to visualize or explore information dynamically, based on historical events in relation to specific locations and time frames.
We aim to create a platform where data is indexed geospatially and temporally, allowing users to access events and histories based on geographical location and their historical significance. Additionally, our platform fosters collaboration through economic incentives, encouraging meaningful contributions in a structured manner. The end goal is to enhance the cultural and historical relevance of locations, thus revolutionizing how tourists and users engage with historical and cultural content.
The industry currently lacks an integrated system that dynamically links geospatial data with historical timelines. While digital maps and geolocation services such as Google Maps and OpenStreetMap are widely used to locate places, they do not offer insights into historical events that occurred at these places. On the other hand, platforms like Wikipedia provide extensive information about historical events but are not linked to specific geographical or temporal frameworks.
There are isolated solutions that focus on geographic data management or collaborative information systems, but they remain distinct from each other. For example, Wikipedia is an excellent example of a content management system with mass collaboration capabilities, while GIS platforms like GeoNames or Mapbox allow for rich geographical data indexing. However, no systems integrate these capabilities with a temporal component, allowing users to explore history visually over time.
Furthermore, platforms like blockchain-based collaborative systems (e.g., Bitcoin, Ethereum) introduce economic models that incentivize participation, but these models are not commonly applied in the realm of cultural data collaboration. By combining the principles of collaboration from Wikipedia and blockchain, and the structured geospatial insights of GIS systems, we are working to build a multi-dimensional platform for cultural exploration.
Technologies
To achieve our goals, we utilize a range of technologies and methodologies:
- Geospatial Information Systems (GIS): We leverage systems such as GeoNames for geolocation data and geographic boundaries. GeoNames provides an open-source database of geographical features that we incorporate to track events and places over time. For dynamic map functionalities, tools like Mapbox and OpenLayers offer powerful mapping and visualization.
- Collaborative Data Models: Wikipedia's collaborative editing framework informs our platform's collaborative component. We also take inspiration from GitHub's versioning system, employing a Diff-Match-Patch algorithm to manage the history of updates to the data.
- Blockchain-based Economic Incentives: Drawing from cryptocurrency models such as Bitcoin and Ethereum, we explore blockchain's capability to introduce economic incentives for user contributions. These technologies allow us to design token-based systems where users are rewarded for meaningful contributions to the platform.
- Database Structures: For handling complex data relationships, we use a combination of graph databases (e.g., Neo4j) for geospatial and temporal queries, and relational databases for structured metadata storage. Graph databases are particularly suited to manage the intricate relationships between historical events, places, and the flow of time, allowing for highly efficient querying of this interconnected data.
- Machine Learning for Data Categorization: We incorporate machine learning algorithms to help classify and summarize historical data. This is particularly important for handling the vast unstructured data retrieved from Wikipedia and other public sources.
- Web Crawlers and Data Aggregators: The data foundation of this platform relies heavily on automated web crawlers that systematically extract relevant historical events from publicly available databases like Wikipedia. This data is then cleaned and categorized for inclusion in our geospatial-temporal platform.
Study Details
The primary goal of our study was to develop a platform capable of integrating geospatial data with a temporal component while also incorporating a collaborative model supported by an economic incentive system. The platform's purpose is to enhance user engagement with cultural and historical data by allowing them to explore events that have occurred in specific locations over time. Additionally, we sought to provide an incentive mechanism that encourages active collaboration and contribution, akin to systems used in blockchain technology.
The platform had three core objectives:
- Spatial and Temporal Data Aggregation: Create a system that allows users to search for information based on both geographical location and time.
- Collaborative Framework with Economic Incentives: Design a collaboration model where users are incentivized through an economic reward system, encouraging data contributions and validation.
- Promotion of Culture and Tourism: Provide a tool that supports cultural education and tourism by highlighting the historical relevance of places around the world.
To address the ambitious goals of this project, we adopted a multi-phase approach, beginning with rigorous research and development of a solid technological foundation.
We started by identifying reliable sources for geospatial data, such as GeoNames, which provides extensive and regularly updated geographic information. The data collected from GeoNames included location coordinates, administrative boundaries, and hierarchical geographical structures from countries down to small cities and towns. This was essential for creating the spatial layer of our platform.To manage this data efficiently, we used a graph database (Neo4j), which is well-suited for complex, interconnected data structures such as historical events linked to geographic locations. This allowed us to represent regions, countries, and specific events in relation to their coordinates over time.
The time dimension was added by associating historical events with specific geographical entities in the graph database. Using open data sources such as Wikipedia, we developed custom web crawlers to extract events, dates, and locations. The temporal data was then integrated into our geospatial structure, enabling users to query not just locations but also the historical events tied to them at different points in time.
We based our collaborative model on Wikipedia's community-driven content management, but with additional economic incentives to encourage sustained contributions. Leveraging our knowledge of blockchain technology, we devised an economic incentive system where users are rewarded with tokens for their contributions, similar to how systems like Ethereum distribute tokens for validating transactions. These tokens can later be exchanged or used within the platform for advanced features or services, thus promoting user participation. Diff-Match-Patch algorithms, often used in collaborative systems like GitHub, were integrated to manage version control, ensuring that changes to event data and locations were tracked, validated, and properly attributed to contributing users.
One of the key challenges we faced was the massive scale of unstructured data available online. Our solution involved machine learning algorithms for data categorization, allowing us to process unstructured historical data and assign it appropriate tags for easier navigation within the platform. This was essential for creating a seamless user experience where users could explore historical events without getting overwhelmed by raw, unprocessed information.
The web crawler system was also enhanced to handle larger and more diverse data sources beyond Wikipedia, such as publicly available archives and historical databases. This ensured that the platform could continually update its data pool with fresh, relevant information.
Our work resulted in the following findings:
The integration of geospatial and temporal data was successfully achieved using a combination of Neo4j graph databases for spatial relationships and a relational database for metadata. Users could query not only where events happened but also filter results based on when they occurred. This provided a novel way to explore historical data.
By offering tokens in exchange for contributions, we created a mechanism where users are incentivized to continuously add and verify historical data. This also introduces a self-sustaining economic model for the platform, where tokenized contributions promote a growing database of events. Gamification elements were added to further incentivize user participation, such as badges, rankings, and leaderboards, but the innovation was the introduction of economic value through tokens, which differentiates this system from traditional collaborative platforms like Wikipedia.
One of the biggest technical challenges we faced was ensuring the consistency between different data layers (graph and relational databases). As the volume of data grew, it became clear that traditional caching methods and database optimization strategies were necessary to maintain performance, especially for geospatial queries. Future development will need to focus on scaling the platform to handle larger datasets and more frequent user interactions. Another challenge was the representation of historical territorial boundaries. Over time, country and regional borders change, and capturing this evolving geography was difficult. We had to develop a system where territories could be visualized according to different historical periods, and this remains an area for further refinement.
The platform we developed opens up numerous opportunities from both cultural and economic perspectives. By making historical and cultural data more accessible and interactive, it can become useful for tourism boards, educational institutions, and businesses looking to engage users in a new, dynamic way.
