This blog post compares various geocoding APIs, focusing on pricing, free tiers, and terms of service. It covers prominent providers like Google Maps Platform, Mapbox, OpenCage, LocationIQ, Positionstack, and Here, examining their cost structures which range from usage-based billing to subscription models. The post highlights free tier limitations, including request quotas, feature restrictions, and commercial usage allowances. It also analyzes terms of use, particularly concerning data ownership, caching policies, and attribution requirements. The comparison aims to help developers select the most suitable geocoding API based on their specific needs and budget.
H3 is Uber's open-source grid system for efficiently indexing and analyzing location data. It uses a hierarchical grid of hexagons, offering a more uniform and distortion-free representation of the Earth's surface compared to traditional latitude/longitude grids. This allows for consistent spatial analysis, as hexagons have equal area and more uniform edge lengths. H3 provides functions for indexing locations, finding neighbors, measuring distances, and performing other geospatial operations, facilitating applications like ride sharing, trip analysis, and urban planning. The system is designed for performance and scalability, enabling efficient processing of large geospatial datasets.
Hacker News users discussed the practical applications and limitations of H3, Uber's hexagonal hierarchical geospatial indexing system. Several commenters pointed out existing similar systems like S2 Geometry, questioning H3's advantages and expressing concern over vendor lock-in. The distortion inherent in projecting a sphere onto a hex grid was also raised, with discussion about the impact on analysis and potential inaccuracies. While some appreciated H3's ease of use and visualization features, others emphasized the importance of understanding the underlying math and potential pitfalls of any such system. Some users highlighted niche applications, like ride-sharing and logistics, where H3's features might be particularly beneficial, while others discussed its potential in areas like environmental monitoring and urban planning. The overall sentiment leaned towards cautious interest, acknowledging H3's potential while emphasizing the need for careful consideration of its limitations and comparison with existing alternatives.
Geocod.io, a geocoding service, is modifying its free tier to combat abuse and ensure its long-term sustainability. Due to a significant increase in usage, including malicious activity like automated queries and denial-of-service attacks, they are implementing stricter rate limits. The new free tier will be limited to 2,500 queries per day, and exceeding this limit will result in a 402 error requiring users to upgrade to a paid plan. They are also strengthening their bot detection measures and emphasizing their commitment to providing a reliable and accessible service for legitimate free tier users while protecting their resources from exploitation.
Hacker News users generally supported the author's efforts to combat abuse of their free tier geocoding service. Several commenters shared their own experiences with similar issues, highlighting the prevalence of abuse and the difficulty in balancing free access with sustainable operation. Some suggested alternative mitigation strategies, including stricter rate limiting, requiring API keys even for free users, and offering a low-cost paid tier with more generous limits. One commenter pointed out the potential legal ramifications of storing user IP addresses, urging the author to ensure compliance with GDPR and other privacy regulations. Another noted the apparent contradiction in blocking VPNs while using Cloudflare, a service often used to bypass such blocks. Overall, the discussion focused on the challenges faced by developers offering free services and the need for effective abuse prevention measures.
TheretoWhere.com lets you visualize ideal housing locations in a city based on your personalized criteria. By inputting preferences like price range, commute time, proximity to amenities (parks, groceries, etc.), and preferred neighborhood vibes, the site generates a heatmap highlighting areas that best match your needs. This allows users to quickly identify promising neighborhoods and explore potential living areas based on their individualized priorities, making the often daunting process of apartment hunting or relocation more efficient and targeted.
HN users generally found the "theretowhere" website concept interesting, but criticized its execution. Several commenters pointed out the limited and US-centric data, making it less useful for those outside major American cities. The reliance on Zillow data was also questioned, with some noting Zillow's known inaccuracies and biases. Others criticized the UI/UX, citing slow load times and a cumbersome interface. Despite the flaws, some saw potential in the idea, suggesting improvements like incorporating more data sources, expanding geographic coverage, and allowing users to adjust weighting for different preferences. A few commenters questioned the overall utility of the heatmap approach, arguing that it oversimplifies a complex decision-making process.
Summary of Comments ( 63 )
https://news.ycombinator.com/item?id=43770446
Hacker News users discussed the practicality of self-hosting geocoding, with some pointing out the hidden costs and complexities involved in maintaining a reliable and performant service, especially with data updates. Several commenters highlighted the value proposition of paid services like Positionstack and LocationIQ for their ease of use and comprehensive features. The adequacy of free tiers for hobby projects was also mentioned, with Nominatim being a popular choice despite its usage limitations. Some users shared their experiences with specific APIs, citing performance differences and quirks in their data. The difficulty in finding a truly free and unrestricted geocoding API was a recurring theme.
The Hacker News post discussing the Geocoding API comparison article has a modest number of comments, focusing primarily on the practicality of self-hosting a geocoding solution and highlighting alternatives not mentioned in the original comparison.
One commenter suggests Nominatim as a viable self-hosted option, pointing out that while it requires substantial resources (specifically mentioning 64GB of RAM), it offers complete control over data and avoids external dependencies. They further clarify that the high RAM requirement is mainly due to needing to hold the entire database in memory for optimal performance, but for less demanding use cases, smaller datasets could suffice, reducing the hardware requirements. This comment sparked a brief discussion about the feasibility of self-hosting for different levels of usage. Another user responded, corroborating the resource intensity of Nominatim, but highlighting the benefit of avoiding recurring costs associated with commercial solutions. They acknowledge the setup can be complex but ultimately rewarding for those with the technical expertise.
Another thread discusses the absence of Pelias from the original comparison. A user points out that Pelias, being an open-source geocoder built on Elasticsearch, is a strong contender, offering flexibility and customization. However, they also acknowledge that the setup and maintenance can be more involved than some other solutions. This comment prompted a response mentioning the operational overhead and complexity of Pelias, agreeing that it’s a powerful tool but requires dedicated effort to manage.
Further down, a commenter mentions LocationIQ as a provider they have had a positive experience with, particularly praising their generous free tier. This comment stands alone without further discussion.
Finally, a short exchange discusses the importance of data freshness for geocoding applications, with one user emphasizing how quickly location data can become outdated, and another suggesting regular updates and potentially supplementing with real-time data sources depending on the specific application’s requirements.
In summary, the comments offer valuable insights into the nuances of choosing a geocoding solution, emphasizing considerations beyond just pricing and free tiers, such as the trade-offs between self-hosting and using a third-party service, the complexities of maintaining open-source solutions, and the crucial role of data freshness.