These numbers move markets, and they are revised after publication. Store only the latest value and your history quietly rewrites itself.
ScrapeIt runs the collection as a managed service. You name the ports and periods; we build the pipeline keeping loaded and empty apart, direction separate and every revision recorded, and hand back CSV, JSON, Excel or a push into your warehouse.
Statistics publish monthly, so collection follows that cycle, with enough frequency to catch revisions rather than only the latest state.
These are public statistics from a public agency, collected within crawl rules that place no restriction on the site. Where a client's question is answered by the published tables as they stand, we will say so rather than quoting for a pipeline.
Statistic records carry the month and year, direction - import or export - container status as loaded or empty, the volume in twenty foot equivalent units, and the total.
Loaded and empty are kept apart rather than summed. Empty container movements reflect repositioning rather than trade, and a total that merges them overstates commercial activity in a way that varies with the trade imbalance.
Revision tracking is the field that justifies collection. Published monthly figures are restated, and a dataset holding only the current value silently rewrites its own history. We keep every observed value with the date it was observed, so a revision is a record rather than an erasure.
Direction matters for the same reason: import and export volumes tell different stories and a combined figure tells neither, particularly at a port with a structural imbalance.
Every row carries the collection timestamp and the observation sequence for that period.
Revision analysis is the distinctive output and requires having collected from the start: how much first-published figures typically move, in which direction, and whether the error is systematic. For anyone building an indicator on these numbers that is essential context.
Cross-port series on one schema is the wider product, and it is where the analytical value concentrates. Volumes shifting between gateways are a routing story that no single port's statistics reveal.
Seasonality decomposition is straightforward once several years are assembled, and container volumes are strongly seasonal around manufacturing and retail cycles.
Limits: these are container counts, not values, commodities or shippers. Anyone needing trade composition needs customs data, which is a different source with different rules. We say so at scoping rather than letting container volumes stand in for trade statistics.
The Port of Los Angeles is the largest container port in the United States and publishes monthly container statistics openly: loaded and empty containers, imports and exports, in twenty foot equivalent units, going back years.
Those figures are watched well beyond the shipping industry. Analysts read them as an indicator of US import demand, and they move before most official trade statistics are released, which is why they get quoted in macroeconomic commentary.
Because the statistics are published as structured tables rather than buried in prose, the first question on any project here is whether collection is needed at all. Where the published tables answer a client's question we say so.
What collection adds is history with revisions, and a comparison across ports. Crawl rules are published, permit the site without restriction and reference a sitemap.
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It would be reasonable to ask why anyone should collect statistics that are already published as tables, and the answer is entirely about time.
A published table shows the current value for each period. What it does not show is what that value was last month, and monthly port statistics are revised. For anyone using these figures as an economic indicator, the difference between the first published number and the revised one is itself information - it is the nowcasting error, and it can only be measured if the original was recorded.
The second reason is cross-port comparison. Major container ports each publish their own statistics in their own formats and conventions, and assembling them onto one schema is the work. Once assembled, the relative movements say more than any single port does, because a shift between ports is a routing decision rather than a demand change.
The third is the loaded and empty split, which is genuinely informative at a port with a large structural imbalance. Empty repositioning volumes reflect the cost of that imbalance and move differently from trade.
The fourth is that these are public statistics from a public agency, which makes this among the least encumbered sources in this catalogue.
Learn how to use web scraping to solve data problems for your organization
Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.
Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.
If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.
ScrapeIt is a managed extraction company, not a tool you have to learn. Our team builds the pipeline, watches it as table formats change between publications, and repairs it before a revision goes unrecorded and becomes unrecoverable.
You see a sample first, in your format, over the periods you actually use, with revisions visible so you can see immediately how much the first published figures moved.
For the revisions. A published table shows the current value; it does not show what that value was last month. Monthly port figures get restated, and for anyone using them as an indicator the size of that revision is itself the information.
Because empty movements are repositioning rather than trade. A combined total overstates commercial activity by an amount that varies with the trade imbalance - which at this port is large and itself worth measuring.
Yes, and that is where the value concentrates. Each port publishes in its own format and conventions, so assembling them onto one schema is the work. Volume shifting between gateways is a routing story no single port reveals.
No - they are container counts, not values, commodities or shippers. Trade composition needs customs data, a different source with different rules. We say so rather than letting container volumes stand in for trade statistics.
Unusually few. These are public statistics from a public agency and the site's crawl rules place no restriction on it. It is among the least encumbered sources in our catalogue.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
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