# Level of availability and country level aggregation

The KPIs are available at the level of location or pair of locations (typically a UN/LOCODE). For the official report, the data was further aggregated at country level by taking average, medians and other statistics of the natural unit indicators generated for all locations in the country (or pair of countries for corridor data). Portspecific data is available for just under 500 ports and can be made available for academic purposes upon request. Consignment-specific data cannot be made publicly available due to confidentiality concerns and data sharing agreements with partnering organizations.

For landlocked countries, the observations serving to estimate the dwell time at port of entry are separate from the observations related to shipments going to the country of transit. Some countries classified as landlocked developing countries in our report are not considered to be such in a global, less strict definition. For example, Azerbaijan, a country that formally has access to the Caspian sea and operates its largest port (Baku), was classified as a landlocked country. This data feature stems from the nature of our datasets: all of the maritime-related data solely focused on container shipping. Our indicators are thus relevant to ports and terminals that are able to handle containerships and ignore dry bulk, liquid bulk, LNG/oil, and Ro/Ro terminals. In the future, we plan to include additional KPIs that include the performance of port facilities that handle dry bulk operations.

# Data cleaning and consistency

Tracking datasets are not perfect. Events may be missing or duplicated in the sequence of individual consignments. Sequences may be truncated. Table 3 includes the sequence for a consignment going from Odessa in Ukraine to India in transit to Nepal. In between, the container is transshipped in Istanbul, Salalah, and Colombo. However, the transshipment in Istanbul includes duplications (highlighted in grey). To account for this type of issue, cleaning procedures are implemented prior to slicing the data to produce indicators. Anomalous events are eliminated based on rules that “normal” events should logically follow (Table 4). This ensures that the slicing isolates consecutive event pairs.

_Table 3: Sample container voyage_

|event|location|date|FULL_ST<br>ATUS|
|---|---|---|---|
|Actualgate out|UAYUZ|2021-10-29-12.59.00.000000|Empty|
|Actualgate in|UAYUZ|2021-10-30-14.14.00.000000|Full|
|Actual loaded on vessel|UAYUZ|2021-10-31-21.59.00.000000|Full|
|Actual vessel departure|UAYUZ|2021-11-01-03.49.00.000000|Full|
|Actual vessel arrival|TRIST|2021-11-07-16.30.00.000000|Full|
|Actual discharge from vessel|TRIST|2021-11-07-23.42.00.000000|Full|
|Actual discharge from vessel|TRAMR|2021-11-07-23.42.07.000000|Full|
|Actual loaded on vessel|TRIST|2021-11-17-03.51.00.000000|Full|
|Actual loaded on vessel|TRAMR|2021-11-17-03.51.46.000000|Full|
|Actual vessel departure|TRIST|2021-11-17-08.15.00.000000|Full|
|Actual vessel arrival|OMSLL|2021-11-27-04.18.00.000000|Full|
|Actual discharge from vessel|OMSLL|2021-11-27-14.15.00.000000|Full|
|Actual loaded on vessel|OMSLL|2021-12-01-21.25.17.000000|Full|
|Actual vessel departure|OMSLL|2021-12-02-00.16.00.000000|Full|
|Actual vessel arrival|LKCMB|2021-12-05-07.38.00.000000|Full|

16