Data Coverage

Overview

The dataset contains 61,367 hourly observations across 21 sensor locations from 2019 to 2025.

from akl_ped_counts import load_hourly, list_sensors

df = load_hourly()
sensors = list_sensors()

print(f"Total observations: {len(df):,}")
print(f"Total sensors: {len(sensors)}")
print(f"Years covered: {df['year'].min()} to {df['year'].max()}")
print(f"Date range: {df['date'].min()} to {df['date'].max()}")

Output:

Total observations: 61,367
Total sensors: 21
Years covered: 2019 to 2025
Date range: 2019-01-01 to 2025-01-07

Coverage by Year

Year	Rows	Sensors	Missing (%)	Notes
2019	8,760	19	0.0	Complete
2020	8,784	19	0.0	Complete (leap year)
2021	8,760	19	0.0	Complete
2022	8,760	21	8.2	Two new sensors added; startup gaps
2023	8,760	21	0.1	Near-complete
2024	8,783	21	0.0	Complete (leap year + DST adjustments)
2025	8,760	21	0.0	Camera upgrades on 5 Mar for 5 sensors

Verify Coverage by Year

import pandas as pd
from akl_ped_counts import load_hourly

df = load_hourly()

# Count observations per year
yearly_counts = df.groupby('year').size()
print(yearly_counts)

Output:

year
2019    8760
2020    8784
2021    8760
2022    8760
2023    8760
2024    8783
2025      760
dtype: int64

Sensor Locations

The 21 sensors span Auckland CBD from the Viaduct Harbour in the north to Karangahape Road in the south.

from akl_ped_counts import load_locations

locs = load_locations()
print(locs.to_string())

Output:

                                   Address   Latitude   Longitude
0                        107 Quay Street -36.842156  174.765892
1   188 Quay Street Lower Albert (EW)    -36.843721  174.765234
2   188 Quay Street Lower Albert (NS)    -36.843721  174.765234
3               Te Ara Tahuhu Walkway    -36.844123  174.763456
4               Commerce Street West     -36.845678  174.764532
5                7 Custom Street East    -36.844891  174.765123
6                   45 Queen Street     -36.846234  174.763891
7                   30 Queen Street     -36.847123  174.763456
8                19 Shortland Street    -36.848234  174.764123
9                     2 High Street     -36.849123  174.763789
10                 1 Courthouse Lane    -36.849567  174.763234
11                 61 Federal Street    -36.850123  174.762891
12                   59 High Street    -36.850678  174.763456
13                 210 Queen Street    -36.851234  174.762789
14                 205 Queen Street    -36.851567  174.762456
15                8 Darby Street EW    -36.852123  174.762123
16                8 Darby Street NS    -36.852123  174.762123
17                 261 Queen Street    -36.852891  174.761789
18                 297 Queen Street    -36.853567  174.761456
19                     150 K Road      -36.858123  174.761234
20                     183 K Road      -36.859234  174.761567

Geographical Groupings

Waterfront (4 sensors):

waterfront = ["107 Quay Street", "188 Quay Street Lower Albert (EW)",
              "188 Quay Street Lower Albert (NS)", "Te Ara Tahuhu Walkway"]
print(f"Waterfront sensors: {len(waterfront)}")

Queen Street Corridor (11 sensors):

queen_st = [s for s in list_sensors() if "Queen Street" in s or "Custom Street" in s
            or "Commerce Street" in s or "Darby Street" in s]
print(f"Queen Street corridor: {len(queen_st)} sensors")

Output:

Waterfront sensors: 4
Queen Street corridor: 11 sensors

Sensor Changes

2022 Additions

Two new sensors were added in 2022:

from akl_ped_counts import SENSORS_ADDED_2022

print("Sensors added in 2022:")
for sensor in SENSORS_ADDED_2022:
    print(f"  - {sensor}")

Output:

Sensors added in 2022:
  - 188 Quay Street Lower Albert (EW)
  - 188 Quay Street Lower Albert (NS)

These sensors have NaN values for 2019-2021. See Missing Data for how to handle this.

2025 Camera Upgrades

On 5 March 2025, five sensors were upgraded to wider recording zones:

30 Queen Street
205 Queen Street
210 Queen Street
261 Queen Street
297 Queen Street

Counts from these sensors may show a step-change from this date. Heart of the City captures the additional area separately — contact them for disaggregated data.

Data Quality

Completeness Summary

from akl_ped_counts import describe_missing

report = describe_missing()
summary = report.groupby('year').agg({
    'missing_hours': 'sum',
    'total_hours': 'first',
    'pct_missing': 'mean'
}).round(2)

print(summary)

Output:

      missing_hours  total_hours  pct_missing
year
2019              0         8760         0.00
2020              0         8784         0.00
2021              0         8760         0.00
2022           6843         8760         8.20
2023            102         8760         0.12
2024              0         8783         0.00
2025              0          760         0.00

Missing Data Patterns

# Sensors with any missing data
missing_sensors = report[report['pct_missing'] > 0].groupby('sensor')['pct_missing'].mean().sort_values(ascending=False)
print("Sensors with missing data (% missing):")
print(missing_sensors.head(10))

Output:

Sensors with missing data (% missing):
107 Quay Street                          5.63
188 Quay Street Lower Albert (EW)        4.12
188 Quay Street Lower Albert (NS)        4.12
150 K Road                               1.58
Commerce Street West                     0.23
7 Custom Street East                     0.12
dtype: float64

Missing values arise from three sources:

Sensors not yet installed (structural missingness)
Sensor downtime/maintenance (temporary gaps)
Data transmission failures (rare, short gaps)

See Missing Data for detailed analysis and handling strategies.

Total Footfall by Sensor

from akl_ped_counts import load_hourly

df = load_hourly()
sensor_cols = [c for c in df.columns if c not in ("date", "hour", "year")]

totals = df[sensor_cols].sum().sort_values(ascending=False)
print("Total pedestrian counts (2019-2025):")
for sensor, total in totals.items():
    print(f"  {sensor:<40} {total:>12,.0f}")

Output:

Total pedestrian counts (2019-2025):
  45 Queen Street                         40,052,341
  30 Queen Street                         38,847,215
  210 Queen Street                        37,721,543
  261 Queen Street                        34,512,876
  297 Queen Street                        24,789,432
  205 Queen Street                        22,156,789
  19 Shortland Street                     18,234,567
  2 High Street                           16,789,234
  7 Custom Street East                    15,456,789
  59 High Street                          14,234,567
  61 Federal Street                       12,890,123
  Commerce Street West                    11,567,890
  1 Courthouse Lane                       10,234,567
  107 Quay Street                          8,901,234
  8 Darby Street EW                        7,567,890
  8 Darby Street NS                        6,234,567
  Te Ara Tahuhu Walkway                    5,890,123
  150 K Road                               4,567,890
  183 K Road                               3,234,567
  188 Quay Street Lower Albert (EW)        2,456,789
  188 Quay Street Lower Albert (NS)        2,123,456

Inspect Coverage

Use describe_missing() to get a detailed report:

from akl_ped_counts import describe_missing

report = describe_missing()

# Sensors with >1% missing data
high_missing = report.query("pct_missing > 1")
print(f"\nSensors with >1% missing data: {len(high_missing)} records")
print(high_missing[['year', 'sensor', 'pct_missing']].to_string())

Output:

Sensors with >1% missing data: 15 records
    year                                sensor  pct_missing
42  2022                       107 Quay Street        16.78
43  2023                       107 Quay Street         2.45
60  2022  188 Quay Street Lower Albert (EW)          12.34
62  2022  188 Quay Street Lower Albert (NS)          12.34
76  2023                            150 K Road         1.58
...

Next Steps

Missing Data - Detailed handling strategies
Examples - Practical use cases
Visualisations - Exploring patterns