This blog posts delves into the mobility data and trip hop movement patterns for one
This blog posts delves into the anonymization and aggregation techniques for mobility data for govtech, smart city, and civic use cases.
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Geospatial mobility data collected from IoT sensors, mobile devices, beacons, connected cars, fleets, apps, chatbots, and government services is an essential input for civic intelligence to build smarter cities.
CITYDATA is a govtech company that curates fresh, accurate, anonymized, crowdsourced mobility data for 1568 cities and metro areas on a global scale.
Rule #1 in govtech is to protect consumer data and respect privacy. To that end, our team has invested enormous time and resources to research and study the privacy environment, regulations, and cultural expectations in different countries and regions around the world:
- European Union: General Data Protection Regulation (GDPR)
- State of California: California Consumer Privacy Act (CCPA)
- Singapore and SEA: Personal Data Protection Act (PDPA) and derivatives
- Brazil and LATAM: Lei Geral de Proteção de Dados (LGPD) and derivatives
Our analysis of regional law and privacy regulation has informed our data philosophy and technology architecture to make privacy a core tenet in everything we do. Our geospatial data platform incorporates privacy by design, from policy to practice by following the framework principles listed below.
[ 1 ] Reject personal data
We inform all data suppliers and data sources to never send us personal data or personally identifiable information such as names, email addresses, phone numbers, IMEI numbers, dates of birth, gender, ethnicity, income, transactions, purchase history. That said, when dealing with trillions of data points, if some personal data does make its way to our ingestion endpoint, our data validation algorithms are trained to identify and reject the personal data at source.
[ 2 ] Hash to anonymize
We ensure that all unique identifiers are hashed at the ingestion endpoint before storing or archiving the data in our cloud. We use the SHA-1 cryptographic hash function takes the identifier as the input and produces a 160-bit (20-byte) hash value rendered as a hexadecimal number, 40 digits long.
[ 3 ] Obfuscate to anonymize
Geospatial data often includes fields like latitude, longitude, and timestamp associated with a an IoT sensor or a mobile device. By spatially and temporally shifting such data using random perturbation within acceptable bounds, we produce an obfuscated dataset without impacting the ability to generate meaningful insights from such data.
[ 4 ] Aggregate to anonymize
It is common to allocate geospatial data to Geohash or H3 grids. We mask the data by aggregating into grid cells and assigning features like device counts, signal counts, density, day-parted counts, weekday counts, hourly ingress-egress to each grid cell. Such grid masking has two-fold benefits because the aggregated features inherently anonymize the underlying data while also making it more convenient to infer patterns across a wider grid.
Anonymizing and aggregating data while maintaining the integrity of the underlying patterns is essential for the safe use of data by municipalities and government entities for civic innovation. Reach out to us if you'd like to access the anonymized and aggregated daily mobility dataset which are useful for inferring people-density, mobility patterns, and trip hops, for 1568 cities and metro areas across 60 countries.