Data Practices

Privacy

Place Intelligence has a sophisticated spatial intelligence technology stack that is used to create insights into how people use places over time, the user mix and their mobility patterns.

This process incorporates:

• Collecting big data from various signal sources, including mobile GSM, GPS, IoT, and WiFi data.
• Developing GIS datasets to label signal data and enrich the results.
• Integrating GIS data into our query-building software, which generates compute jobs associated with user accounts.
• Utilizing our extensive library of geoprocessing algorithms to convert data into meaningful metrics.
• Employing supercomputing systems to handle and process vast datasets as needed.
• Presenting results in a team setting with permission and access controls, allowing organizations to determine data model visibility.
• Streaming data models through our user-friendly spatial intelligence software, designed for creating impactful maps, filtering datasets, and exporting reports.

Understanding input data sources – Signals Data

Signals data refers to the digital information derived from devices that can be collected and used to reveal patterns, behaviours, or activities. In the context of urban analytics signals data refers to the digital footprints emitted by hand-held devices and vehicle systems that are generated by a device when it communicates with other devices. These signals can be processed to determine the time, location, and connection method of an signal event. Common sources of signals data sets include:

Mobile Phones: Emit signals that can be used to determine their location based on cell tower triangulation (GSM), GPS, or WiFi networks.

WiFi Access Points: Devices connected to a WiFi network can be located based on the strength and origin of their connection.

Beacons and IoT Devices: These often use technologies like Bluetooth Low Energy (BLE) to emit signals that can be detected by nearby devices, indicating proximity or exact location within a confined space.

Vehicle-based GPS Systems: These are specialized GPS systems installed in vehicles, often integrated with the vehicle’s onboard systems. They not only provide location data but can also offer route navigation, traffic updates, and other vehicle-specific functionalities. The signals data from these systems can be used for fleet management, real-time tracking, and analyzing driving patterns.

LPWAN Technologies: Devices connected through Low Power Wide Area Networks (like LoRa or Sigfox) send signals that can indicate their general location or other data.

Sensors: Various sensors, from temperature to motion detectors, emit signals indicating changes in their environment or the presence of an entity.

How We Work

At Place Intelligence, we specialize in providing location intelligence services for the built environment professions. Our work is grounded in the latest, privacy-compliant data sources available worldwide, which we use to develop critical analysis tools and assessment methods. This ensures that our findings are evidence-based and rigorously validated against our global index of cities and place typologies.

To deliver sustainable, financially viable, and long-lived outcomes, Place Intelligence understands the importance of using the right big data sources that can provide insights at any scale and location worldwide. To achieve this, we have invested years in developing industry-leading big data warehouses, analysis tools, machine learning frameworks, and artificial intelligence platforms that underpin our critical analysis work.

Our Commitment to Privacy

At Place Intelligence, we are committed to upholding the highest standards of data privacy and GDPR compliance. We have extensive experience in delivering digital strategies for data and analytics, and our team has a proven track record in developing smart/digital city frameworks for universities, cities, local governments, and private practice organizations with a strong emphasis on privacy-first principles.

Over the past decade, we have pioneered smart cities, place measurement, and big data analytics in the built environment, providing solutions to organizations worldwide. Our work includes high-profile projects such as the City of Melbourne Smart City Strategy, the Waverley Council Place Performance Framework, and the ACT Government’s City Renewal Authority and Transport Canberra Digital City Frameworks and Place Audit Toolkits.

At Place Intelligence, we go beyond strategy by specializing in place performance measurement and indexing. We use data insights to drive best practice urban regeneration, development, and create liveable and sustainable communities while maintaining strict GDPR compliance and data privacy standards. We have invested countless hours into creating leading data sets, systems, and technologies that can be deployed instantly. Our proprietary data analytics tools are designed around the core principle of understanding how people use places and who these users are. 

Our automated data capture and measurement protocols are used to inform place optimization and measurement studies at diverse scales worldwide, providing valuable insights to a range of client groups, including state and local governments, development authorities, design and planning agencies, and place-making and management groups while safeguarding their data privacy.

Input Data Compliance and Accuracy

We ensure that all data that we obtain comes from reputable companies with compliance policies that enable them to on-sell their product or services relative to our geographical area of interest. This includes:

General Data Protection Regulation (GDPR) and compliance with the Australian Privacy Principles. 

Have global consent management systems in place, listing Place Intelligence as a downstream consumer of the data.

Place Intelligence compiles, stores, and validates data to ensure that the data we rely on:

• contains no personal identifiable information
• is of significant size to be statistically relevant (e.g. enough data over a long enough period of time);
• is of an acceptable resolution of horizontal accuracy to ensure locational insights match exact geographic coordinates;  
• is stored in secure and tamper proof infrastructure.

Data procured by Place Intelligence is audited by our data assurance team, to ensure that the data sets we use do not contain any false or duplicate signals, and have not been up-sampled or interpolated by service providers to increase data sample sizes.

How we process the data

We use an array of data processing and mapping tools to generate insights. 

Place Intelligence processes signals data to generate location insights. In most cases we follow a typical query methodology:

Extract: We pull raw data from our data base on the geographic area of interest (geo-fence).

Cache: The geo-fence returns all signals obtained within the data model, which are loaded into a project specific data base.

Cleanse and Anomonize: By default we create psuedonomized identifiers that can never be linked to an individual before undertaking any work. This data is cleansed of anomalies and validated before wee undertake any work.

Query: We perform an array of queries on the data including – Descriptive Analysis, Diagnostic Analysis, and Predictive Analysis.

Export: We export processed data into various tabular formats (e.g. CSV), and geo-spatial formats (e.g. ESRI shapefile, GeoJson) that contain no PII or Sudo PII. 

Removing Biases 

Horizontal Accuracy Issues

Signal intelligence is powerful in that it allows for both large scale urban (city) analytics as well as small scale (human) level assessments. 

Here we apply different modeling methods for different use cases – for example state scale modeling that use GSM triangulation protocols from signals infrastructure provide insights on urban mobility at levels of horizontal accuracy between 150 and 300 meters, LPWAN provide connections to a broader number of devices with horizontal accuracy at +-2km. Analyses that leverage GPS data can provide levels of horizontal accuracy to +-5 meters.  

Sample Size

When working with signals data we are aware that many panels do not represent 100% of all people, that are sparse data models have limits in their applications. We ensure that our data panel  is aggregated from the broadest range of sources possible and that data is sourced from a wide cross-section of the population to remove age and demographic clustering, age, gender, and demographic data. This ensures our base data is fully privacy prioritized.

How do you ensure privacy compliance in the data you use?

Place Intelligence is committed to protecting the privacy of individuals. We follow a few simple protocols with every project to ensure that: 

We remove all potential personal identifiable information (PII) through our data management process. 

We never share data to our clients that contains PII or potential PII, even if the data was given to us by the client themselves. 

Data is presented in aggregate statistics and insights can never be used to identify individuals or at-risk groups.  

When obtaining vehicle gps and mobile device signal data, we only work with data service providers that have implemented global consent management platforms for the collection and use of location based data from individuals – meaning every signal we use has been shared voluntarily by users and Place Intelligence is listed as a consumer of this information.

In some countries services providers are allowed to provide an Advertising ID (AID) or a persistent identifier such as a MAC address in their data- how do you handle this information? 

We aren’t an advertising agency, so we don’t use AID’s in our analysis. Moreover, we don’t undertake any data enrichment that links any potential PII data string to an actual person. At Place Intelligence, we assign, by default, prior to undertaking any spatial analysis a unique identifier to each signal event so that the data we query is free from all PII or potential PII. 

GDPR Compliant Input Data

We only work with data that has undergone data management from GDPR compliant data services providers.

This ensures every data point we use contains no Personal Identifiable Information.

AWS Deep Security Infrastructure

Ingested data is stored in a project specific AWS database that can only be accessed by our data science and data engineering team.

This data is never used for any other project.

Place Intelligence Data Management

Project data is then encrypted and all pseudonymized id’s are removed and replaced with a machine generated Place Intelligence ID.

This means that no data can ever be re-identified and every signal is anonymous. 

Place Intelligence Geospatial Framework

Analysis process uses an H3 GIS Mesh and client provided GIS Polygons for analysis.

This means that our analysis is limited to the geo boundaries that we are studying, and does not extend beyond our site extents.

Place Intelligence Geospatial Analysis

Data analysis is then undertaken using our validated spatial data and aggregated input signals data. 

Results are tested against control information for accuracy and calibrated as required. 

Place Intelligence Data Products

The results of our work are city models that showcase historical patterns of place use and movement, saved in geospatial formats.

These files contain only spatial and statistical data.

Place Intelligence Data Studio

Place Intelligence data products are then loaded into our secure and tamper proof Web based GIS platform. 

This means that only users who have been granted access to the Data Studio can see and interrogate the data.

 To learn more about our data practices get in touch today.