Occupancy

Occupancy is a primary data set available from Occuspace that reports the level of occupancy of your spaces (i.e. how many people are in each location over a given time frame). It is highly valuable in understanding how your space is currently used, and to aid in making future planning and allocation decisions. This metric provides a rich level of fidelity in the data at the neighborhood level that is flexible and can align with the way that you view and manage your real estate.

For understanding your space behavior and making optimization and planning decisions, there is no better data set to base your decisions than Occupancy. Occuspace's platform makes it simple to continuously monitor as much of your real estate as desired, and report on this valuable and actionable metric.

Your Occupancy data is available 24x7x365 via the Occuspace Portal in the "Analytics" section which provides you with access to all of your data collected since installation. You can also access this data programmatically from the Occuspace Customer API.

Occupancy Data Points

Occupancy is the number of people in a space over a given period of time (e.g. a day, an hour, a date range). Occuspace reports each Occupancy data point in four specific ways:

• Average Occupancy - The average number of people that were in the space over the time period

• Average Percent Occupied - The average percentage occupied the space was over the time period, based on the defined capacity of the space

• Peak Occupancy - The peak number of people that were in the space over the time period

• Peak Percent Occupied - The percentage occupied the space was at the peak level of occupancy during the time period, based on the defined capacity of the space

The Analytics module of the Customer Portal as well as the Occuspace API allow you to retrieve Occupancy data points for any of your spaces. The data points that are available in these interfaces will have each of the four attributes described above.

Occupancy reports how many people on average are in a given space over a defined time period

It is not unusual for there to be significant differences in the average and peak Occupancy values for a space during a time frame, particularly for longer time frames of analysis. This is usually the case for spaces that are dynamic environments and have highly variable rates of use throughout the day. Spaces that are more static and constant in nature will generally have average and peak Occupancy that are much closer to each other.

Data Reporting Intervals

Occupancy data provided by the Occuspace platform is available in different time period intervals to satisfy varying levels of granularity desired in data analysis. The intervals available include full day, 60 minute, 30 minute, and 15 minute periods of time.

The Occuspace Portal visualizes Occupancy data in daily and hourly intervals, which are generally appropriate for most data analysis needs. These intervals are also the most stable from a data perspective, and where the machine learning algorithms provide the highest level of accuracy.

Daily Occupancy

For the date and time range selected the average occupancy for each day is displayed in the chart and table.

Occupancy is frequently analyzed at daily time intervals

Hourly Occupancy

For the date and time range defined the average occupancy for each hour of the day is displayed in the chart and table.

Analytics provides an hourly breakout of Occupancy as well for more dynamic trend analysis

Weekday & Hourly Occupancy

For the date and time range defined the average occupancy for each hour of the day and for each day of the week is displayed in the chart and table. This visualization in particular helps understand weekly trends.

Two dimensional hourly and weekday analysis of Occupancy is also provided

Finer Data Granularity Intervals

More granular data reporting intervals of 30 minutes and 15 minutes can be suitable for decisions that need to be based on shorter time segments of the day. The tradeoff is that these smaller intervals can result in noisier Occupancy data that fluctuates substantially from one interval to the next, especially for very dynamic environments. Occuspace recommends that these intervals are only used when they can influence decisions in your business that are also similarly highly dynamic in nature (these tend to be more real-time signal based use cases). Otherwise the data can be much harder to interpret and act upon.

These finer granularity data intervals are available in the Data Export functionality in the Analytics module of the Customer Portal, which will provide you with a downloadable CSV file for further analysis.

Neighborhood Level Data Breakout

Occupancy data is continuously collected and reported for each of the spaces in the neighborhood breakout that was defined during your on-boarding and installation. This neighborhood breakout maps to a hierarchy for how you manage your own business and real estate (e.g. by department or business group). The Occuspace Portal allows you to view your Occupancy data for any of these neighborhoods.

Occupancy is frequently reported at a neighborhood level data reporting granularity

You can also compare the Occupancy data of one space against up to five other spaces (or compare the Occupancy data of one space against previous time ranges in the past for the same space) using the Compare To functionality in the Analytics module. This feature is particularly powerful for space understanding, planning, and allocation purposes.

Comparison functionality allows for visualizing spaces against other spaces being measured, or against other time periods

Occupancy data is also aggregated from individual spaces to the associated parent level spaces as well (e.g. the various floors of a library can be viewed individually, but you can also look at the Occupancy data and patterns of the library as a whole).

Occupancy can provide a comparison of parent and children space relationships

By dissecting Occupancy trends and patterns within defined neighborhoods, businesses can unlock a deeper understanding of how each sector of their space is being utilized over time. This granular approach enables more strategic and evidence-based decisions concerning space allocation, optimization, and future planning. It facilitates a more nuanced approach to managing space, allowing you to identify underutilized areas, predict peak usage times, and adjust resources accordingly.