Your candidate-site table may show a foot-traffic figure collected near each storefront. As a retail planner, you want to know how that activity compares with the other locations under review. The number may cover people passing a point, entries through a door, or visits defined by a data provider. Those details matter when the comparison will help decide which property deserves another visit or a closer lease review.
| Supplied measure | What it counts | Check before comparing |
|---|---|---|
| Pedestrian passing count | Movement past a defined observation point | Count location, direction, hours, and conditions |
| Store entries | People entering through the monitored entrance | Entrance coverage, observation period, and repeat entries |
| Estimated visits | Visits defined by the external data source | Visit definition, coverage, method, and reporting period |
Make traffic figures comparable
Define what was counted
Choose the traffic measure before building the comparison. For a storefront shortlist, that might be pedestrians passing a defined point near each entrance. For operating stores, it might be entry counts collected during comparable trading hours. Keep those two measures in separate comparisons.
Use counts you collected or data you got from an authorized external source. Those supplied values are the foot-traffic evidence you'll map in Maptive. The marker tools let you display and compare them; the traffic observations need to come with their own source and method.
Write a plain definition in the site table. Record whether the count includes both directions, whether one person can contribute several passages, and where the observation point is. A count at a busy crossing may include pedestrians who never pass the candidate's door. A sensor monitoring one entrance may leave out another entrance entirely.
Keep resident population and vehicle traffic in separate fields if they are useful to the wider site review. Neither is a pedestrian count. Customer pins also describe the customers in your file, rather than the people walking past an available storefront.
Match periods and observation duration
Compare the hours when the proposed store would trade. A weekday lunch count and a weekend afternoon count may reveal useful patterns, but their difference can't be attributed to the sites alone. Day of week, season, weather, and nearby events can all change short observations.
Keep the start and end dates, daypart, and observed hours. If the raw totals cover unequal durations, calculate an observed hourly rate in your spreadsheet before import. Preserve the totals and hours alongside it.
For an arithmetic illustration, 360 passages over 3 observed hours equals 120 passages per observed hour. A count of 240 over 2 hours gives the same rate. Those sums let you compare counts taken over different lengths of time. They don't establish that the sites have equal all-day traffic, since the hours and conditions may differ.
Don't turn a short count into an annual estimate by simply multiplying it across the year. Annual estimates need suitable longer-term patterns and adjustment factors. If your external source supplies an annual estimate, retain its definition and method rather than comparing it directly with a raw afternoon count.
Leave an unmeasured value blank in the source workfile and flag it for follow-up. Zero should mean a measured zero, not a missing report.
Import the retail observation table
Prepare a table with headers for the site ID, address or coordinates, measure definition, numeric count or rate, observation dates, daypart, observed hours, source, and notes. Include weather and event notes when they could explain an unusual result. Keep a quality or review-status field if some observations need confirmation.
For the first comparison, use one comparable value per site. You can retain repeated observations in a separate detailed table, but make the unit of each imported row clear. Several observations at the same site shouldn't look like several different candidate properties.
In Maptive, choose Create New Map, name the map, and click Continue. Under Your Data, add the Excel, Csv, or Tsv file, or paste the table with its headers. Click Map Now. If Maptive asks you to match columns, check the location fields and click Done.
Use Categorize Data to correct matching. Section 1 is for address information; Section 2 covers title and contact information. Keep the traffic measure, source, and site ID out of the address fields. You can reach Categorize Data through the Data tab, or through a marker's gears. A matching change made there applies to all markers.
Check the site pins against the actual properties. A street address that places a marker at the wrong end of a large shopping center can confuse the access review. When you have verified coordinates for the storefront, retain them in the workfile with the site ID.
Color sites by the supplied traffic value
Open Map Tools and Grouping Tool / Colored Pins Tool. Choose Numeric for the prepared traffic count or rate column. Set the number of groups, choose percentage or number ranges, set the marker style, and select Group Now.
Use a small enough number of ranges that you can read the comparison. Keep each marker linked to the candidate site and its measured value. Inspect the range limits before describing sites as high or low traffic. A marker's color shows its range. The original figure gives you the detail.
For a shortlist, number ranges can help you compare the same traffic bands across views. If you change the ranges between views, a site can change color without its traffic changing. Keep the thresholds with the exported comparison when another person needs to read the result.
You can use a Secondary group for another useful variable, such as site type. Add it only if you can still tell which visual feature represents traffic. A heavily styled marker that requires several checks to interpret will slow down a property review.
A numerical heat map may help when you have many observation points. It smooths nearby values into a surface, so it answers a different visual question from a shortlist of individual sites. It can't recover pedestrian paths from candidate-site points, and a warm area doesn't show which entrance people use. Keep the site markers in view when you need to decide on a lease.
Filter like-for-like observations
Open Map Tools and Filter Tool. Use Create Filters on first use, or the gear if filters or grouping already exist. Select Date beside the observation-date column to choose the period. Use Group for the source, measure type, or daypart when those columns contain categories.
Apply the restrictions needed for one fair comparison. You might compare weekday lunch observations from the same collection method, then review weekend observations in a separate view. Don't combine those views into a single ranking unless your prepared measure accounts for the difference.
Check the surviving rows. Look for a candidate that disappeared because its date is outside the range, or because its source label was entered differently. Use a consistent category name in the source table, and disable an unneeded filter by unchecking its checkbox.
Keep the actual observed hours in view during review. Filtering both sites to the same date doesn't fix a full-day total at one and a two-hour total at the other. Confirm the numeric column represents the count or rate you intended before reading the colors.
When the set is ready, open Export Data on the right side of the map and choose Export Filtered Data. Select .xlsx, .tsv, .csv, or Clipboard and click Export Now. Retain the IDs, raw observations, hours, and source notes in the comparison file. Export Data Visible on Map follows the viewport, so zooming into one neighborhood can leave other shortlisted sites out of that export.
Check whether the traffic can reach the storefront
Inspect pedestrian access directly
Visit the shortlisted properties during the relevant trading hours and check the route between the count point and the entrance. Note the crossing, entrance location, storefront visibility, and any obstacle that changes how people approach the site. Keep those notes against the same site ID you used on the map.
A large passing count may occur across a road from the unit. Confirm whether people can cross conveniently and whether the storefront can be seen at the point where they choose their path. Also check that the counting location still matches the reported one after construction or an entrance change.
Compare what you find with the observation notes. An event-day surge may be relevant to an event-focused concept, while a store relying on ordinary weekday trade needs evidence for those hours. Collect the missing observation rather than treating a favorable count as a complete answer.
Foot traffic alone doesn't tell you whether passersby want the store's offer or will buy. Keep customer fit, entry behavior, and sales evidence as separate questions in the wider site review.
Keep driving reach separate
If driving access matters too, open Map Tools and Drive Time Polygon Tool. Enter the site's address, set hours and minutes, and select Add Drive Time Polygon. Use the result to inspect modeled driving reach with a consistent threshold across candidates.
The polygon assumes perfect driving conditions and excludes historical and current traffic. It describes driving access, so keep it separate from the pedestrian measurement and field-check difficult approaches. Resident counts within that area don't become foot traffic simply because they surround the site.
Send the traffic export on with the property questions that still need answers. Identify which sites need another count, an access check, or confirmation of the external source's visit definition before the team advances them to lease review.
