Going concrete about managing the concrete jungle

Last week I wrote about delivery platforms and their effect on restaurant density. To be honest, the piece vastly underperformed.

One possible reason might be that the piece was too nerdy, or just boring.

Another one - the one I’m holding on to right now - is that it didn’t reach the potential “nerd-herd” out there who might be interested in the topic.

So here’s another try to reach that untapped audience. Or at least a sub-group among those folks: the ones with an active stake in managing the urban space who could make a better use of data.

By the way, even if your job doesn’t involve overseeing your city’s sidewalks, this post may matter to you too. Specially if you like to walk in the street and not be run by a courier - like it happened to me a few weeks ago.

From Guesswork to Grid: How Data Can Unclog the Sidewalk

One problem with the data debate, as with words like data-driven, data-informed, etc. is that it’s hard to specify how data can actually help make decisions. A good way to start is by clarifying exactly what is the question that data can help answer.

Roman philosophers said “no wind is favorable if you don’t know where you’re going”… today, we can follow suit with “no data will be useful if you don’t know what question you want it to answer.”

Let’s move from philosophy to decisions, then.

The skyrocketing increase of last-mile deliveries is impacting urban street-life. I analyzed some of these impacts here, closing with a brief playbook on how data can help city managers respond to these changes.

But let’s get even more concrete: data lets city managers..

1. Detect areas where couriers spend more time making their deliveries (dwell time).

2. Notice where couriers and deliverers are blocking sidewalks (sidewalk blockage index).

3. Identify the streets where residents are voicing more complaints (daily complaints)

Here is a quick description of the indicators and data sources for each of those uses:

Building on those indicators, I quickly built a “Curb Management Dashboard” demo using Claude and synthetic data. The heatmap shows the city areas with highest sidewalk blockage and average dwell times. Traffic-light coloring points to congested streets. This has many uses for city managers. For example, when thresholds are crossed, field officers could get a notification on their handhelds. Peak hour trends can also inform a more efficient allocation of field officers along the day.

Let’s see how this could be applied in one of the streets where I was almost injured by a reckless courier in Madrid:

Calle Barceló, Madrid, Tuesday 12PM

• Data insight: Average dwell time = 5.8 min (target < 3 min); blockage index = 70 %.

• Action: Installed a 10m2 “delivery space” + adjusted pickup window signage.

• Result after 30 days: dwell time down to 3 min; complaints cut in half.

Real time monitoring is definitely a useful application of data, but historical records are particularly valuable. The analysis of these indicators over time can help identify trends, including seasonal changes, as well as measure impacts of certain policies and interventions such as capital investments in dedicated waiting zones, or other regulations such as Barcelona’s zoning of dark kitchens.

Hopefully, this demo dashboard makes my point about the value of data more concrete. Numbers change the conversation. A six-minute average dwell time triggers enforcement more precisely than a hunch. A 70 percent sidewalk obstruction rate makes budget requests for pickup zones evidence-based rather than speculative. Data also levels the playing field: the same key indicators apply across every block, protecting neighborhoods that often get overlooked in favor of headline corridors favored by influential council-members.

To close with the tune I keep singing: when we make curb space visible, the concrete jungle becomes more manageable.