Comparing Economic Systems using Locus Maps

Locus Maps show the distribution of companies in geographically defined areas. Locus Maps enable users to compare the commercial compositions across different communities!

The Locus Model uses simple phrases to describe the products of economic agents -- we call this a locus. Just like English sentences, each locus is made up of a verb and an object; it’s as easy as saying “transports people” or “produces equipment”. The locus of an economic agent can also be thought as the functional location of that agent in the context of the larger economy. Locus Maps visualize loci by splitting the verb and object along the axes of a coordinate plane. As a result each box on the grid contains businesses that are functionally similar to each other.

By providing visual representations of loci, maps.locus allows users to identify unique typologies of economic communities. While economic activity isn’t distributed evenly across sub-regions, it does tend to cluster in identifiable ways. Because the Locus Maps are standardized, it’s easy to compare one geographic area (a zip code, a city, or a metropolitan area) to another and identify differences in the composition of businesses. Below, we’ll walk you through a few example Maps to show how best to utilize maps.locus to explore and compare economic compositions.

I. Comparing States using Locus Maps

Locus Maps of Agricultural States

State of Iowa

State of Nebraska

State of North Dakota
Each of these boxes are over 300% of the national average. This specific box represents companies that produce food or fuel -- think oil, corn, and food crops.

II. Comparing Zip Codes using Locus Maps

Urban Residential Zip Codes

Union Square
New York, NY (10003)

Pacific Heights
San Francisco, CA (94115)

Brookline
Boston, MA (02445)
Urban residential areas can be recognized by a large percentage of the businesses serving people. These businesses are hospitals, doctors, nail salons, barber shops, and spas.

Central Business District Zip Codes

Birmingham AL (35203)

Des Moines, IA (50309)

Toledo, OH (43604)
These areas tend to have large office buildings where lawyers, consultants, and government officials work.

Financial Center Zip Codes

Mid-town Manhattan
New York, NY (10022)

Newport Beach, CA (92660)

Greenwich, CT (06830)
Financial centers are distinguishable by two types of business functions. Similar to business districts, there is a large concentration of lawyers and consultants. Additionally, there is a larger proportion of money service firms like financial advisory, investment management, insurance, and accounting institutions. In the map above, the lawyers and consultants are identified by the blue and black box; the financial advisors, investment firms and accountants are in the blue-orange box. In these instances, the blue areas of the grid-boxes correspond to the management activity and the orange areas of the grid-boxes correspond with the resource money.

Specialty Industry Zip-Codes

Fashion District
New York, NY (10018)

Info Tech Area
Redwood City, CA (94065)

Industrial/ Tech
San Jose, CA (95134)

Locus Maps make it easy to visualize and compare economic specializations by region. These are three examples, each showing a different industry specialization.

  1. The fashion district in NYC has a higher proportion of businesses that produce and sell fabric and clothing.
  2. In Redwood City, Oracle and other info-tech startups cluster together in business parks.
  3. In San Jose, we see a large cluster of equipment manufacturers next to large clusters of software developers and platform providers. These activities seem to be closely linked -- equipment manufacturers and software/IT companies benefit from being geographically proximate.

Investigate More for Yourself

Here are some example types of zip codes to explore.

University zip codes:

University-adjacent tech developments:

The Future of Maps.Locus

In the current iteration of the Locus Map, boxes are sized based on the number of businesses with the given location. A bigger box for a given functional location indicates a more prevalent economic function in the selected universe. In future iterations, we plan to incorporate other sizing parameters, like revenue or number of employees, to allow for comparisons across multiple dimensions. Because the Locus language can also be used to describe jobs, we plan to incorporate employment data into the app to allow users to identify and compare labor distributions between geographic areas.