Comparing Economic Systems using Locus Maps

Locus Maps are FIS-based visualizations that show the number of companies per functional role in a geographically defined area. Locus Maps enable users to identify the commercial composition of a geographic area and compare it to other geographic areas.

At the heart of the Locus Model is the notion of “functional locations” for economic activities. Locus describes businesses by identifying their role (or functional location) in the economy and describing it using a constructed, standardized language derived from the Locus Model. In this language, each business function is identified by an activity, like transportation (“1.2” in the language), and the object of that activity, like information (“C”). When put together, we get the sentence “1.2 C” or “transports information,” a sentence that could describe the postal service or an internet service provider. We call this sentence a locus, because it functionally locates a business within the broader economic universe. Each locus represents a functional location.

Locus Maps are visual representations of loci for a defined geographic area. The axes are the activities and output resources from the Locus language, so each box on the grid contains businesses that are functionally similar to each other — each box contains businesses with the same locus, or functional location. Because the Locus language is comprehensive of all economic activity, every participant in an economy has a locus and can be located on a Locus Map.

In the current iteration of the Locus Map, boxes are sized based on the number of businesses with the given locus. 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.

By providing visual representations of loci, allows users to identify different typologies of economic communities. While economic activity isn’t distributed evenly across sub-regions, it does tend to cluster in identifiable ways. Because the 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. Some zip codes are central business districts, some manufacturing centers, and some residential neighborhoods. Below, we’ll walk you through a few example Maps to show how best to utilize to explore economic compositions.

I. Comparing States using Locus Maps

Locus Maps of Industrial States

State of Pennsylvania
Penn 536cfc67f4a624537928dab03cd256cd912163ed375db3824248937fb5af33ba
State of Michigan
Michigan 7ebf42e805bd6584cc5e3fdfbbba05afe795c6e445795bfcf8f068a3b3620d41
State of Ohio
Ohio b0ae4ef8c002eb01a89459440dc791bc6f13e166ea3a67a8d3fcaa51816144e2

Note: Boxes are sized by number of companies

Locus Maps of Agricultural States

State of Iowa
Iowa 6afc11b2edaffcd2f22be57e0db1226d114c1d7132bf403fff1129e65b85b589
State of Nebraska
Nebraska b2b269d276899875324a2fd804502971c89317236848617757d56cf76c011241
State of North Dakota
North dakota 8caa8b6c6950d21180f57f9120571fd498a418d2715f4ce26f64037589e396ae

Note: Boxes are sized by number of companies

Agricultural states are recognizable by their large 2 Fuel boxes, given their high concentrations of businesses that produce food.

II. Comparing Zip Codes using Locus Maps

Urban Residential Zip Codes

Union Square
New York, NY (10003)
10003 d36d300963654f3eff3f53e35932a176aa56193cc54342f39121092e648052a4
Pacific Heights
San Francisco, CA (94115)
94115 729e6b79a9414407cbd4f1acdf79fe6de469ecd5fcc834fcba225035ba540ad1
Boston, MA (02445)
02445 ea84dd01821b3a4c2dfc338f6c005e7df923106a243c79b0d8781afc1ffc9dc2

Note: Boxes are sized by number of companies

Urban Residential areas are noticeable by the presence of a large 2 People box (representing services for people like nail salons, barber shops, hospitals), a relatively large 3 Food services box (representing restaurants, bars, supermarkets) and not much else.

Central Business District Zip-Codes

Birmingham AL (35203)
35203 6e24c48a718d96677f7a6f88c0f9de405fb720007e1ce0708a09a2115336a480
Des Moines, IA (50309)
50309 4f55353cd1d64b7a64829740e1cf27bfa99626c06f56445e264d843bd3b36c19
Toledo, OH (43604)
43604 82c1daa78e59760728cbbc075e14994d549cd8568415bbfba04b0d725159aecb

Note: Boxes are sized by number of companies

Business Districts have high concentrations of 4 Div service businesses. These businesses are largely legal, consulting, and accounting firms that have many customers across various industries which are reflected by the Div object.

Financial Center Zip-Codes

Mid-town Manhattan
New York, NY (10022)
10022 b928c9f8489b2478015dd77e930bfcb92ebf7355062007d851f105c15621ee53
Boston Waterfront
Boston, MA (02210)
02210 fa623d16a0749d5f1f018908c1833d605b2f8b770950fb130291e4f4b61081b0

Greenwich, CT (06830)
06830 d00dd87b146271ab5d7e2b462f930e41672c73bf146f4852ca3f892533d7c13c

Note: Boxes are sized by number of companies

Financial Centers are found close to or mixed with Business Districts. There is a large presence of 4 Div and 4 Money service businesses. These businesses include finance, insurance, real estate asset managers, lawyers, consultants, and accountants.

Shopping Area Zip-Codes

King of Prussia Mall
King of Prussia, PA (19406)
19406 1e65237f9ce7c8e7d50a1dae0c0cce7d5c37ec863fd4e9bbcfe7cf590f09b088
South Coast Plaza
Costa Mesa, CA (92626)
92626 56f8b303637e254863ea81d1049c9247e3d1e2423dfe424b63f0d4fb60763c5d
Mall of America
Bloomington, MN (55425)
55425 36ddb01218675925b9398d94544c9cd79751358396ff89b066b114d1e337c14d

Note: Boxes are sized by number of companies

Areas with large shopping centers tend to have large 3 Equipment functions with slightly smaller 3 Fuel (food courts, bars, etc.) functions.

Specialty Industry Zip-Codes

Fashion District
New York, NY (10018)
10018 82e613e748c2f830a70c81d46bf174436bf045245cfecdc08d8135d1b9b6bf7b
Info Tech Area
Redwood City, CA (94065)
94065 9c030339eae8e72eba2e7ee15841c56aa4074c0d9227f61e2b53b44544465616
Industrial/ Tech
San Jose, CA (95134)
95134 3b2bcbe788414359f46b6e9d9cdba348aee35e1f1884a8eec7f2f0ee17399a4e

Note: Boxes are sized by number of companies

In zip codes with a specialty industry, that industry will dominate. For instance, you can see on the grid that Redwood City is the home of Oracle and other info-tech start-ups located in its business parks.

Investigate More for Yourself

Here are some example types of zip codes to explore.

University zip codes:

  • Stanford, Palo Alto, CA (94304)
  • Northwestern, Evanston, IL (60208)
  • Princeton, Princeton, NJ (08544)

University-adjacent tech developments:

  • Palo Alto, Palo Alto, CA (94306)
  • Northwestern, Evanston, IL (60202)
  • Princeton, Princeton, NJ (08540)