Veblen in the Metropolis: Land Use Proximity in United States

Urban LandscapesE. Anthon Eff

Middle Tennessee State University, Murfreesboro, TN

• Why no pawn shops in shopping malls?

• No liquor stores near churches?

• No adult bookstores near schools?

• No nursing homes near cemeteries?

• What are the (tacit) rules of land use proximity?– Instrumental? (health; efficiency)– Ceremonial? (status, power; superstition,

religion)

• Unconscious preferences Tacit rules Collective action Landuse regulations Landuse associations.

• Empirical look at landuse associations can give sense of these unconscious preferences.

• Data: all parcels in Davidson County, Tennessee (Nashville); 216,898 parcels, classified in 77 landuses.

• Calculate the probability that one land use will be adjacent to another.– Adjacent: two parcels adjacent when borders

within 70 feet of each other.– 216,898 parcels related in 2,397,367

proximate parcel pairs.

Example of parcel proximity relationships

matrix M: each cell mij gives the number of times that a parcel of land use i is proximate to a parcel of land use j. (M is 77x77, only eight rows and columns shown)

landuse description ADS AH AL AO ARBS BC BFI CAADS Auto Dealer - Sales 307 1 13 3 198 83 53 0AH Apartment High-rise 2 2 30 6 0 0 1 2AL Apartment Low-rise 13 25 2,586 65 36 6 6 183AO Apartment walkup 3 6 27 242 2 0 0 24ARBS Auto Repair and Body Shop 182 1 36 3 281 10 44 12BC Business Center 86 0 4 0 9 128 5 0BFI Branch Financial Institution 44 1 7 0 38 5 56 4CA Common Area 0 1 185 26 26 0 4 36,518

Matrix M can be used to create the transition matrix P, where each cell pij gives the probability that a parcel of land use i is proximate to a parcel of land use j.

jij

ijij m

mp

(Eight of 77 rows and columns shown)

landuse description ADS AH AL AO ARBS BC BFI CAADS Auto Dealer - Sales 0.09140 0.00030 0.00387 0.00089 0.05895 0.02471 0.01578 0.00000AH Apartment High-rise 0.00204 0.00204 0.03067 0.00613 0.00000 0.00000 0.00102 0.00204AL Apartment Low-rise 0.00086 0.00166 0.17176 0.00432 0.00239 0.00040 0.00040 0.01215AO Apartment walkup 0.00237 0.00475 0.02136 0.19146 0.00158 0.00000 0.00000 0.01899ARBS Auto Repair and Body Shop 0.04084 0.00022 0.00808 0.00067 0.06306 0.00224 0.00987 0.00269BC Business Center 0.05520 0.00000 0.00257 0.00000 0.00578 0.08216 0.00321 0.00000BFI Branch Financial Institution 0.01858 0.00042 0.00296 0.00000 0.01605 0.00211 0.02365 0.00169CA Common Area 0.00000 0.00002 0.00336 0.00047 0.00047 0.00000 0.00007 0.66354

matrix X: each cell xij gives the expected probability that a parcel of land use i is proximate to a parcel of land use j.

i jij

iij

ijij m

mpEx

The expected proximity matrix X can then be compared with the actual proximity matrix P to give matrix D:

D = P - X

Net Probability: D = P - X

(Eight of 77 rows and columns shown)

These net probabilities represent the tacit rules governing land use associations.

landuse description ADS AH AL AO ARBS BC BFI CAADS Auto Dealer - Sales 0.089984 -0.000113 -0.002459 0.000362 0.057073 0.024055 0.014783 -0.023137AH Apartment High-rise 0.000633 0.001634 0.024345 0.005604 -0.001873 -0.000655 0.000027 -0.021092AL Apartment Low-rise -0.000549 0.001249 0.165429 0.003786 0.000518 -0.000256 -0.000597 -0.010982AO Apartment walkup 0.000961 0.004336 0.015031 0.190924 -0.000291 -0.000655 -0.000996 -0.004150ARBS Auto Repair and Body Shop 0.039432 -0.000187 0.001749 0.000142 0.061188 0.001589 0.008879 -0.020444BC Business Center 0.053787 -0.000411 -0.003762 -0.000531 0.003903 0.081502 0.002214 -0.023137BFI Branch Financial Institution 0.017169 0.000011 -0.003374 -0.000531 0.014174 0.001456 0.022653 -0.021448CA Common Area -0.001412 -0.000393 -0.002968 -0.000059 -0.001401 -0.000655 -0.000923 0.640404

(Eight of 77 rows and columns shown)

Structural Equivalence: In order to compare the similarity between i and j in the pattern of their associations, one can take the Jaccard distance between row i and row j of matrix D, giving matrix E:

IDF IDS ADS AH AL AO ARBS BC BFI CAADS Auto Dealer - Sales 0.000000 0.878459 0.868719 0.783421 0.418336 0.736964 0.742148 0.982328AH Apartment High-rise 0.878459 0.000000 0.940601 0.737613 0.877273 0.888207 0.863837 0.984481AL Apartment Low-rise 0.868719 0.940601 0.000000 0.928712 0.840917 0.939148 0.920112 0.842867AO Apartment walkup 0.783421 0.737613 0.928712 0.000000 0.802846 0.893333 0.869944 0.982560ARBS Auto Repair and Body Shop 0.418336 0.877273 0.840917 0.802846 0.000000 0.781605 0.732070 0.977203BC Business Center 0.736964 0.888207 0.939148 0.893333 0.781605 0.000000 0.813180 0.990060BFI Branch Financial Institution 0.742148 0.863837 0.920112 0.869944 0.732070 0.813180 0.000000 0.990645CA Common Area 0.982328 0.984481 0.842867 0.982560 0.977203 0.990060 0.990645 0.000000

n

kjkik

n

kjkik

ij

mm

mmb

1

1 eij=2bij/(1+bij)

landuse description freq. in pairs

no. of parcels

as pct. of all parcels

net prob. self-proximity

openness lowest net prob.

highest net prob.

SFDPP Single Family Dwelling Per Parcel 1,017,839 132,670 61.167 0.3741 -0.25249 RESCU SFDPPRESCU Residential Condominium Unit 295,806 19,272 8.885 0.6426 -0.09271 SFDPP RESCUVRESL Vacant Residential Land 212,654 14,220 6.556 0.2891 -0.0236 SFDPP VRESLXXX Unknown (always open land) 252,510 3,620 1.669 0.3527 -0.02056 SFDPP XXXSFD Single Family Dwelling 118,039 6,580 3.034 0.4551 -0.01068 SFDPP SFDCA Common Area 55,564 6,155 2.838 0.6404 -0.00866 SFDPP CAD Duplex 70,939 9,426 4.346 0.2176 -0.00722 RESCU D

Omitting 63 land uses in the middleTOHDW Terminal or Heavy Duty Warehouse 10,819 690 0.318 0.1343 0.01608 SFDPP TOHDWSSO Strip Shopping (no major tenants) 6,134 560 0.258 0.0926 0.01903 SFDPP OSGRSOSGRS One Story General Retail Store 10,193 878 0.405 0.2123 0.02061 SFDPP OSGRSVIL Vacant Industrial Land 14,447 689 0.318 0.2526 0.02212 SFDPP VILOBNML Office Building (Non-Medical Low-rise) 15,636 1,766 0.814 0.1586 0.02601 SFDPP OBNMLPL Parking Lot 11,847 1,441 0.664 0.2078 0.02814 SFDPP PLVCL Vacant Commercial Land 36,751 3,219 1.484 0.2441 0.05106 SFDPP VCL

From Table 1: 7 landuses with highest openness (all commercial or industrial) and 7 landuses with lowest openness (all respectable housing or common area)

Block Modeled Graph

Summary of tacit rules:

• Single family home on city lot is most isolated from other land uses.

• Residential is isolated from commercial.

• Lower-status housing (apartments, mobile homes) tends to be more associated with commercial land uses.

How do tacit rules of land use associations vary across space?

• Compliance less in low income areas?

• Compliance less in older areas?

Use Jaccard distance (matrix E). For each parcel find mean of the distances between its landuse and the landuses of adjacent parcels. Call this mean distance.

For this parcel, calculate mean of distances between its landuse and the landuses of adjacent parcels

Map colors: Local G* z-score for mean distance. Darkest color is significantly high distance, lightest color is significantly low distance.

Chart: mean distance of parcels along transect.

Inner City

Subdivisions

Subdivisions

Inner City

Tract means: Mean distance for Single Family Dwellings per Parcel, againstappraised value of home and against median HH income

Tract means: Mean distance for Single Family Dwellings per Parcel, againstpercentage black and percentage white

Summary of variation over space:

• Parcels more likely to be adjacent to unlike landuses toward city center.

• Higher income tracts and higher home appraised value tracts have homes more isolated from unlike landuses.

• Racial composition appears to have no effect on whether homes are adjacent to unlike landuses.

• Structure and homogeneity of suburbs serve to isolate homes from unlike landuses.

• 18th century Europe, emerging capitalist middle class, erosion of traditions (Möser, Simmel)– Paternalistic relationships Instrumental

relationships (e.g., serfs wage labor)– Pecuniary valuations displace traditional values.– Old elite displaced (e.g., guild masters lose market

to factory owners).

• Fashion cycles begin; work areas move out of home to different part of city; home separates into public and private areas (Braudel)

• 19th century Europe, middle class cult of the family (“domestic ideology”), centered on home, provides new source of meaning (Guttormsson)

• Simmel:– High status seek to differentiate selves from low

status. Low status seek to emulate high status.– High status maintain distinctions through:

• Constant innovation (fashion)• Distinctions difficult to emulate (Veblen)

– Conspicuous consumption to signal status in period with rapid change in elite. (home as a display good; home’s “public” areas filled with display goods)

– Conspicuous leisure as the most potent signal of status. (Work areas removed from home. Work moved to different quarter of the city)

• Sumptuary laws

• Sumptuary laws are collective action by high status, to maintain differentiation– Boundary maintenance is a collective good

(Olson), benefiting all high status. – Sumptuary laws important in maintaining

boundary when emulation relatively easy (location is easy to emulate)

– Cheaters have incentive to help low status emulate high status location (“chop” elite homes; infill with apartments)

– Collective action to coerce cheaters: landuse regulations.

Summary:

• Most salient separation is between single family home on city lot and commercial landuses.

• This separation based on – the use of the home as a signal of status,

removing from the home all trace of useful work.– the “domestic ideology”: family as the focus of

life, separated from external world

• Landuse regulations function as sumptuary laws maintaining status boundary.