Clark County

I can provide this with precise, latest figures, but need your preference on source/year:

• 2020 Decennial Census (official headcount, limited variables), or
• 2019–2023 ACS 5-year estimates (most current, includes age/household details with margins of error).

Which do you prefer? If ACS, do you want margins of error included?

Clark County, SD snapshot (estimates)

• Population: ~3,800 (2020). Estimated email users: 2,600–2,900 residents, based on national adoption applied to local age structure.
• Age distribution of email use:
  • 18–29: ~93–97%
  • 30–49: ~93–97%
  • 50–64: ~88–92%
  • 65+: ~70–80%
  • Teens (13–17): ~80–90%
• Gender split: Roughly even; minor differences by age cohort, not statistically large.
• Digital access trends (ACS and rural SD benchmarks):
  • Broadband subscription: ~75–80% of households.
  • Device access (computer and/or smartphone): ~85–90% of households.
  • Smartphone‑only internet users: ~10–15%.
  • Rural residents report more reliance on fixed wireless and legacy DSL; fiber availability is growing via state/federal investments (e.g., BEAD), but coverage remains patchy outside towns.
• Local density/connectivity context:
  • Very low density (~4 people/sq. mile) increases last‑mile costs and slows fiber rollout.
  • Agricultural land use and dispersed addresses lead to greater dependence on wireless and satellite solutions compared with urban SD.

Method: Applied Pew Research email adoption by age to Clark County’s population and ACS rural connectivity patterns. Numbers are directional, suitable for planning rather than precise counts.

Below is a practical, directional summary using standard public sources (ACS S2801/S2802, FCC Broadband Data Collection, carrier coverage disclosures, and speedtest aggregators).

Executive snapshot

• Context: Clark County is a low‑density, rural county centered on the US‑212 corridor. Rural age structure and sparse infrastructure shape mobile adoption and performance.
• What’s different from the South Dakota average: slightly lower smartphone adoption, a higher share of households relying on cellular for home internet, fewer 5G mid‑band zones, and lower median mobile speeds away from the highway/town centers.

User estimates (order‑of‑magnitude, with assumptions noted)

• Total mobile users: roughly 2.8k–3.4k unique users countywide.
  • Basis: county population ~4k; adult share ~75–80%; smartphone adoption in rural SD typically trails the state average by ~3–8 percentage points.
• Smartphone adoption (household level): about 75–85% in Clark County vs roughly low‑to‑mid‑80s statewide.
  • Expect a slightly larger gap among 65+ households.
• Households primarily relying on mobile data for home internet: approximately 18–25% in Clark County vs ~12–16% statewide.
  • This “mobile‑only” internet reliance is driven by limited fixed‑line options on farms and ex‑urban parcels.

Demographic patterns (how Clark likely differs from statewide)

• Age:
  • Clark County has a higher share of residents 65+. Smartphone adoption among 65+ is typically 10–15 points lower than among all adults, widening the county–state gap versus SD overall.
  • Younger cohorts (under 35) are at or near parity with statewide smartphone adoption, but data plan constraints (caps/coverage) are more common in rural areas.
• Income and education:
  • Rural income mix and more price‑sensitive households correlate with higher use of budget Android devices and prepaid plans, and a greater reliance on mobile data in lieu of fixed broadband.
• Race/ethnicity:
  • Clark County is predominantly White with smaller minority populations than SD overall. Statewide disparities in device and broadband access tied to American Indian communities are less visible in Clark simply because the populations are smaller there; the main adoption divide locally is age/income rather than race.
• Household composition:
  • Higher share of single‑elderly and farm households increases odds of basic/voice‑centric phones and hotspot‑based home connectivity compared with the SD average.

Digital infrastructure and performance (county specifics vs statewide)

• Coverage mix:
  • 4G LTE: near‑universal along US‑212 and around towns; patchier on section roads and low‑lying areas. This is broadly similar to SD, but Clark sees more dead zones off‑corridor.
  • 5G:
    • Low‑band (coverage 5G) is present along main corridors/towns.
    • Mid‑band (C‑band/2.5 GHz) is limited mostly to town centers and the 212 corridor; coverage falls off quickly in farm/rangeland—meaning fewer areas enjoy the higher speeds common around larger SD metros.
• Speeds (typical, not measured here):
  • Median mobile download speeds are likely below the SD statewide median by a noticeable margin outside town centers (think tens of Mbps rather than high tens/low hundreds), with bigger variance by carrier.
• Carriers:
  • Verizon and AT&T generally provide the most consistent rural coverage; T‑Mobile’s low‑band covers the corridor, with mid‑band concentrated near town centers. Carrier choice matters more here than in SD’s larger cities.
• Tower/backhaul footprint:
  • Lower tower density than state averages; sites clustered along US‑212 and near communities. Off‑corridor sectors may be capacity‑constrained and dependent on microwave backhaul where fiber laterals are sparse.
  • Fiber backbones run the 212 corridor; rural cooperatives’ builds improve backhaul where present, but not uniformly across the county.
• Fixed‑mobile interplay:
  • Where DSL or cable is weak or absent, households commonly use smartphone hotspots or fixed‑wireless (LTE/5G) for home internet—more prevalent than in SD’s cities and larger towns.

Trends to watch (local vs state)

• Gradual catch‑up in 5G mid‑band as carriers infill along 212 and add rural sectors; this should narrow the speed gap with the state average, but coverage will remain spottier off‑corridor.
• Continued increase in “mobile‑only” households unless fiber/co‑op fixed wireless expands deeper into farms and acreages.
• Aging population dynamics mean overall smartphone penetration may rise more slowly than the state unless targeted programs drive senior adoption.

Clark County, SD social media snapshot (est.)

• User base

  • Residents: ~3,900
  • Estimated social media users (age 13+): 2,600–2,800 (≈78–84% of 13+)

• Most‑used platforms (share of local social media users)

  • YouTube: 75–80%
  • Facebook: 70–75%
  • Instagram: 30–35%
  • Snapchat: 30–40%
  • TikTok: 30–35%
  • Pinterest: 25–30% (skews female)
  • X (Twitter): 10–15%
  • LinkedIn: 10–15%
  • Reddit: 8–12%
  • WhatsApp: 5–10%

• Age mix of social users

  • 13–17: 7–9% (heaviest on Snapchat, TikTok; IG rising)
  • 18–29: 18–22% (IG/Snap/TikTok; YouTube nearly universal)
  • 30–49: 32–36% (Facebook and YouTube core; IG secondary)
  • 50–64: 22–26% (Facebook, YouTube)
  • 65+: 15–20% (Facebook primary; YouTube for how‑tos/news)

• Gender breakdown (of social users)

  • Women: 52–55% (higher Facebook, Pinterest use)
  • Men: 45–48% (higher YouTube, Reddit, X)

• Behavioral trends

  • Community‑first: Local news, school sports, church/fair updates, and county groups drive engagement.
  • Marketplace‑driven: Facebook Marketplace and buy/sell groups are key for vehicles, farm/ranch equipment, and services.
  • Video‑forward: Short‑form (Reels/TikTok) growing; practical long‑form on YouTube (repairs, ag, DIY) remains strong.
  • Messaging: Facebook Messenger is default across ages; teens/20s favor Snapchat for daily chat.
  • Trust cues: Real local faces, names, and teams outperform polished brand creative; comments/testimonials matter more than raw likes.
  • Timing: Peaks around 6–8 a.m. and 7–9 p.m.; weekend evenings strong; seasonal lift in winter and during weather events.
  • Ads: Best with simple, locally shot creative, clear offers, and tight geo‑targeting (≈10–25 miles); boosted posts often beat broad interest targeting.

Notes

• County‑level social stats aren’t directly published; figures are modeled from Census population, rural usage patterns, and recent U.S. platform adoption research. Treat as directional estimates.