KNN GIST with a Lateral twist: Coming soon to a database near you   

The very constant

SELECT s.pid 
FROM ch10.land AS l
WHERE l.land_type = 'apartment'
ORDER BY ST_GeomFromText('POINT(234054 902182)',26986) <-> l.geom LIMIT 10;

The exploited correlated query constant

This approach forces a query for each row and each subquery in row references a constant geometry which is the current row thus is constant enough

SELECT l.pid AS reference_parcel 
  , (SELECT pid 
  FROM ch10.land As s 
    ORDER BY l.geom <#> s.geom LIMIT 1) As closest_parcel   
FROM ch10.land AS l
WHERE l.land_type = 'education';

And if you needed to get more than one row back, like what is the N closest (N > 1), it was some really ugly code you had to resort to if you wanted to use KNN gist. So ugly you'd be better off just sticking with ST_DWithin and guess an expansion to check.

Coming in PostgreSQL 9.3: LATERAL correlated query

But in 9.3 we have LATERAL which allows us to put our subquery back in the FROM clause and still utilize a KNN GIST index and adorn with as many additional computed columns as we want. It looks like this:

SELECT l.pid, s.pid As closest_apartment, s.dist_m
FROM ch10.land As l CROSS JOIN LATERAL 
(SELECT pid, ST_Distance(b.geom,l.geom) as dist_m 
  FROM ch10.land As b WHERE b.land_type = 'apartment'
   ORDER BY b.geom <-> l.geom LIMIT 10  ) As s 
   WHERE l.land_type = 'education'
ORDER BY l.pid, dist_m;

The explain plan demonstrates the spatial index is being used

Sort  (cost=15074.71..15081.26 rows=2620 width=24) (actual time=129.435..129.495 rows=2620 loops=1)
  Output: l.pid, b.pid, (st_distance(b.geom, l.geom))
  Sort Key: l.pid, (st_distance(b.geom, l.geom))
  Sort Method: quicksort  Memory: 301kB
  ->  Nested Loop  (cost=0.56..14925.95 rows=2620 width=24) (actual time=0.411..119.107 rows=2620 loops=1)
        Output: l.pid, b.pid, (st_distance(b.geom, l.geom))
        ->  Index Scan using idx_land_land_type on ch10.land l  (cost=0.29..178.63 rows=262 width=277) 
         (actual time=0.040..0.163 rows=262 loops=1)
              Output: l.pid, l.bldg_val, l.land_val, l.other_val, l.total_val, l.addr_num, l.full_str, l.land_type, l.units, l.geom
              Index Cond: ((l.land_type)::text = 'education'::text)
        ->  Limit  (cost=0.28..56.09 rows=10 width=277) (actual time=0.176..0.452 rows=10 loops=262)
              Output: b.pid, (st_distance(b.geom, l.geom)), ((b.geom <-> l.geom))
              ->  Index Scan using idx_land_geom_gist on ch10.land b  (cost=0.28..5882.65 rows=1054 width=277) 
              (actual time=0.174..0.449 rows=10 loops=262)
                    Output: b.pid, st_distance(b.geom, l.geom), (b.geom <-> l.geom)
                    Order By: (b.geom <-> l.geom)
                    Filter: ((b.land_type)::text = 'apartment'::text)
                    Rows Removed by Filter: 224
Total runtime: 129.687 ms