diff --git a/ext/AvizExt/viz/location_selection.jl b/ext/AvizExt/viz/location_selection.jl
index dad016e03..a94e5483f 100644
--- a/ext/AvizExt/viz/location_selection.jl
+++ b/ext/AvizExt/viz/location_selection.jl
@@ -131,12 +131,8 @@ function ADRIA.viz.ranks_to_frequencies(
 end
 
 """
-    ADRIA.viz.selection_criteria_map(rs::Union{Domain,ResultSet}, decision_matrix::YAXArray, scores::Vector{Float64};
-        criteria::Vector{Symbol}=Array(decision_matrix.criteria), opts::OPT_TYPE=DEFAULT_OPT_TYPE(), fig_opts::OPT_TYPE=set_figure_defaults(DEFAULT_OPT_TYPE()),
-        axis_opts::OPT_TYPE=set_axis_defaults(DEFAULT_OPT_TYPE()))
-    ADRIA.viz.selection_criteria_map!(g::Union{GridLayout,GridPosition}, rs::Union{Domain,ResultSet}, decision_matrix::YAXArray,
-        scores::Vector{Float64}; criteria::Vector{Symbol}=Array(decision_matrix.criteria), opts::OPT_TYPE=DEFAULT_OPT_TYPE(),
-        axis_opts::OPT_TYPE=set_axis_defaults(DEFAULT_OPT_TYPE()))
+    ADRIA.viz.selection_criteria_map(rs::Union{Domain,ResultSet}, decision_matrix::YAXArray, scores::Vector{Float64}; criteria::Vector{Symbol}=Array(decision_matrix.criteria), opts::OPT_TYPE=DEFAULT_OPT_TYPE(), fig_opts::OPT_TYPE=set_figure_defaults(DEFAULT_OPT_TYPE()), axis_opts::OPT_TYPE=set_axis_defaults(DEFAULT_OPT_TYPE()))
+    ADRIA.viz.selection_criteria_map!(g::Union{GridLayout,GridPosition}, rs::Union{Domain,ResultSet}, decision_matrix::YAXArray, scores::Vector{Float64}; criteria::Vector{Symbol}=Array(decision_matrix.criteria), opts::OPT_TYPE=DEFAULT_OPT_TYPE(), axis_opts::OPT_TYPE=set_axis_defaults(DEFAULT_OPT_TYPE()))
 
 Plot several spatial maps showing spatial distribution of location selection criteria, with the final map being the
 aggregate location selection score.
diff --git a/ext/AvizExt/viz/spatial.jl b/ext/AvizExt/viz/spatial.jl
index cfc8789e5..b20cc9f36 100644
--- a/ext/AvizExt/viz/spatial.jl
+++ b/ext/AvizExt/viz/spatial.jl
@@ -254,15 +254,14 @@ function ADRIA.viz.map!(
 end
 
 """
-    ADRIA.viz.map(rs::Union{Domain,ResultSet}, outputs_matrix::Matrix, map_titles::Vector{String}; opts::OPT_TYPE=DEFAULT_OPT_TYPE(),
-        fig_opts::OPT_TYPE=set_figure_defaults(DEFAULT_OPT_TYPE()), axis_opts::OPT_TYPE=set_axis_defaults(DEFAULT_OPT_TYPE()))
-    ADRIA.viz.map!(g::Union{GridLayout,GridPosition}, rs::Union{Domain,ResultSet}, outputs_matrix::Matrix, map_titles::Vector{String};
-        opts::OPT_TYPE=DEFAULT_OPT_TYPE(), axis_opts::OPT_TYPE=set_axis_defaults(DEFAULT_OPT_TYPE()))
+    ADRIA.viz.map(rs::Union{Domain,ResultSet}, outputs_matrix::Matrix, map_titles::Vector{String}; opts::OPT_TYPE=DEFAULT_OPT_TYPE(), fig_opts::OPT_TYPE=set_figure_defaults(DEFAULT_OPT_TYPE()), axis_opts::OPT_TYPE=set_axis_defaults(DEFAULT_OPT_TYPE()))
+    ADRIA.viz.map!(g::Union{GridLayout,GridPosition}, rs::Union{Domain,ResultSet}, outputs_matrix::Matrix, map_titles::Vector{String}; opts::OPT_TYPE=DEFAULT_OPT_TYPE(), axis_opts::OPT_TYPE=set_axis_defaults(DEFAULT_OPT_TYPE()))
 
 Plot a series of maps from an arbitrary (n_locs*n_maps) matrix of outputs.
 
 # Arguments
 - `rs` : ResultSet
+- `g` : Figure GridPosition or GridLayout.
 - `outputs_matrix` : Matrix of outputs where n_locs is the numberof locations and n_maps is the number of different
     maps to plot.
 - `map_titles` : Titles for each map to be plotted.
diff --git a/test/site_selection.jl b/test/site_selection.jl
index ddf02c7f2..3b4a9a6e9 100644
--- a/test/site_selection.jl
+++ b/test/site_selection.jl
@@ -89,3 +89,54 @@ end
         rank in 1:size(s_order, 1)
     ]) || "Ranking does not match mcda score ordering"
 end
+
+@testset "Test decision matrix spatial plotting" begin
+    dom = ADRIA.load_domain(TEST_DOMAIN_PATH, 45)
+    N = 2^3
+    scens = ADRIA.sample_selection(dom, N)  # get scenario dataframe
+    scen = scens[1, :]
+
+    # Get seeding preferences
+    seed_pref = ADRIA.decision.SeedPreferences(dom, scen)
+
+    # Calculate criteria vectors
+    # Cover
+    sum_cover = vec(sum(dom.init_coral_cover; dims=1).data)
+    # DHWS
+    dhw_scens = dom.dhw_scens[:, :, Int64(scen["dhw_scenario"])]
+    plan_horizon = Int64(scen["plan_horizon"])
+    decay = 0.99 .^ (1:(plan_horizon + 1)) .^ 2
+    dhw_projection = ADRIA.decision.weighted_projection(
+        dhw_scens, 1, plan_horizon, decay, 75
+    )
+    # Connectivity
+    area_weighted_conn = dom.conn.data .* ADRIA.site_k_area(dom)
+    conn_cache = similar(area_weighted_conn)
+    in_conn, out_conn, network = ADRIA.connectivity_strength(
+        area_weighted_conn, sum_cover, conn_cache
+    )
+
+    # Create decision matrix
+    seed_decision_mat = ADRIA.decision.decision_matrix(
+        dom.loc_ids,
+        seed_pref.names;
+        seed_in_connectivity=in_conn,
+        seed_out_connectivity=out_conn,
+        seed_heat_stress=dhw_projection,
+        seed_coral_cover=sum_cover
+    )
+
+    # Get results from applying MCDA algorithm
+    crit_agg = ADRIA.decision.criteria_aggregated_scores(
+        seed_pref, seed_decision_mat, mcda_funcs[1]
+    )
+
+    # Don't plot constant criteria
+    is_const = Bool[length(x) == 1 for x in unique.(eachcol(seed_decision_mat.data))]
+
+    # Plot normalized scores and criteria as map
+    decision_mat_fig = ADRIA.viz.selection_criteria_map(
+        dom, seed_decision_mat[criteria=.!is_const],
+        crit_agg.scores ./ maximum(crit_agg.scores)
+    )
+end