You can achieve this using the imagesc function in MATLAB. First, create a colormap for the categories, and then use the imagesc function to display the 2D grid with the colors defined by the categorical mask. This will create a plot of the 2D grid with each tile's color defined by the x2 categorical mask. The colormap is set to match the colors of your categories (black for empty bins, green, orange, yellow, and red for the other categories). The colorbar shows the correspondence between colors and categories.
% Dummy data
datO = [1000000.*rand(4000,1), 10000.*rand(4000,1), 250 + 500.*rand(4000,1)];
% Dummy logical masks for categorization
LG = false(4000,1); LG(1:4:4000) = true;
LO = false(4000,1); LO(2:4:4000) = true;
LY = false(4000,1); LY(3:4:4000) = true;
LR = false(4000,1); LR(4:4:4000) = true;
% Histogram bins
xbins = [min(datO(:,1)):12500:max(datO(:,1))];
ybins = [min(datO(:,3)):12:max(datO(:,3))];
% Preallocation for best practice
x1 = zeros(numel(xbins)-1,numel(ybins)-1,4);
% Counts for each category
x1(:,:,1) = histcounts2(datO(LG,1),datO(LG,3),xbins,ybins); % Green counts
x1(:,:,2) = histcounts2(datO(LO,1),datO(LO,3),xbins,ybins); % Orange counts
x1(:,:,3) = histcounts2(datO(LY,1),datO(LY,3),xbins,ybins); % Yellow counts
x1(:,:,4) = histcounts2(datO(LR,1),datO(LR,3),xbins,ybins); % Red counts
% Majority category by bin & recover empty bins
[~,x2] = max(x1,[],3);
x2(~any(x1,3)) = 0;
% Create a colormap for the categories (assuming 0 is for empty bins)
custom_colormap = [0 0 0; 0 1 0; 1 0.5 0; 1 1 0; 1 0 0]; % [black; green; orange; yellow; red]
% Plot the 2D grid using imagesc
figure;
imagesc(x2);
colormap(custom_colormap);
colorbar('Ticks', 0:4, 'TickLabels', {'Empty', 'Green', 'Orange', 'Yellow', 'Red'});
axis equal;
colormap(colormap);
colorbar('Ticks', 0:4, 'TickLabels', {'Empty', 'Green', 'Orange', 'Yellow', 'Red'});
axis equal;
