Although　point　patterns　have　been　intensively　studied,　the　problem　of　how　to　extract　the　structure　information　from　a　clustered　spatial　point　pattern　still　remains.　The　author　put　forth　the　H　function,　which　is　capable　of　measuring　the　distributive　characteristics　of　a　two　dimension　discrete　point　set　in　a　square,　and　derives　its　analytical　expression　theoretically　based　on　geometric　probability.　Then　a　general　algorithm　to　extract　the　structure　information　of　a　two　dimension　discrete　point　set　is　designed　and　implemented　by　means　of　H　function.　Subsequently　the　algorithm　is　employed　to　process　a　clustered　spatial　point　pattern　consisting　of　real　residential　coordinate　data.　Its　structure　information　is　derived　and　visualized.　After　that,　generate　the　Delaunay　Triangulation　and　Voronoi　Diagram　of　the　same　spatial　point　pattern　respectively,　keep　the　vertices　of　1/10　and　1/100　Delaunay　triangles　with　the　least　area　in　the　point　set　to　form　two　graphs,　and　keep　the　generators　of　1/10　and　1/100　Voronoi　polygons　with　the　least　area　in　the　point　set　to　form　another　two　graphs,　make　a　contrast　between　the　result　of　the　algorithm　with　each　of　the　four　graphs.　It　turns　out　that　although　Delaunay　Triangulation　and　Voronoi　Diagram　are　capable　of　indicating　the　local　point　density　of　a　clustered　spatial　point　pattern,　they　are　explicitly　limited　in　terms　of　extracting　its　structure　information,　while　the　algorithm　introduced　in　this　article　can　effectively　perform　doing　so.