This　paper　presents　a　new　methodology　and　evaluation　experiments　on　the　detection　of　spatial　community　structure　in　movements,　which　can　reveal　unknown　spatial　constructs　and　boundaries.　While　there　are　numerous　existing　approaches　for　community　structure　detection　in　spatial　networks　using　either　general-purpose　methods　or　spatially　modified　extensions,　they　are　usually　designed　and　applied　without　controlled　evaluation　and　understanding　of　their　robustness　in　finding　the　underlying　spatial　communities.　Towards　addressing　this　challenge,　we　develop　a　new　approach,　Spatial　Tabu　Optimization　for　Community　Structure　(STOCS),　which　transforms　trajectory　data　to　a　spatial　network,　integrates　different　community　structure　measures　(e.g.　modularity　or　edge　ratio),　and　partitions　the　network　into　geographic　regions　to　discover　spatial　communities　in　movements.　We　systematically　evaluate　and　compare　the　new　approach　with　existing　methods　using　synthetic　datasets　that　have　known　spatial　community　structures.　Evaluation　results　show　that　general-purpose　(non-spatial)　methods　are　not　robust　for　detecting　spatial　structures　-　their　outcomes　vary　dramatically　for　the　same　data　with　different　levels　of　spatial　aggregation　(resolution),　data　sampling,　or　data　noise.　STOCS　is　substantially　more　robust　in　discovering　underlying　spatial　structures.　Last,　we　present　two　case　studies　with　animal　movements　and　urban　population　movements　to　demonstrate　the　application　of　the　approach.