I need to choose a few points to represent the grid below, e.g: 8 points in the grid below. This point need to be geographic distributed
This points needs to be geographic distributed. Lets see a bad and good example of how this selection should be.
Bad:
As you can see in image, the points 1, 2 , 3, 4 are selected in the image, represents a bad distribution because they are closer of each other, while a lot of blank spaces is left around the area.
Good:
In this case I have a better points distribution, as you can see looking at points.
Is there any known algorithm to solve this kind of problem? If not, what kind of approach can I use?
Follow the points used in this example.
[
{"display_id":1, "wkt":"POINT (-47.80695254757422 -22.678429514767355)"},
{"display_id":2, "wkt":"POINT (-47.806265165267931 -22.678709067305636)"},
{"display_id":3, "wkt":"POINT (-47.805185075503346 -22.678713359032901)"},
{"display_id":4, "wkt":"POINT (-47.804128517254583 -22.678972576366846)"},
{"display_id":5, "wkt":"POINT (-47.803315305801831 -22.679118168261493)"},
{"display_id":6, "wkt":"POINT (-47.80486315146171 -22.679622692653357)"},
{"display_id":7, "wkt":"POINT (-47.805928393847445 -22.679266043585148)"},
{"display_id":8, "wkt":"POINT (-47.805871214731461 -22.679927380399725)"},
{"display_id":9, "wkt":"POINT (-47.805506639679493 -22.680573125783766)"},
{"display_id":10, "wkt":"POINT (-47.804138349557441 -22.680221601307309)"},
{"display_id":11, "wkt":"POINT (-47.803295302306303 -22.680106119879895)"},
{"display_id":12, "wkt":"POINT (-47.803473382902766 -22.681130363637003)"},
{"display_id":13, "wkt":"POINT (-47.804627679504833 -22.680956502028398)"},
{"display_id":14, "wkt":"POINT (-47.805385901872484 -22.681506440753566)"},
{"display_id":15, "wkt":"POINT (-47.805064212910565 -22.68252301012539)"},
{"display_id":16, "wkt":"POINT (-47.804283035172091 -22.681816000936266)"},
{"display_id":17, "wkt":"POINT (-47.803872740310844 -22.682595698326967)"},
{"display_id":18, "wkt":"POINT (-47.803008668734286 -22.682028672335353)"},
{"display_id":19, "wkt":"POINT (-47.803257051331435 -22.68328279557819)"},
{"display_id":20, "wkt":"POINT (-47.804471052169916 -22.68312058413283)"},
{"display_id":21, "wkt":"POINT (-47.805032383854858 -22.683776531670816)"},
{"display_id":22, "wkt":"POINT (-47.804122198020593 -22.683839290355991)"},
{"display_id":23, "wkt":"POINT (-47.803418308129508 -22.683984010332541)"}
]
Polygon:
POLYGON ((-47.80270054936408 -22.67861903798622, -47.80285343527793 -22.68001730758419, -47.80286148190497 -22.68059393496459, -47.80286148190498 -22.68099979999268, -47.80276492238045 -22.68138091605933, -47.80258521437645 -22.68169026273702, -47.80247256159782 -22.68208622546592, -47.80232101678848 -22.68224708499785, -47.80221372842789 -22.68234607538523, -47.80218690633774 -22.68241536861385, -47.80226737260818 -22.68253415692431, -47.80255705118179 -22.68259355104094, -47.80323296785355 -22.68255890447602, -47.8034046292305 -22.68256385398584, -47.8034046292305 -22.68268759167296, -47.80330270528793 -22.68281627874909, -47.8032597899437 -22.68295486469597, -47.80317932367325 -22.68302415761687, -47.80247658491135 -22.68279153124382, -47.802412211895 -22.6828558747482, -47.8025034070015 -22.68294001620836, -47.80263215303421 -22.68319738976591, -47.80287355184555 -22.68342011649297, -47.80302911996841 -22.68385072047251, -47.80292719602585 -22.68440505923624, -47.80563622713089 -22.68439516034941, -47.80550748109818 -22.68284102624986, -47.80537873506547 -22.68250445985635, -47.8055289387703 -22.68243516667275, -47.80657231807709 -22.67930208827934, -47.80772030353546 -22.67802507844368, -47.80270054936408 -22.67861903798622))
