1

I noticed that $near and $nearSphere returns completely different data and I got suspicious. After all, when latitude is near equator it sphere distance shouldn't differ a lot from euclidean distance.

db.tablebusiness.find({ "LongitudeLatitude" : { "$nearSphere" : [106.772835, -6.186753], "$maxDistance" : 0.053980478460939611 }, "Prominent" : { "$gte" : 15 }, "indexContents" : { "$all" : [/^soto/, /^nasi/] } }).limit(200);

It returns 48 data.

I'll attached it anyway

/* 0 */
{
  "_id" : "nasi-uduk-soto-ayam__-6.18_106.77",
  "BuildingID" : null,
  "Title" : "Nasi Uduk Soto Ayam",
  "InBuildingAddress" : null,
  "Building" : null,
  "Street" : "Jl. Panjang",
  "Districts" : [],
  "City" : "Jakarta",
  "Country" : "Indonesia",
  "LongitudeLatitudeFromGoogle" : null,
  "DistanceFromGoogleAddress" : 0.0,
  "Checkin" : 0,
  "Note" : null,
  "PeopleCount" : 0,
  "Prominent" : 45.5,
  "CountViews" : 0,
  "StreetAdditional" : null,
  "LongitudeLatitude" : {
    "Longitude" : 106.765673160553,
    "Latitude" : -6.17522230915668
  },
  "Rating" : {
    "Stars" : 0.0,
    "Weight" : 0.0
  },
  "CurrentlyWorkedURL" : null,
  "Reviews" : [],
  "ZIP" : null,
  "Tags" : ["Restaurant"],
  "Phones" : [],
  "Website" : null,
  "Email" : null,
  "Price" : null,
  "openingHour" : null,
  "Promotions" : [],
  "SomethingWrong" : false,
  "BizMenus" : [],
  "Brochures" : [],
  "Aliases" : [],
  "indexContents" : ["restaura", "estauran", "staurant", "taurant", "aurant", "urant", "rant", "ant", "nt", "t", "nasi", "asi", "si", "i", "uduk", "duk", "uk", "k", "soto", "oto", "to", "o", "ayam", "yam", "am", "m"]
}

/* 1 */
{
  "_id" : "nasi-soto-padang_pasar-slipi-jaya_-6.19_106.80",
  "BuildingID" : null,
  "Title" : "Nasi Soto Padang",
  "InBuildingAddress" : "Lt.1 Los Alas",
  "Building" : null,
  "Street" : "Jl.Kemanggisan Utama Raya",
  "Districts" : [],
  "City" : "Jakarta",
  "Country" : "Indonesia",
  "LongitudeLatitudeFromGoogle" : null,
  "DistanceFromGoogleAddress" : 0.0,
  "Checkin" : 0,
  "Note" : null,
  "PeopleCount" : 0,
  "Prominent" : 45.5,
  "CountViews" : 0,
  "StreetAdditional" : null,
  "LongitudeLatitude" : {
    "Longitude" : 106.79647564888,
    "Latitude" : -6.18998465381734
  },
  "Rating" : {
    "Stars" : 0.0,
    "Weight" : 0.0
  },
  "CurrentlyWorkedURL" : null,
  "Reviews" : [],
  "ZIP" : null,
  "Tags" : ["Restaurant"],
  "Phones" : [],
  "Website" : null,
  "Email" : null,
  "Price" : null,
  "openingHour" : null,
  "Promotions" : [],
  "SomethingWrong" : false,
  "BizMenus" : [],
  "Brochures" : [],
  "Aliases" : [],
  "indexContents" : ["restaura", "estauran", "staurant", "taurant", "aurant", "urant", "rant", "ant", "nt", "t", "nasi", "asi", "si", "i", "soto", "oto", "to", "o", "padang", "adang", "dang", "ang", "ng", "g"]
}

...

/* 47 */
{
  "_id" : "nasi-gandul__-7.43_109.24",
  "BuildingID" : null,
  "Title" : "Nasi Gandul",
  "InBuildingAddress" : null,
  "Building" : null,
  "Street" : "Jl. Brobahan Pr - 40 rt 004 Rw 004",
  "Districts" : [],
  "City" : "Purwokerto",
  "Country" : "Indonesia",
  "LongitudeLatitudeFromGoogle" : null,
  "DistanceFromGoogleAddress" : 0.0,
  "Checkin" : 0,
  "Note" : null,
  "PeopleCount" : 0,
  "Prominent" : 30.5,
  "CountViews" : 0,
  "StreetAdditional" : null,
  "LongitudeLatitude" : {
    "Longitude" : 109.239182174206,
    "Latitude" : -7.42585664273589
  },
  "Rating" : {
    "Stars" : 3.0,
    "Weight" : 1.0
  },
  "CurrentlyWorkedURL" : null,
  "Reviews" : [],
  "ZIP" : "53116",
  "Tags" : ["Angkringan", "Restaurant Indonesian", "Soto & Sop"],
  "Phones" : ["+62(281)7918181"],
  "Website" : null,
  "Email" : null,
  "Price" : null,
  "openingHour" : null,
  "Promotions" : [],
  "SomethingWrong" : false,
  "BizMenus" : [],
  "Brochures" : [],
  "Aliases" : [],
  "indexContents" : ["angkring", "ngkringa", "gkringan", "kringan", "ringan", "ingan", "ngan", "gan", "an", "n", "restaura", "estauran", "staurant", "taurant", "aurant", "urant", "rant", "ant", "nt", "t", "indonesi", "ndonesia", "donesian", "onesian", "nesian", "esian", "sian", "ian", "soto", "oto", "to", "o", "&", "sop", "op", "p", "nasi", "asi", "si", "i", "gandul", "andul", "ndul", "dul", "ul", "l"]
}

I put the data into excel and compute the distance using this formula

=SQRT(POWER(E2-$G$1,2)+POWER((F2-$H$1)*COS($H$1*PI()/180),2))

That one should greatly approximate spherical distance. Also notice that latitude is -6 which is not far from equator.

Here is the result

0.013516826
0.023857967
0.037658667
0.038737146
0.042414787
0.046725248
0.051006427
0.053567221
0.057448344
0.061592999
0.062329244
0.065276161
0.066035611
0.076251787
0.109671831
0.112097201
0.13417281
0.136471939
0.172293693
1.058802838
1.078123028
1.079160684
1.080954023
1.081148114
1.081099449
1.092061283
1.094281476
1.094431917
1.096845722
1.097063729
1.096953691
1.097201996
1.105389179
1.105442127
1.10839237
1.108717834
1.108840349
1.111636423
1.113187903
1.118767984
1.118767984
1.133952371
1.135077548
1.154967917
1.161142923
1.185994885
1.199509086
2.756884824

Here is the screenshot of my excel sheetenter image description here

Here is the actual excel

            106.772835  -6.186753
  "_id"      "nasi-uduk-soto-ayam__-6.18_106.77",   106.7656732 -6.175222309    0.013516826
  "_id"      "nasi-soto-padang_pasar-slipi-jaya_-6.19_106.80",  106.7964756 -6.189984654    0.023857967
  "_id"      "nasi-uduk-soto-ayam__-6.22_106.77",   106.7718959 -6.224620499    0.037658667
  "_id"      "nasi-campur-%26-soto-babat-kenanga_komp.-ruko-permata-senayan%2C_-6.22_106.79",   106.79199   -6.22062    0.038737146
  "_id"      "soto-mie-nasi_indomaret-(univ.-mercu-buana)_-6.21_106.74",    106.7379928 -6.211082144    0.042414787
  "_id"      "soto-kudus%2C-soto-ayam-%26amp%3B-nasi-pindang_plaza-senayan_-6.23_106.80",   106.7989969 -6.225694167    0.046725248
  "_id"      "soto-kudus%2C-soto-ayam-%26amp%3B-nasi-pindang_plaza-bri-ii_-6.22_106.81",    106.8136847 -6.217476287    0.051006427
  "_id"      "soto-kudus%2C-soto-ayam-%26amp%3B-nasi-pindang_automall-indonesia_-6.23_106.81",  106.8096431 -6.225898946    0.053567221
  "_id"      "soto-kudus%2C-soto-ayam-%26amp%3B-nasi-pindang_menara-mulia_-6.22_106.82",    106.81656   -6.224232983    0.057448344
  "_id"      "nasi-gule-gandaria__-6.24_106.80",    106.795285  -6.244444863    0.061592999
  "_id"      "nasi-soto-monggo-mampir__-6.24_106.80",   106.7971516 -6.244479441    0.062329244
  "_id"      "nasi-soto-ayam-\"pak-min\"__-6.24_106.82",    106.8151546 -6.236743573    0.065276161
  "_id"      "nasi-soto-ayam-pak-min---santa__-6.24_106.81",    106.812959  -6.239508   0.066035611
  "_id"      "soto-mie-nasi-bogor__-6.23_106.83",   106.8322349 -6.234845139    0.076251787
  "_id"      "nasi-uduk-soto-ayam__-6.24_106.87",   106.8693781 -6.239089942    0.109671831
  "_id"      "soto-ayam-%2F-nasi-uduk-betawi__-6.28_106.71",    106.714092  -6.282785   0.112097201
  "_id"      "soto-minang-roda-jaya_ruko-tongkol-indah_-6.12_106.89",   106.889044  -6.119294471    0.13417281
  "_id"      "nasi-bebek-dan-soto-mas-muchlis_summarecon-kelapa-gading_-6.16_106.91",   106.9059021 -6.156281135    0.136471939
  "_id"      "nasi-liwet-%26-soto-kwali-kalimalang__-6.25_106.93",  106.9338112 -6.248527508    0.172293693
  "_id"      "soto-ayam-nasi-rames__-6.88_107.58",  107.5751638 -6.881692483    1.058802838
  "_id"      "nasi-liwet-soto-solo_rumah-mode_-6.88_107.60",    107.5998616 -6.882452292    1.078123028
  "_id"      "nasi-soto-ayam-madura__-6.91_107.57", 107.5749707 -6.912900474    1.079160684
  "_id"      "nasi-soto-mie__-6.89_107.60", 107.5969434 -6.890355658    1.080954023
  "_id"      "nasi-rames-soto-bandung-enjoi__-6.89_107.60", 107.5970721 -6.890504777    1.081148114
  "_id"      "nasi-soto-ayam__-6.91_107.58",    107.5769877 -6.913560825    1.081099449
  "_id"      "nasi-soto-ayam__-6.91_107.60",    107.5977552 -6.90656    1.092061283
  "_id"      "nasi-soto-ayam-madura__-6.89_107.62", 107.6157832 -6.888615935    1.094281476
  "_id"      "nasi-goreng-soto-ayam__-6.90_107.61", 107.6093245 -6.89662571 1.094431917
  "_id"      "nasi-soto-gulai-kambing__-6.90_107.61",   107.6090455 -6.900715755    1.096845722
  "_id"      "putra-bengawan-nasi-goreng-%26-soto__-6.89_107.62",   107.617187  -6.891298   1.097063729
  "_id"      "nasi-soto-ayam__-6.90_107.61",    107.6088095 -6.901163102    1.096953691
  "_id"      "nasi-uduk-%26amp%3B-soto-kikil-cak-khohar__-6.89_107.61", 107.6144958 -6.894765295    1.097201996
  "_id"      "nasi-soto-ayam__-6.90_107.62",    107.6204395 -6.900453028    1.105389179
  "_id"      "nasi-goreng-soto-ayam__-6.90_107.62", 107.6206326 -6.900303912    1.105442127
  "_id"      "nasi-soto-ayam-madura__-6.91_107.62", 107.6169634 -6.909279174    1.10839237
  "_id"      "nasi-soto_pasar-baru_-6.92_107.61",   107.605226  -6.923423387    1.108717834
  "_id"      "nasi-soto-ayam-madura_pasar-baru_-6.92_107.61",   107.6053333 -6.92348729 1.108840349
  "_id"      "nasi-soto-sarinah__-6.90_107.63", 107.631175  -6.897282   1.111636423
  "_id"      "nasi-uduk-soto__-6.89_107.64",    107.6361465 -6.893636262    1.113187903
  "_id"      "nasi-soto-ayam-madura__-6.91_107.63", 107.629376  -6.910677   1.118767984
  "_id"      "nasi-soto-ayam__-6.91_107.63",    107.629376  -6.910677   1.118767984
  "_id"      "nasi-uduk-soto__-6.94_107.62",    107.6248813 -6.93937772 1.133952371
  "_id"      "nasi-soto-ayam-khas-madura__-6.94_107.63",    107.625749  -6.940099   1.135077548
  "_id"      "nasi-uduk-%26amp%3B-soto__-6.96_107.64",  107.638936  -6.955310213    1.154967917
  "_id"      "nasi-soto-ayam_ruko-metro-trade-center_-6.94_107.66", 107.6600504 -6.940208438    1.161142923
  "_id"      "nasi-soto-ayam-khas-madura__-6.94_107.69",    107.6948118 -6.937141164    1.185994885
  "_id"      "nasi-soto-ayam-khas-madura-cak-nonk__-6.93_107.72",   107.7156472 -6.932668019    1.199509086
  "_id"      "nasi-gandul__-7.43_109.24",   109.2391822 -7.425856643    2.756884824

Look again the term in the query

"$maxDistance" : 0.053980478460939611

Go figure.

Improve this question
2
  • Can you please clarify what your question is here?
    – JNK
    Commented Sep 7, 2012 at 12:06
  • updated the question title
    – user4951
    Commented Sep 7, 2012 at 12:40

1 Answer 1

Reset to default
3

You are seeing too many results for $nearSphere compared with $near because with spherical geometry operators (i.e. $nearSphere), you also need to convert the any distances used in the query (i.e. $maxDistance) to radians in order to get the right result. Here, it doesn't look like you converted $maxDistance to radians.

To convert from distance to radians, divide the distance by the radius of the earth. If you are using metric units, this would be 6378.137 km. If you are using imperial units, this would be: 3963.192 mi

Try the $nearSphere query again with $maxDistance converted to radians.

Improve this answer
4
  • Wait a minute. So we specify longitude and latitude in DEGREE and then specify the $maxDistance in radiant? Are you sure? Where can I see it? I am using degree as $maxDistance. Where in the documentation it says that it should be in radian? Also my result is off by 2, not by 180/pi
    – user4951
    Commented Sep 10, 2012 at 7:52
  • "All distances use radians. This allows you to easily multiply by the radius of the earth (about 6371 km or 3959 miles) to get the distance in your choice of units. Conversely, divide by the radius of the earth when doing queries." from: mongodb.org/display/DOCS/Geospatial+Indexing
    – andrewleung
    Commented Sep 10, 2012 at 13:59
  • Perfect. I upvoted your answer on the stackoverflow too.
    – user4951
    Commented Sep 11, 2012 at 9:06
  • I still don't get it. To convert 1 m to radians, I need to divide that meter by 6378.137 km? Why is it 1000 times off?
    – Vanuan
    Commented Jun 1, 2014 at 16:00

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