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Merge pull request #537 from mapbox/adjust-cluster

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Make clusters look better by averaging locations of clustered points
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Eric Fischer 2018-03-02 11:15:07 -08:00 committed by GitHub
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11 changed files with 3967 additions and 3946 deletions
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4
CHANGELOG.md
View File

@ -1,3 +1,7 @@
## 1.27.9
* Make clusters look better by averaging locations of clustered points
## 1.27.8
* Add --accumulate-attribute to keep attributes of dropped, coalesced,

6
README.md
View File

@ -218,7 +218,7 @@ tippecanoe -z5 -o filtered.mbtiles -j '{ "ne_10m_admin_0_countries": [ "all", [
compensate for the larger marker, or `-Bf`*number* to allow at most *number* features in the densest tile.
* `-al` or `--drop-lines`: Let "dot" dropping at lower zooms apply to lines too
* `-ap` or `--drop-polygons`: Let "dot" dropping at lower zooms apply to polygons too
* `-K` _distance_ or `--cluster-distance=`_distance_: Cluster points (as with `--cluster-densest-as-needed`, but without the experimental discovery process) that are approximately within _distance_ of each other. The units are tile coordinates within a nominally 256-pixel tile, so the maximum value of 255 allows only one feature per tile. Values around 20 are probably appropriate for typical marker sizes.
* `-K` _distance_ or `--cluster-distance=`_distance_: Cluster points (as with `--cluster-densest-as-needed`, but without the experimental discovery process) that are approximately within _distance_ of each other. The units are tile coordinates within a nominally 256-pixel tile, so the maximum value of 255 allows only one feature per tile. Values around 10 are probably appropriate for typical marker sizes. See `--cluster-densest-as-needed` below for behavior.
### Dropping a fraction of features to keep under tile size limits
@ -229,7 +229,7 @@ tippecanoe -z5 -o filtered.mbtiles -j '{ "ne_10m_admin_0_countries": [ "all", [
* `-aD` or `--coalesce-densest-as-needed`: Dynamically combine the densest features from each zoom level into other nearby features to keep large tiles under the 500K size limit. (Again, mostly useful for polygons.)
* `-aS` or `--coalesce-fraction-as-needed`: Dynamically combine a fraction of features from each zoom level into other nearby features to keep large tiles under the 500K size limit. (Again, mostly useful for polygons.)
* `-pd` or `--force-feature-limit`: Dynamically drop some fraction of features from large tiles to keep them under the 500K size limit. It will probably look ugly at the tile boundaries. (This is like `-ad` but applies to each tile individually, not to the entire zoom level.) You probably don't want to use this.
* `-aC` or `--cluster-densest-as-needed`: If a tile is too large, try to reduce its size by increasing the minimum spacing between features, and leaving one placeholder feature from each group. The remaining feature will be given a `"cluster": true` attribute to indicate that it represents a cluster, a `"point_count"` attribute to indicate the number of features that were clustered into it, and a `"sqrt_point_count"` attribute to indicate the relative width of a feature to represent the cluster.
* `-aC` or `--cluster-densest-as-needed`: If a tile is too large, try to reduce its size by increasing the minimum spacing between features, and leaving one placeholder feature from each group. The remaining feature will be given a `"cluster": true` attribute to indicate that it represents a cluster, a `"point_count"` attribute to indicate the number of features that were clustered into it, and a `"sqrt_point_count"` attribute to indicate the relative width of a feature to represent the cluster. If the features being clustered are points, the representative feature will be located at the average of the original points' locations; otherwise, one of the original features will be left as the representative.
### Dropping tightly overlapping features
@ -309,7 +309,7 @@ The postfilter receives the features at tile resolution, after simplification, c
The layer name is provided as part of the `tippecanoe` element of the feature and must be passed through
to keep the feature in its correct layer. In the case of the prefilter, the `tippecanoe` element may also
contain `index`, `sequence`, and `extent` elements, which must be passed through for internal operations like
contain `index`, `sequence`, `extent`, and `dropped`, elements, which must be passed through for internal operations like
`--drop-densest-as-needed`, `--drop-smallest-as-needed`, and `--preserve-input-order` to work.
#### Examples:

72
dirtiles.cpp
View File

@ -152,48 +152,48 @@ std::vector<zxy> enumerate_dirtiles(const char *fname) {
}
sqlite3 *dirmeta2tmp(const char *fname) {
sqlite3 *db;
char *err = NULL;
sqlite3 *db;
char *err = NULL;
if (sqlite3_open("", &db) != SQLITE_OK) {
fprintf(stderr, "Temporary db: %s\n", sqlite3_errmsg(db));
exit(EXIT_FAILURE);
}
if (sqlite3_exec(db, "CREATE TABLE metadata (name text, value text);", NULL, NULL, &err) != SQLITE_OK) {
fprintf(stderr, "Create metadata table: %s\n", err);
exit(EXIT_FAILURE);
}
if (sqlite3_open("", &db) != SQLITE_OK) {
fprintf(stderr, "Temporary db: %s\n", sqlite3_errmsg(db));
exit(EXIT_FAILURE);
}
if (sqlite3_exec(db, "CREATE TABLE metadata (name text, value text);", NULL, NULL, &err) != SQLITE_OK) {
fprintf(stderr, "Create metadata table: %s\n", err);
exit(EXIT_FAILURE);
}
std::string name = fname;
name += "/metadata.json";
std::string name = fname;
name += "/metadata.json";
FILE *f = fopen(name.c_str(), "r");
if (f == NULL) {
perror(name.c_str());
exit(EXIT_FAILURE);
}
FILE *f = fopen(name.c_str(), "r");
if (f == NULL) {
perror(name.c_str());
exit(EXIT_FAILURE);
}
json_pull *jp = json_begin_file(f);
json_object *o = json_read_tree(jp);
json_pull *jp = json_begin_file(f);
json_object *o = json_read_tree(jp);
if (o->type != JSON_HASH) {
fprintf(stderr, "%s: bad metadata format\n", name.c_str());
exit(EXIT_FAILURE);
}
if (o->type != JSON_HASH) {
fprintf(stderr, "%s: bad metadata format\n", name.c_str());
exit(EXIT_FAILURE);
}
for (size_t i = 0; i < o->length; i++) {
if (o->keys[i]->type != JSON_STRING || o->values[i]->type != JSON_STRING) {
fprintf(stderr, "%s: non-string in metadata\n", name.c_str());
}
for (size_t i = 0; i < o->length; i++) {
if (o->keys[i]->type != JSON_STRING || o->values[i]->type != JSON_STRING) {
fprintf(stderr, "%s: non-string in metadata\n", name.c_str());
}
char *sql = sqlite3_mprintf("INSERT INTO metadata (name, value) VALUES (%Q, %Q);", o->keys[i]->string, o->values[i]->string);
if (sqlite3_exec(db, sql, NULL, NULL, &err) != SQLITE_OK) {
fprintf(stderr, "set %s in metadata: %s\n", o->keys[i]->string, err);
}
sqlite3_free(sql);
}
char *sql = sqlite3_mprintf("INSERT INTO metadata (name, value) VALUES (%Q, %Q);", o->keys[i]->string, o->values[i]->string);
if (sqlite3_exec(db, sql, NULL, NULL, &err) != SQLITE_OK) {
fprintf(stderr, "set %s in metadata: %s\n", o->keys[i]->string, err);
}
sqlite3_free(sql);
}
json_end(jp);
fclose(f);
return db;
json_end(jp);
fclose(f);
return db;
}

11
main.cpp
View File

@ -1195,7 +1195,7 @@ int read_input(std::vector<source> &sources, char *fname, int maxzoom, int minzo
}
// Trim out characters that can't be part of selector
std::string out;
std::string out;
for (size_t p = 0; p < trunc.size(); p++) {
if (isalpha(trunc[p]) || isdigit(trunc[p]) || trunc[p] == '_' || (trunc[p] & 0x80) != 0) {
out.append(trunc, p, 1);
@ -2518,7 +2518,8 @@ int main(int argc, char **argv) {
src.layer = std::string(optarg).substr(0, cp - optarg);
src.file = std::string(cp + 1);
sources.push_back(src);
} break;
break;
}
case 'z':
if (strcmp(optarg, "g") == 0) {
@ -2695,7 +2696,8 @@ int main(int argc, char **argv) {
exit(EXIT_FAILURE);
}
}
} break;
break;
}
case 'a': {
char *cp;
@ -2707,7 +2709,8 @@ int main(int argc, char **argv) {
exit(EXIT_FAILURE);
}
}
} break;
break;
}
case 'v':
fprintf(stderr, VERSION);

11
man/tippecanoe.1
View File

@ -3,6 +3,8 @@
Builds vector tilesets \[la]https://www.mapbox.com/developers/vector-tiles/\[ra] from large (or small) collections of GeoJSON \[la]http://geojson.org/\[ra], Geobuf \[la]https://github.com/mapbox/geobuf\[ra], or CSV \[la]https://en.wikipedia.org/wiki/Comma-separated_values\[ra] features,
like these \[la]MADE_WITH.md\[ra]\&.
.PP
[Mapbox Tippecanoe](\[la]https://user-images.githubusercontent.com/1951835/36568734-ede27ec0-17df-11e8-8c22-ffaaebb8daf4.JPG\[ra])
.PP
[Build Status](https://travis\-ci.org/mapbox/tippecanoe.svg) \[la]https://travis-ci.org/mapbox/tippecanoe\[ra]
[Coverage Status](https://coveralls.io/repos/mapbox/tippecanoe/badge.svg?branch=master&service=github) \[la]https://coveralls.io/github/mapbox/tippecanoe?branch=master\[ra]
.SH Intent
@ -212,7 +214,8 @@ If the type is \fB\fCfloat\fR or \fB\fCint\fR and the original attribute was non
If the type is \fB\fCint\fR and the original attribute was floating\-point, it is rounded to the nearest integer.
.IP \(bu 2
\fB\fC\-E\fR\fIattribute\fP\fB\fC:\fR\fIoperation\fP or \fB\fC\-\-accumulate\-attribute=\fR\fIattribute\fP\fB\fC:\fR\fIoperation\fP: Preserve the named \fIattribute\fP from features
that are dropped, coalesced\-as\-needed, or clustered. The \fIoperation\fP may be \fB\fCsum\fR, \fB\fCproduct\fR, \fB\fCmean\fR, \fB\fCconcat\fR, or \fB\fCcomma\fR
that are dropped, coalesced\-as\-needed, or clustered. The \fIoperation\fP may be
\fB\fCsum\fR, \fB\fCproduct\fR, \fB\fCmean\fR, \fB\fCmax\fR, \fB\fCmin\fR, \fB\fCconcat\fR, or \fB\fCcomma\fR
to specify how the named \fIattribute\fP is accumulated onto the attribute of the same name in a feature that does survive.
.RE
.SS Filtering features by attributes
@ -247,7 +250,7 @@ compensate for the larger marker, or \fB\fC\-Bf\fR\fInumber\fP to allow at most
.IP \(bu 2
\fB\fC\-ap\fR or \fB\fC\-\-drop\-polygons\fR: Let "dot" dropping at lower zooms apply to polygons too
.IP \(bu 2
\fB\fC\-K\fR \fIdistance\fP or \fB\fC\-\-cluster\-distance=\fR\fIdistance\fP: Cluster points (as with \fB\fC\-\-cluster\-densest\-as\-needed\fR, but without the experimental discovery process) that are approximately within \fIdistance\fP of each other. The units are tile coordinates within a nominally 256\-pixel tile, so the maximum value of 255 allows only one feature per tile. Values around 20 are probably appropriate for typical marker sizes.
\fB\fC\-K\fR \fIdistance\fP or \fB\fC\-\-cluster\-distance=\fR\fIdistance\fP: Cluster points (as with \fB\fC\-\-cluster\-densest\-as\-needed\fR, but without the experimental discovery process) that are approximately within \fIdistance\fP of each other. The units are tile coordinates within a nominally 256\-pixel tile, so the maximum value of 255 allows only one feature per tile. Values around 10 are probably appropriate for typical marker sizes. See \fB\fC\-\-cluster\-densest\-as\-needed\fR below for behavior.
.RE
.SS Dropping a fraction of features to keep under tile size limits
.RS
@ -266,7 +269,7 @@ compensate for the larger marker, or \fB\fC\-Bf\fR\fInumber\fP to allow at most
.IP \(bu 2
\fB\fC\-pd\fR or \fB\fC\-\-force\-feature\-limit\fR: Dynamically drop some fraction of features from large tiles to keep them under the 500K size limit. It will probably look ugly at the tile boundaries. (This is like \fB\fC\-ad\fR but applies to each tile individually, not to the entire zoom level.) You probably don't want to use this.
.IP \(bu 2
\fB\fC\-aC\fR or \fB\fC\-\-cluster\-densest\-as\-needed\fR: If a tile is too large, try to reduce its size by increasing the minimum spacing between features, and leaving one placeholder feature from each group. The remaining feature will be given a \fB\fC"cluster": true\fR attribute to indicate that it represents a cluster, a \fB\fC"point_count"\fR attribute to indicate the number of features that were clustered into it, and a \fB\fC"sqrt_point_count"\fR attribute to indicate the relative width of a feature to represent the cluster.
\fB\fC\-aC\fR or \fB\fC\-\-cluster\-densest\-as\-needed\fR: If a tile is too large, try to reduce its size by increasing the minimum spacing between features, and leaving one placeholder feature from each group. The remaining feature will be given a \fB\fC"cluster": true\fR attribute to indicate that it represents a cluster, a \fB\fC"point_count"\fR attribute to indicate the number of features that were clustered into it, and a \fB\fC"sqrt_point_count"\fR attribute to indicate the relative width of a feature to represent the cluster. If the features being clustered are points, the representative feature will be located at the average of the original points' locations; otherwise, one of the original features will be left as the representative.
.RE
.SS Dropping tightly overlapping features
.RS
@ -376,7 +379,7 @@ The postfilter receives the features at tile resolution, after simplification, c
.PP
The layer name is provided as part of the \fB\fCtippecanoe\fR element of the feature and must be passed through
to keep the feature in its correct layer. In the case of the prefilter, the \fB\fCtippecanoe\fR element may also
contain \fB\fCindex\fR, \fB\fCsequence\fR, and \fB\fCextent\fR elements, which must be passed through for internal operations like
contain \fB\fCindex\fR, \fB\fCsequence\fR, \fB\fCextent\fR, and \fB\fCdropped\fR, elements, which must be passed through for internal operations like
\fB\fC\-\-drop\-densest\-as\-needed\fR, \fB\fC\-\-drop\-smallest\-as\-needed\fR, and \fB\fC\-\-preserve\-input\-order\fR to work.
.SS Examples:
.RS

126
tests/accumulate/out/-z3_-Ethesum@sum_-Etheproduct@product_-Ethemax@max_-Ethemin@min_-Ethemean@mean_-Etheconcat@concat_-Ethecomma@comma_-r1_-K100.json
View File

@ -12,17 +12,17 @@
}, "features": [
{ "type": "FeatureCollection", "properties": { "zoom": 0, "x": 0, "y": 0 }, "features": [
{ "type": "FeatureCollection", "properties": { "layer": "in", "version": 2, "extent": 4096 }, "features": [
{ "type": "Feature", "properties": { "thesum": 669, "themax": 94, "themin": 3, "theproduct": 1.50635366827988e+21, "themean": 47.785714285714288, "theconcat": "31478286774655129484515737", "thecomma": "3,14,78,28,67,74,65,5,12,94,84,51,57,37", "clustered": true, "point_count": 14, "sqrt_point_count": 3.74 }, "geometry": { "type": "Point", "coordinates": [ -147.392578, 84.786525 ] } }
{ "type": "Feature", "properties": { "thesum": 669, "themax": 94, "themin": 3, "theproduct": 1.50635366827988e+21, "themean": 47.785714285714288, "theconcat": "31478286774655129484515737", "thecomma": "3,14,78,28,67,74,65,5,12,94,84,51,57,37", "clustered": true, "point_count": 14, "sqrt_point_count": 3.74 }, "geometry": { "type": "Point", "coordinates": [ -131.308594, 60.283408 ] } }
,
{ "type": "Feature", "properties": { "thesum": 960, "themax": 99, "themin": 9, "theproduct": 3.663431746147758e+29, "themean": 49.41935483870968, "theconcat": "97539299902113384943547193275237229", "thecomma": "97,53,92,99,90,21,13,38,49,43,54,71,9,32,75,23,72,29", "clustered": true, "point_count": 18, "sqrt_point_count": 4.24 }, "geometry": { "type": "Point", "coordinates": [ -19.160156, 85.013073 ] } }
{ "type": "Feature", "properties": { "thesum": 960, "themax": 99, "themin": 9, "theproduct": 3.663431746147758e+29, "themean": 49.41935483870968, "theconcat": "97539299902113384943547193275237229", "thecomma": "97,53,92,99,90,21,13,38,49,43,54,71,9,32,75,23,72,29", "clustered": true, "point_count": 18, "sqrt_point_count": 4.24 }, "geometry": { "type": "Point", "coordinates": [ -113.115234, -2.986927 ] } }
,
{ "type": "Feature", "properties": { "thesum": 625, "themax": 100, "themin": 19, "theproduct": 256866214501512000, "themean": 52.45, "theconcat": "594210066226395738619", "thecomma": "59,42,100,66,22,63,95,73,86,19", "clustered": true, "point_count": 10, "sqrt_point_count": 3.16 }, "geometry": { "type": "Point", "coordinates": [ -146.953125, -72.893802 ] } }
{ "type": "Feature", "properties": { "thesum": 625, "themax": 100, "themin": 19, "theproduct": 256866214501512000, "themean": 52.45, "theconcat": "594210066226395738619", "thecomma": "59,42,100,66,22,63,95,73,86,19", "clustered": true, "point_count": 10, "sqrt_point_count": 3.16 }, "geometry": { "type": "Point", "coordinates": [ -71.367188, -53.696706 ] } }
,
{ "type": "Feature", "properties": { "thesum": 397, "themax": 91, "themin": 2, "theproduct": 8357260912000, "themean": 51.75, "theconcat": "11216822689255591", "thecomma": "11,2,16,82,26,89,25,55,91", "clustered": true, "point_count": 9, "sqrt_point_count": 3 }, "geometry": { "type": "Point", "coordinates": [ 30.849609, 80.193694 ] } }
{ "type": "Feature", "properties": { "thesum": 397, "themax": 91, "themin": 2, "theproduct": 8357260912000, "themean": 51.75, "theconcat": "11216822689255591", "thecomma": "11,2,16,82,26,89,25,55,91", "clustered": true, "point_count": 9, "sqrt_point_count": 3 }, "geometry": { "type": "Point", "coordinates": [ 48.164062, 66.583217 ] } }
,
{ "type": "Feature", "properties": { "thesum": 793, "themax": 93, "themin": 7, "theproduct": 3.649478722049509e+25, "themean": 51.492063492063497, "theconcat": "3317626981793467648183915808524", "thecomma": "33,17,62,69,81,7,93,46,76,48,18,39,15,80,85,24", "clustered": true, "point_count": 16, "sqrt_point_count": 4 }, "geometry": { "type": "Point", "coordinates": [ 177.363281, 76.393312 ] } }
{ "type": "Feature", "properties": { "thesum": 793, "themax": 93, "themin": 7, "theproduct": 3.649478722049509e+25, "themean": 51.492063492063497, "theconcat": "3317626981793467648183915808524", "thecomma": "33,17,62,69,81,7,93,46,76,48,18,39,15,80,85,24", "clustered": true, "point_count": 16, "sqrt_point_count": 4 }, "geometry": { "type": "Point", "coordinates": [ 71.279297, -22.268764 ] } }
,
{ "type": "Feature", "properties": { "thesum": 821, "themax": 96, "themin": 1, "theproduct": 3.7235827383082715e+25, "themean": 50.40506329113924, "theconcat": "8310357913170648446526136879658", "thecomma": "83,10,35,79,1,31,70,64,8,44,6,52,61,36,87,96,58", "clustered": true, "point_count": 17, "sqrt_point_count": 4.12 }, "geometry": { "type": "Point", "coordinates": [ 43.330078, -79.981891 ] } }
{ "type": "Feature", "properties": { "thesum": 821, "themax": 96, "themin": 1, "theproduct": 3.7235827383082715e+25, "themean": 50.40506329113924, "theconcat": "8310357913170648446526136879658", "thecomma": "83,10,35,79,1,31,70,64,8,44,6,52,61,36,87,96,58", "clustered": true, "point_count": 17, "sqrt_point_count": 4.12 }, "geometry": { "type": "Point", "coordinates": [ 129.990234, -69.472969 ] } }
] }
] }
,
@ -30,27 +30,27 @@
{ "type": "FeatureCollection", "properties": { "layer": "in", "version": 2, "extent": 4096 }, "features": [
{ "type": "Feature", "properties": { "thesum": 65, "themax": 65, "themin": 65, "theproduct": 65, "themean": 65, "theconcat": 65, "thecomma": 65 }, "geometry": { "type": "Point", "coordinates": [ -166.464844, 2.811371 ] } }
,
{ "type": "Feature", "properties": { "thesum": 371, "themax": 75, "themin": 9, "theproduct": 6630617750400, "themean": 46.375, "theconcat": "384943547193275", "thecomma": "38,49,43,54,71,9,32,75", "clustered": true, "point_count": 8, "sqrt_point_count": 2.83 }, "geometry": { "type": "Point", "coordinates": [ -176.484375, -1.142502 ] } }
{ "type": "Feature", "properties": { "thesum": 371, "themax": 75, "themin": 9, "theproduct": 6630617750400, "themean": 46.375, "theconcat": "384943547193275", "thecomma": "38,49,43,54,71,9,32,75", "clustered": true, "point_count": 8, "sqrt_point_count": 2.83 }, "geometry": { "type": "Point", "coordinates": [ -164.838867, -39.639538 ] } }
,
{ "type": "Feature", "properties": { "thesum": 183, "themax": 72, "themin": 23, "theproduct": 2833416, "themean": 48.27272727272727, "theconcat": "23722959", "thecomma": "23,72,29,59", "clustered": true, "point_count": 4, "sqrt_point_count": 2 }, "geometry": { "type": "Point", "coordinates": [ -131.440430, -48.632909 ] } }
{ "type": "Feature", "properties": { "thesum": 183, "themax": 72, "themin": 23, "theproduct": 2833416, "themean": 48.27272727272727, "theconcat": "23722959", "thecomma": "23,72,29,59", "clustered": true, "point_count": 4, "sqrt_point_count": 2 }, "geometry": { "type": "Point", "coordinates": [ -155.039062, -67.424364 ] } }
,
{ "type": "Feature", "properties": { "thesum": 230, "themax": 100, "themin": 22, "theproduct": 6098400, "themean": 51.357142857142857, "theconcat": "421006622", "thecomma": "42,100,66,22", "clustered": true, "point_count": 4, "sqrt_point_count": 2 }, "geometry": { "type": "Point", "coordinates": [ -116.235352, -78.595299 ] } }
{ "type": "Feature", "properties": { "thesum": 230, "themax": 100, "themin": 22, "theproduct": 6098400, "themean": 51.357142857142857, "theconcat": "421006622", "thecomma": "42,100,66,22", "clustered": true, "point_count": 4, "sqrt_point_count": 2 }, "geometry": { "type": "Point", "coordinates": [ -98.041992, -61.396719 ] } }
,
{ "type": "Feature", "properties": { "thesum": 336, "themax": 95, "themin": 19, "theproduct": 713902770, "themean": 55.111111111111117, "theconcat": "6395738619", "thecomma": "63,95,73,86,19", "clustered": true, "point_count": 5, "sqrt_point_count": 2.24 }, "geometry": { "type": "Point", "coordinates": [ -50.756836, -49.037868 ] } }
{ "type": "Feature", "properties": { "thesum": 336, "themax": 95, "themin": 19, "theproduct": 713902770, "themean": 55.111111111111117, "theconcat": "6395738619", "thecomma": "63,95,73,86,19", "clustered": true, "point_count": 5, "sqrt_point_count": 2.24 }, "geometry": { "type": "Point", "coordinates": [ -34.848633, -39.842286 ] } }
] }
] }
,
{ "type": "FeatureCollection", "properties": { "zoom": 1, "x": 0, "y": 0 }, "features": [
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] }
] }
,
@ -422,7 +422,7 @@
,
{ "type": "FeatureCollection", "properties": { "zoom": 3, "x": 4, "y": 2 }, "features": [
{ "type": "FeatureCollection", "properties": { "layer": "in", "version": 2, "extent": 4096 }, "features": [
{ "type": "Feature", "properties": { "thesum": 18, "themax": 16, "themin": 2, "theproduct": 32, "themean": 9, "theconcat": "216", "thecomma": "2,16", "clustered": true, "point_count": 2, "sqrt_point_count": 1.41 }, "geometry": { "type": "Point", "coordinates": [ 14.051514, 46.732331 ] } }
{ "type": "Feature", "properties": { "thesum": 18, "themax": 16, "themin": 2, "theproduct": 32, "themean": 9, "theconcat": "216", "thecomma": "2,16", "clustered": true, "point_count": 2, "sqrt_point_count": 1.41 }, "geometry": { "type": "Point", "coordinates": [ 19.346924, 54.065836 ] } }
,
{ "type": "Feature", "properties": { "thesum": 82, "themax": 82, "themin": 82, "theproduct": 82, "themean": 82, "theconcat": 82, "thecomma": 82 }, "geometry": { "type": "Point", "coordinates": [ 31.959229, 55.596971 ] } }
,
@ -438,7 +438,7 @@
,
{ "type": "FeatureCollection", "properties": { "zoom": 3, "x": 5, "y": 7 }, "features": [
{ "type": "FeatureCollection", "properties": { "layer": "in", "version": 2, "extent": 4096 }, "features": [
{ "type": "Feature", "properties": { "thesum": 114, "themax": 79, "themin": 35, "theproduct": 2765, "themean": 57, "theconcat": "3579", "thecomma": "35,79", "clustered": true, "point_count": 2, "sqrt_point_count": 1.41 }, "geometry": { "type": "Point", "coordinates": [ 57.952881, -82.229086 ] } }
{ "type": "Feature", "properties": { "thesum": 114, "themax": 79, "themin": 35, "theproduct": 2765, "themean": 57, "theconcat": "3579", "thecomma": "35,79", "clustered": true, "point_count": 2, "sqrt_point_count": 1.41 }, "geometry": { "type": "Point", "coordinates": [ 55.667725, -83.244185 ] } }
] }
] }
,
@ -478,7 +478,7 @@
,
{ "type": "Feature", "properties": { "thesum": 62, "themax": 62, "themin": 62, "theproduct": 62, "themean": 62, "theconcat": 62, "thecomma": 62 }, "geometry": { "type": "Point", "coordinates": [ 120.717773, 34.052659 ] } }
,
{ "type": "Feature", "properties": { "thesum": 150, "themax": 81, "themin": 69, "theproduct": 5589, "themean": 75, "theconcat": "6981", "thecomma": "69,81", "clustered": true, "point_count": 2, "sqrt_point_count": 1.41 }, "geometry": { "type": "Point", "coordinates": [ 113.609619, 13.848747 ] } }
{ "type": "Feature", "properties": { "thesum": 150, "themax": 81, "themin": 69, "theproduct": 5589, "themean": 75, "theconcat": "6981", "thecomma": "69,81", "clustered": true, "point_count": 2, "sqrt_point_count": 1.41 }, "geometry": { "type": "Point", "coordinates": [ 119.443359, 16.088042 ] } }
,
{ "type": "Feature", "properties": { "thesum": 7, "themax": 7, "themin": 7, "theproduct": 7, "themean": 7, "theconcat": 7, "thecomma": 7 }, "geometry": { "type": "Point", "coordinates": [ 126.595459, 8.733077 ] } }
] }
@ -500,7 +500,7 @@
,
{ "type": "FeatureCollection", "properties": { "zoom": 3, "x": 7, "y": 6 }, "features": [
{ "type": "FeatureCollection", "properties": { "layer": "in", "version": 2, "extent": 4096 }, "features": [
{ "type": "Feature", "properties": { "thesum": 97, "themax": 61, "themin": 36, "theproduct": 2196, "themean": 48.5, "theconcat": "6136", "thecomma": "61,36", "clustered": true, "point_count": 2, "sqrt_point_count": 1.41 }, "geometry": { "type": "Point", "coordinates": [ 146.239014, -77.413467 ] } }
{ "type": "Feature", "properties": { "thesum": 97, "themax": 61, "themin": 36, "theproduct": 2196, "themean": 48.5, "theconcat": "6136", "thecomma": "61,36", "clustered": true, "point_count": 2, "sqrt_point_count": 1.41 }, "geometry": { "type": "Point", "coordinates": [ 149.490967, -77.911068 ] } }
,
{ "type": "Feature", "properties": { "thesum": 87, "themax": 87, "themin": 87, "theproduct": 87, "themean": 87, "theconcat": 87, "thecomma": 87 }, "geometry": { "type": "Point", "coordinates": [ 165.520020, -69.862328 ] } }
,
@ -522,7 +522,7 @@
,
{ "type": "FeatureCollection", "properties": { "zoom": 3, "x": 7, "y": 4 }, "features": [
{ "type": "FeatureCollection", "properties": { "layer": "in", "version": 2, "extent": 4096 }, "features": [
{ "type": "Feature", "properties": { "thesum": 101, "themax": 70, "themin": 31, "theproduct": 2170, "themean": 50.5, "theconcat": "3170", "thecomma": "31,70", "clustered": true, "point_count": 2, "sqrt_point_count": 1.41 }, "geometry": { "type": "Point", "coordinates": [ 151.424561, -2.043024 ] } }
{ "type": "Feature", "properties": { "thesum": 101, "themax": 70, "themin": 31, "theproduct": 2170, "themean": 50.5, "theconcat": "3170", "thecomma": "31,70", "clustered": true, "point_count": 2, "sqrt_point_count": 1.41 }, "geometry": { "type": "Point", "coordinates": [ 149.227295, -12.007085 ] } }
,
{ "type": "Feature", "properties": { "thesum": 64, "themax": 64, "themin": 64, "theproduct": 64, "themean": 64, "theconcat": 64, "thecomma": 64 }, "geometry": { "type": "Point", "coordinates": [ 159.675293, -17.130292 ] } }
,

1854
tests/muni/out/-Z11_-z13_-O100_--cluster-densest-as-needed.json
View File

File diff suppressed because it is too large Load Diff

5752
tests/muni/out/-r1_-K20.json
View File

File diff suppressed because it is too large Load Diff

3
tile-join.cpp
View File

@ -896,7 +896,8 @@ int main(int argc, char **argv) {
std::string before = std::string(optarg).substr(0, cp - optarg);
std::string after = std::string(cp + 1);
renames.insert(std::pair<std::string, std::string>(before, after));
} break;
break;
}
case 'q':
quiet = true;

72
tile.cpp
View File

@ -439,7 +439,7 @@ void *partial_feature_worker(void *v) {
}
}
(*partials)[i].geoms.clear(); // avoid keeping two copies in memory
(*partials)[i].geoms.clear(); // avoid keeping two copies in memory
signed char t = (*partials)[i].t;
int z = (*partials)[i].z;
int line_detail = (*partials)[i].line_detail;
@ -1501,46 +1501,43 @@ void preserve_attribute(attribute_op op, std::map<std::string, accum_state> &att
p.full_values[i].type = mvt_double;
break;
case op_max:
{
double existing = atof(p.full_values[i].s.c_str());
double maybe = atof(val.s.c_str());
if (maybe > existing) {
p.full_values[i].s = val.s.c_str();
p.full_values[i].type = mvt_double;
}
case op_max: {
double existing = atof(p.full_values[i].s.c_str());
double maybe = atof(val.s.c_str());
if (maybe > existing) {
p.full_values[i].s = val.s.c_str();
p.full_values[i].type = mvt_double;
}
break;
}
case op_min:
{
double existing = atof(p.full_values[i].s.c_str());
double maybe = atof(val.s.c_str());
if (maybe < existing) {
p.full_values[i].s = val.s.c_str();
p.full_values[i].type = mvt_double;
}
case op_min: {
double existing = atof(p.full_values[i].s.c_str());
double maybe = atof(val.s.c_str());
if (maybe < existing) {
p.full_values[i].s = val.s.c_str();
p.full_values[i].type = mvt_double;
}
break;
}
case op_mean:
{
auto state = attribute_accum_state.find(key);
if (state == attribute_accum_state.end()) {
accum_state s;
s.sum = atof(p.full_values[i].s.c_str()) + atof(val.s.c_str());
s.count = 2;
attribute_accum_state.insert(std::pair<std::string, accum_state>(key, s));
case op_mean: {
auto state = attribute_accum_state.find(key);
if (state == attribute_accum_state.end()) {
accum_state s;
s.sum = atof(p.full_values[i].s.c_str()) + atof(val.s.c_str());
s.count = 2;
attribute_accum_state.insert(std::pair<std::string, accum_state>(key, s));
p.full_values[i].s = milo::dtoa_milo(s.sum / s.count);
} else {
state->second.sum += atof(val.s.c_str());
state->second.count += 1;
p.full_values[i].s = milo::dtoa_milo(s.sum / s.count);
} else {
state->second.sum += atof(val.s.c_str());
state->second.count += 1;
p.full_values[i].s = milo::dtoa_milo(state->second.sum / state->second.count);
}
p.full_values[i].s = milo::dtoa_milo(state->second.sum / state->second.count);
}
break;
}
case op_concat:
p.full_values[i].s += val.s;
@ -1758,6 +1755,19 @@ long long write_tile(FILE *geoms, long long *geompos_in, char *metabase, char *s
indices.push_back(sf.index);
if ((sf.index < merge_previndex || sf.index - merge_previndex < mingap) && find_partial(partials, sf, which_partial, layer_unmaps)) {
partials[which_partial].clustered++;
if (partials[which_partial].t == VT_POINT &&
partials[which_partial].geoms.size() == 1 &&
partials[which_partial].geoms[0].size() == 1 &&
sf.geometry.size() == 1) {
double x = (double) partials[which_partial].geoms[0][0].x * partials[which_partial].clustered;
double y = (double) partials[which_partial].geoms[0][0].y * partials[which_partial].clustered;
x += sf.geometry[0].x;
y += sf.geometry[0].y;
partials[which_partial].geoms[0][0].x = x / (partials[which_partial].clustered + 1);
partials[which_partial].geoms[0][0].y = y / (partials[which_partial].clustered + 1);
}
preserve_attributes(arg->attribute_accum, attribute_accum_state, sf, stringpool, pool_off, partials[which_partial]);
continue;
}

2
version.hpp
View File

@ -1,6 +1,6 @@
#ifndef VERSION_HPP
#define VERSION_HPP
#define VERSION "tippecanoe v1.27.8\n"
#define VERSION "tippecanoe v1.27.9\n"
#endif