--- name: "v.dissolve" section: "1grass" platform: "linux" package: "grass-doc" version: "8.4.1-1" release: "Debian 13 (Trixie)" platforms: ["linux"] url: "https://new.carta.tech/v.dissolve" --- # v.dissolve(1grass) Dissolves adjacent or overlapping features sharing a common category number or attribute. ## NAME **v.dissolve** - Dissolves adjacent or overlapping features sharing a common category number or attribute. ## KEYWORDS vector, dissolve, area, line ## SYNOPSIS **v.dissolve**\ **v.dissolve --help**\ **v.dissolve** **input**=*name* \[**layer**=*string*\] \[**column**=*name*\] **output**=*name* \[**aggregate\_columns**=*name*\[,*name*,...\]\] \[**aggregate\_methods**=*string*\[,*string*,...\]\] \[**result\_columns**=*name*\[,*name*,...\]\] \[**aggregate\_backend**=*string*\] \[--**overwrite**\] \[--**help**\] \[--**verbose**\] \[--**quiet**\] \[--**ui**\] ### Flags: - **--overwrite** Allow output files to overwrite existing files - **--help** Print usage summary - **--verbose** Verbose module output - **--quiet** Quiet module output - **--ui** Force launching GUI dialog ### Parameters: - **input**=*name* **\[required\]** Name of input vector map\ Or data source for direct OGR access - **layer**=*string* Layer number or name.\ Vector features can have category values in different layers. This number determines which layer to use. When used with direct OGR access this is the layer name.\ Default: *1* - **column**=*name* Name of attribute column used to dissolve features - **output**=*name* **\[required\]** Name for output vector map - **aggregate\_columns**=*name\[,name*,...\] Names of attribute columns to get aggregate statistics for\ One column name or SQL expression per method if result columns are specified - **aggregate\_methods**=*string\[,string*,...\] Aggregate statistics method (e.g., sum)\ Default is all available basic statistics for a given backend (for sql backend: avg, count, max, min, sum) - **result\_columns**=*name\[,name*,...\] New attribute column names for aggregate statistics results\ Defaults to aggregate column name and statistics name and can contain type - **aggregate\_backend**=*string* Backend for attribute aggregation\ Default is sql unless the provided aggregate methods are for univar\ Options: *sql, univar*\ **sql**: Uses SQL attribute database\ **univar**: Uses v.db.univar ## DESCRIPTION The *v.dissolve* module is used to merge adjacent or overlapping features in a vector map that share the same category value. The resulting merged feature(s) retain this category value. *Figure: Areas with the same attribute value (first image) are merged* *into one (second image).* Instead of dissolving features based on the category values, the user can define an integer or string column using the **column** parameter. In that case, features that share the same value in that column are dissolved. Note, the newly created layer does not retain the category (cat) values from the input layer. Note that multiple areas with the same category or the same attribute value that are not adjacent are merged into one entity, which consists of multiple features, i.e., a multipart feature. ### Attribute aggregation The attributes of merged areas can be aggregated using various aggregation methods. The specific methods available depend on the backend used for aggregation. Two aggregate backends (specified with the **aggregate\_backend** parameter) are available, *univar* and *sql*. The backend is determined automatically based on the requested methods. When the function is one of the *SQL* build-in aggregate functions, the *sql* backend is used. Otherwise, the *univar* backend is used. The default behavior is intended for interactive use and testing. For scripting and other automated usage, explicitly specifying the backend with the **aggregate\_backend** parameter is strongly recommended. When choosing, note that the *sql* aggregate backend, regardless of the underlying database, will typically perform significantly better than the *univar* backend. ### Aggregation using univar backend When *univar* is used, the methods available are the ones which *v.db.univar* uses by default, i.e., *n*, *min*, *max*, *range*, *mean*, *mean\_abs*, *variance*, *stddev*, *coef\_var*, and *sum*. ### Aggregation using sql backend When the *sql* backend is used, the methods depend on the SQL database backend used for the attribute table of the input vector. For SQLite, there are at least the following built-in aggregate functions: *count*, *min*, *max*, *avg*, *sum*, and *total*. For PostgreSQL, the list of aggregate functions is much longer and includes, e.g., *count*, *min*, *max*, *avg*, *sum*, *stddev*, and *variance*. ### Defining the aggregation method If only the parameter **aggregate\_columns** is provided, all the following aggregation statistics are calculated: *n*, *min*, *max*, *mean*, and *sum*. If the *univar* backend is specified, all the available methods for the *univar* backend are used. The **aggregate\_methods** parameter can be used to specify which aggregation statistics should be computed. Alternatively, the parameter **aggregate\_columns** can be used to specify the method using SQL syntax. This provides the highest flexibility, and it is suitable for scripting. The SQL statement should specify both the column and the functions applied, e.g.,\ ``` aggregate_columns="sum(cows) / sum(animals)". ``` Note that when the **aggregate\_columns** parameter is used, the *sql* backend should be used. In addition, the **aggregate\_columns** and **aggregate\_methods** cannot be used together. For convenience, certain methods, namely *n*, *count*, *mean*, and *avg*, are automatically converted to the appropriate name for the selected backend. However, for scripting, it is recommended to specify the appropriate method (function) name for the backend, as the conversion is a heuristic that may change in the future. If the **result\_columns** is not provided, each method is applied to each column specified by **aggregate\_columns**. This results in a column for each of the combinations. These result columns have auto-generated names based on the aggregate column and method. For example, setting the following parameters:\ ``` aggregate_columns=A,B aggregate_methods=sum,n ``` results in the following columns: A\_sum, A\_n, B\_sum, B\_n. See the Examples section. If the **result\_column** is provided, each method is applied only once to the matching column in the aggregate column list, and the result will be available under the name of the matching result column. For example, setting the following parameter:\ ``` aggregate_columns=A,B aggregate_methods=sum,max result_column=sum_a, n_b ``` results in the column *sum\_a* with the sum of the values of *A* and the column *n\_b* with the max of *B*. Note that the number of items in **aggregate\_columns**, **aggregate\_methods** (unless omitted), and **result\_column** needs to match, and no combinations are created on the fly. See the Examples section. For scripting, it is recommended to specify all resulting column names, while for interactive use, automatically created combinations are expected to be beneficial, especially for exploratory analysis. The type of the result column is determined based on the method selected. For *n* and *count*, the type is INTEGER and for all other methods, it is DOUBLE. Aggregate methods that produce other types require the type to be specified as part of the **result\_columns**. A type can be provided in **result\_columns** using the SQL syntax `name type`, e.g., `sum_of_values` `double precision`. Type specification is mandatory when SQL syntax is used in **aggregate\_columns** (and **aggregate\_methods** is omitted). ## NOTES GRASS defines a vector area as a composite entity consisting of a set of closed boundaries and a centroid. The centroids must contain a category number (see *v.centroids*), this number is linked to area attributes and database links. Multiple attributes may be linked to a single vector entity through numbered fields referred to as layers. Refer to *v.category* for more details. Merging of areas can also be accomplished using `v.extract -d` which provides some additional options. In fact, *v.dissolve* is simply a front-end to that module. The use of the *column* parameter adds a call to *v.reclass* before. ## EXAMPLES ### Basic use ``` v.dissolve input=undissolved output=dissolved ``` ### Dissolving based on column attributes North Carolina data set:\ ``` g.copy vect=soils_general,mysoils_general v.dissolve mysoils_general output=mysoils_general_families column=GSL_NAME ``` ### Dissolving adjacent SHAPE files to remove tile boundaries If tile boundaries of adjacent maps (e.g. CORINE Landcover SHAPE files) have to be removed, an extra step is required to remove duplicated boundaries:\ ``` # patch tiles after import: v.patch -e `g.list type=vector pat="clc2000_*" separator=","` out=clc2000_patched # remove duplicated tile boundaries: v.clean clc2000_patched out=clc2000_clean tool=snap,break,rmdupl thresh=.01 # dissolve based on column attributes: v.dissolve input=clc2000_clean output=clc2000_final col=CODE_00 ``` ### Attribute aggregation While dissolving, we can aggregate attribute values of the original features. Let’s aggregate area in acres (ACRES) of all municipal boundaries (boundary\_municp) in the full NC dataset while dissolving common boundaries based on the name in the DOTURBAN\_N column (long lines are split with backslash marking continued line as in Bash):\ ``` v.dissolve input=boundary_municp column=DOTURBAN_N output=municipalities \ aggregate_columns=ACRES ``` To inspect the result, we will use *v.db.select* retrieving only one row for `DOTURBAN_N == ’Wadesboro’`:\ ``` v.db.select municipalities where="DOTURBAN_N == ’Wadesboro’" separator=tab ``` The resulting table may look like this:\ ``` cat DOTURBAN_N ACRES_n ACRES_min ACRES_max ACRES_mean ACRES_sum 66 Wadesboro 2 634.987 3935.325 2285.156 4570.312 ``` The above created multiple columns for each of the statistics computed by default. We can limit the number of statistics computed by specifying the method which should be used:\ ``` v.dissolve input=boundary_municp column=DOTURBAN_N output=municipalities_2 \ aggregate_columns=ACRES aggregate_methods=sum ``` The above gives a single column with the sum for all values in the ACRES column for each group of original features which had the same value in the DOTURBAN\_N column and are now dissolved (merged) into one. ### Aggregating multiple attributes Expanding on the previous example, we can compute values for multiple columns at once by adding more columns to the **aggregate\_columns** option. We will compute average of values in the NEW\_PERC\_G column:\ ``` v.dissolve input=boundary_municp column=DOTURBAN_N output=municipalities_3 \ aggregate_columns=ACRES,NEW_PERC_G aggregate_methods=sum,avg ``` By default, all methods specified in the **aggregate\_methods** are applied to all columns, so result of the above is four columns. While this is convenient for getting multiple statistics for similar columns (e.g. averages and standard deviations of multiple population statistics columns), in our case, each column is different and each aggregate method should be applied only to its corresponding column. The *v.dissolve* module will apply each aggregate method only to the corresponding column when column names for the results are specified manually with the **result\_columns** option:\ ``` v.dissolve input=boundary_municp column=DOTURBAN_N output=municipalities_4 \ aggregate_columns=ACRES,NEW_PERC_G aggregate_methods=sum,avg \ result_columns=acres,new_perc_g ``` Now we have full control over what columns are created, but we also need to specify an aggregate method for each column even when the aggregate methods are the same:\ ``` v.dissolve input=boundary_municp column=DOTURBAN_N output=municipalities_5 \ aggregate_columns=ACRES,DOTURBAN_N,TEXT_NAME aggregate_methods=sum,count,count \ result_columns=acres,number_of_parts,named_parts ``` While it is often not necessary to specify aggregate methods or names for interactive exploratory analysis, specifying both **aggregate\_methods** and **result\_columns** manually is a best practice for scripting (unless SQL syntax is used for **aggregate\_columns**, see below). ### Aggregating using SQL syntax The aggregation can be done also using the full SQL syntax and set of aggregate functions available for a given attribute database backend. Here, we will assume the default SQLite database backend for attribute. Modifying the previous example, we will now specify the SQL aggregate function calls explicitly instead of letting *v.dissolve* generate them for us. We will compute sum of the ACRES column using `sum(ACRES)` (alternatively, we could use SQLite specific `total(ACRES)` which returns zero even when all values are NULL). Further, we will count number of aggregated (i.e., dissolved) parts using `count(*)` which counts all rows regardless of NULL values. Then, we will count all unique names of parts as distinguished by the MB\_NAME column using `count(distinct` `MB_NAME)`. Finally, we will collect all these names into a comma-separated list using `group_concat(MB_NAME)`:\ ``` v.dissolve input=boundary_municp column=DOTURBAN_N output=municipalities_6 \ aggregate_columns="total(ACRES),count(*),count(distinct MB_NAME),group_concat(MB_NAME)" \ result_columns="acres REAL,named_parts INTEGER,unique_names INTEGER,names TEXT" ``` Here, *v.dissolve* doesn’t make any assumptions about the resulting column types, so we specified both named and the type of each column. When working with general SQL syntax, *v.dissolve* turns off its checks for number of aggregate and result columns to allow for all SQL syntax to be used for aggregate columns. This allows us to use also functions with multiple parameters, for example specify separator to be used with *group\_concat*:\ ``` v.dissolve input=boundary_municp column=DOTURBAN_N output=municipalities_7 \ aggregate_columns="group_concat(MB_NAME, ’;’)" \ result_columns="names TEXT" ``` To inspect the result, we will use *v.db.select* retrieving only one row for `DOTURBAN_N == ’Wadesboro’`:\ ``` v.db.select municipalities_7 where="DOTURBAN_N == ’Wadesboro’" separator=tab ``` The resulting table may look like this:\ ``` cat DOTURBAN_N names 66 Wadesboro Wadesboro;Lilesville ``` ## SEE ALSO *v.category,* *v.centroids,* *v.extract,* *v.reclass,* *v.db.univar,* *v.db.select* ## AUTHORS M. Hamish Bowman, Department of Marine Science, Otago University, New Zealand (module)\ Markus Neteler (column support)\ Trevor Wiens (help page)\ Vaclav Petras, NC State University, Center for Geospatial Analytics, GeoForAll Lab (aggregate statistics) ## SOURCE CODE Available at: v.dissolve source code (history) Accessed: Monday Feb 24 17:51:45 2025 Main index \| Vector index \| Topics index \| Keywords index \| Graphical index \| Full index © 2003-2025 GRASS Development Team, GRASS GIS 8.4.1 Reference Manual