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====2/8/2017 WEDNESDAY''9AM-11AM''==== 03-29:* Attempted to come up with possible Troubled by the variety of cases for locating the description - separating keys by keywords will not work favorably when it hits University of California vs. University of accelerators Southern California case - pick up from extracting bodies find a way to match University of text from the about page Southern California first (given that it existsmore specific ones first)- but how to generalize
====2/13/2017 MONDAY ''2PM-6PM''====03-27:* Goals (Writing code for trials): 1) Build ER Diagram 2) For university matches - decided to go through keys instead of each dataitem. Use keywords in each entity, get XML snippet 3) Build a parser/ripper key to go through the dataitem - misspellings are currently unaccounted for single file; the python parser can be found at: E:\McNair\Projects\FDA Trials\Jeemin_Project[[Trial Data Project]].
====2/15/2017 WEDNESDAY ''9AM-11AM''====03-24:* Discussed with Catherine what Julia & Meghana about university keys to do with FDA Trial data use to count # of occurrences, including aliases and decided misspellings* Thoughts: to have use a dictionary scoring metric with zip-codes as keys and number a key of trials occurred in that zipcode as values. Was still UNIVERSITY OF CALIFORNIA SYSTEM, it should have a 'better' score when compared to MATHEMATICAL SCIENCES PUBLISHERS C/O UNIVERSITY OF CALIFORNIA BERKELEY or CALIFORNIA AT LOS ANGELES, UNVIERSITY OF than when compared to UNIVERSITY OF SOUTHERN CALIFORNIA, which may pose a challenge when attempting to loop through the files without the code having to exist implement this in a more general sense. In normalizing a string, strip "THE", "," and split words by spaces and compare each keyword from the same directory as the XML filestwo strings. Plan to write Deciding on which strings to excel via tsv, with zipcompare will be another issue -code as one column and # of occurrence as the otherlength (within some range maybe) could be an option.* Federal Grant Data XML Parser was rerun - same output textfiles
====2/17/2017 FRIDAY ''2PM-6PM''====03-22:* Completed code for counting the number of occurrences for each unique zipcode. (currently titled Read string matching & locatedcalculating distance, below are relevant links* [http: E:\McNair\Projects\FDA Trials\Jeemin_Project\Jeemin_Running_File//www.cs.py)cmu. It has been running for 20+min because of the comprehensive XML data filesedu/~wcohen/postscript/ijcai-ws-2003.pdf]* [http://web. Meanwhile started coding to create a dictionary with the keys corresponding to each unique trial ID, mapped to every other information (location, sponsors, phase, drugs archive.org/web/20081224234350/http://www.dcs.etcshef.) (currently titled & located: E:\McNair\Projects\FDA Trials\Jeemin_Project\Jeemin_FDATrial_as_key_data_rippingac.py)uk/~sam/stringmetrics.html]
====2/20/2017 MONDAY ''2PM-403-22:30PM''====* Continued working on Jeemin_FDATrial_as_key_data_ripping.py Talked to Julia about universal matcher, want to find tags and place combine all University of California's to University of those information in a list. California, The other zipcode file did not finish executing after 2+ hours Regents of running it * Converted crunchbase2013 data from mySQL to PostgreSQL, but having trouble with the last table - considering the possibility of splitting the record file into smaller bitscb_relationships, complains about syntax error at or running near some places - but generally all tables exist in database called crunchbase* Federal Grant Data XML Parser was run - the processing on a faster machine.three output textfiles can be found in E:\McNair\Projects\Federal Grant Data\NSF
====2/22/2017 WEDNESDAY ''9AM-1203-16:30PM''====* Further documented [[University Patent Matching]]* Finished Jeemin_FDATrial_as_key_data_ripping.py (E:\McNair\Projects\FDA Trials\Jeemin_Project\Jeemin_FDATrial_as_key_data_ripping.py), which outputs to E:\McNair\Projects\FDA Trials\Jeemin_Project\general_data_ripping_output.txt; TODO: output four different tables & replace the write in the same for-loop as going through each filewriting XML Parser
====2/24/2017 FRIDAY ''2:30PM-603-15:30PM''====* Continued working Todo: write a wikipage on producing multiple tables - first two are done. Was working possible input/output info on locationstring matcher* Wrote part of XML parser, as there are multiple location tags per location.extracted yearly data into E:\McNair\Projects\Federal Grant Data\NSF\NSF Extracted Data (up to year 2010)
====2/27/2017 MONDAY ''2PM-6PM''====03-14:* Finished producing tables from Jeemin_FDATrial_as_key_data_ripping.py* Talked Started pulling academy cases but there are too many cases to Julia worry about LinkedIn data extracting - to be discussed further with Julia & Peter.* Started web crawler for Wikipedia - currently pulls Endowment, Academic staff, students, undergraduates, and postgraduates info found on Rice Wikipedia pagein terms of institution of interest. Can be found A document is located in : E:\McNair\Projects\University Patents\Jeemin_University_wikipedia_crawleracademies_verify_cases.txt* Need Julia/Meghana to look through the hits and see which are relevant & extract pattern from there. * Having trouble outputting txt file without double quotes around every line. * Thinking that one text file should be output for all keywords instead of having one each, to avoid overlap (ex) COLLEGE and UNIVERSITY are both keywords; ALBERT EINSTEIN COLLEGE OF YESHIVA UNIVERSITY will be hit twice if it were counted as two separate instances, one accounting for COLLEGE and the other for UNIVERSITY) - either in the form of if-elseif statements or one big regex check.py
====3/1/2017 WEDNESDAY ''9AM-12PM''====03-13:* Started reFor University Patent Data Matching -running Jeemin_FDATrial_as_key_data_rippingmatched SCHOOL (output: E:\McNair\Projects\University Patents\school_pulled_from_assignee_list_USA) and matched INSTITUTE(output: E:\McNair\Projects\University Patents\institute_pulled_from_assignee_list_USA).py* [[University Patent Matching]] * To be worked on later: Grant XML parsing & general name matcher
====3/3/2017 FRIDAY ''2PM-5PM03-08:* Wrote regex pattern that identifies all "university" matchings - can be found in E:\McNair\Projects\University Patents\university_pulled_from_assignee_list_USA -- is an output file* Talked to Sonia, but didn''====t come to solid conclusion on identifying whether key words associate with city or country by running a python function* Attempted to output Output sql tablesfrom finished run of Jeemin_FDATrial_as_key_data_ripping.py * Ran through assigneelist_USA.txt to see how many different ways UNIVERSITY could be spelled wrong. There were many.* Tried to logic through creating a pattern that could catch all different versions of UNIVERSITY. Discuss further on whether UNIVERSITIES and those that include UNIVERSITIES but include INC in the end should be pulled as relevant information
====3/6/017 MONDAY ''2PM2017-6PM''====03-06:
====3/8/2017 WEDNESDAY ''9AM-12PM''====03-03:* Output Attempted to output sql tables 2017-03-01:* Started re-running Jeemin_FDATrial_as_key_data_ripping.py 2017-02-27:* Finished producing tables from finished run of Jeemin_FDATrial_as_key_data_ripping.py * Ran through assigneelist_USA.txt Talked to Julia about LinkedIn data extracting - to see how many different ways UNIVERSITY could be spelled wrongdiscussed further with Julia & Peter.* Started web crawler for Wikipedia - currently pulls Endowment, Academic staff, students, undergraduates, and postgraduates info found on Rice Wikipedia page. There were manyCan be found in : E:\McNair\Projects\University Patents\Jeemin_University_wikipedia_crawler.py 2017-02-24:* Tried to logic through creating a pattern that could catch all different versions of UNIVERSITYContinued working on producing multiple tables - first two are done. Discuss further Was working on whether UNIVERSITIES and those that include UNIVERSITIES but include INC in the end should be pulled location, as relevant informationthere are multiple location tags per location.
====3/8/2017 WEDNESDAY ''2PM-5PM ''====02-22:* Wrote regex pattern that identifies all "university" matchings - can be found in Finished Jeemin_FDATrial_as_key_data_ripping.py (E:\McNair\Projects\FDA Trials\Jeemin_Project\Jeemin_FDATrial_as_key_data_ripping.py), which outputs to E:\McNair\Projects\University PatentsFDA Trials\Jeemin_Project\university_pulled_from_assignee_list_USA general_data_ripping_output.txt; TODO: output four different tables & replace the write in the same for-- is an output loop as going through each file* Talked to Sonia, but didn't come to solid conclusion on identifying whether key words associate with city or country by running a python function
====3/13/2017 MONDAY ''12PM-2PM''====02-20:* For University Patent Data Matching - matched SCHOOL (output: E:\McNair\Projects\University Patents\school_pulled_from_assignee_list_USA) Continued working on Jeemin_FDATrial_as_key_data_ripping.py to find tags and matched INSTITUTE(output: E:\McNair\Projects\University Patents\institute_pulled_from_assignee_list_USA)place all of those information in a list. * [[University Patent Matching]] * To be worked The other zipcode file did not finish executing after 2+ hours of running it - considering the possibility of splitting the record file into smaller bits, or running the processing on later: Grant XML parsing & general name matchera faster machine.
====3/14/2017 TUESDAY ''12PM-2PM''====02-17:* Started pulling academy cases but there are too many cases to worry about, in terms Completed code for counting the number of institution of interestoccurrences for each unique zipcode. A document is (currently titled & located in : E:\McNair\Projects\University PatentsFDA Trials\Jeemin_Project\academies_verify_casesJeemin_Running_File.txt* Need Julia/Meghana to look through the hits and see which are relevant & extract pattern from there. * Having trouble outputting txt file without double quotes around every linepy). * Thinking that one text file should be output It has been running for all keywords instead 20+min because of having one the comprehensive XML data files. Meanwhile started coding to create a dictionary with the keys corresponding to eachunique trial ID, mapped to avoid overlap every other information (exlocation, sponsors, phase, drugs ...etc.) COLLEGE and UNIVERSITY are both keywords; ALBERT EINSTEIN COLLEGE OF YESHIVA UNIVERSITY will be hit twice if it were counted as two separate instances, one accounting for COLLEGE and the other for UNIVERSITY(currently titled & located: E:\McNair\Projects\FDA Trials\Jeemin_Project\Jeemin_FDATrial_as_key_data_ripping.py) - either in the form of if-elseif statements or one big regex check.
====3/15/2017 WEDNESDAY ''9AM-1PM''====02-15:* Todo: write Discussed with Catherine what to do with FDA Trial data and decided to have a wikipage on possible input/output info on string matcher* Wrote part dictionary with zip-codes as keys and number of trials occurred in that zipcode as values. Was still attempting to loop through the files without the code having to exist in the same directory as the XML parserfiles. Plan to write to excel via tsv, extracted yearly data into E:\McNair\Projects\Federal Grant Data\NSF\NSF Extracted Data (up to year 2010)with zip-code as one column and # of occurrence as the other.
====3/16/2017 THURSDAY ''12PM-2PM''====* Further documented [[University Patent Matching]]02-13:* Finished writing Goals (for trials): 1) Build ER Diagram 2) For each entity, get XML Parsersnippet 3) Build a parser/ripper for single file; the python parser can be found at: E:\McNair\Projects\FDA Trials\Jeemin_Project
====3/20/2017 MONDAY ''2PM-6PM''====02-08:* Talked Attempted to Julia about universal matcher, want to combine all University come up with possible cases for locating the description of California's to University of California, The Regents accelerators - pick up from extracting bodies of* Converted crunchbase2013 data text from mySQL to PostgreSQL, but having trouble with the last table - cb_relationships, complains about syntax error at or near some places - but generally all tables exist in database called crunchbasepage (given that it exists)* Federal Grant Data XML Parser was run - the three output textfiles can be found in E:\McNair\Projects\Federal Grant [[Trial Data\NSFProject]]
====3/22/2017 WEDNESDAY ''9AM-12PM''====02-06:* Read string matching Set up wikiPage & calculating distance, below are relevant links* [http://wwwremote desktop.cs.cmu.edu/~wcohen/postscript/ijcai-ws-2003.pdf]* [http://Started working on python version of webcrawler.archive.org/web/20081224234350/http:/So far it successfully prints out a catchphrase/wwwdescription for one website.dcsTo be worked on.shef.ac.uk/~sam/stringmetricsThe python file can be found in: E:\McNair\Projects\Accelerators\Python WebCrawler\webcrawlerpython.html]py
====3/24/2017 FRIDAY ''2PM-5PM''====
* Discussed with Julia & Meghana about university keys to use to count # of occurrences, including aliases and misspellings
* Thoughts: to use a scoring metric with a key of UNIVERSITY OF CALIFORNIA SYSTEM, it should have a 'better' score when compared to MATHEMATICAL SCIENCES PUBLISHERS C/O UNIVERSITY OF CALIFORNIA BERKELEY than when compared to UNIVERSITY OF SOUTHERN CALIFORNIA, which may pose a challenge when attempting to implement this in a more general sense. In normalizing a string, strip "THE", "," and split words by spaces and compare each keyword from the two strings. Deciding on which strings to compare will be another issue - length (within some range maybe) could be an option.
* Federal Grant Data XML Parser was rerun - same output textfiles
no edit summary
[[Jeemin Sim]] [[Work Logs]] [[Jeemin Sim (WorkLog)|(log page)]] ===Spring 2018===2017-01-11:*Was briefed on [[Urban Start-up Agglomeration and Venture Capital Investment]]*Going forward - running regressions in R in PostgreSQL and with desired layers (instead of levels) ===Fall 2017===<onlyinclude> 2017-12-05:* Uploaded tables with KML files in TIF folder in bulk(E:) drive** Allegheny County** Atlanta** Chicago** Columbus, OH** Dublin, OH** Vermont** Washington, D.C. *Updated documentation for: ** http://www.edegan.com/wiki/TIF_Project#Uploading_TIF_Data_onto_database_.28tigertest.29 * shp2pgsql needs to be installed to upload Shapefiles to PostgreSQL database** applies for Houston, TX and Dallas, TX** DONE 2017-12-04:* To upload KML file into database with specified table name: researcher@McNairDBServ:/bulk/tigertest$ ogr2ogr -f PostgreSQL PG:"dbname=tigertest" chicagotif.kml -nln chicagotif* chicagotif table now resides in tigertest database 2017-11-30:* Displayed map with TIF districts and startups* Location: E:\McNair\Projects\Agglomeration\TIF\McNair_Color_Chicago_TIF_and_startups.png* Added information and steps to ArcMap/ArcGIS documentation page* Create a project page for 'Working with POSTGIS' and add instructions for uploading KML file onto POSTGIS** Command used : *** Logged in as : researcher@McNairDBServ /bulk/tigertest$ ogr2ogr -f PostgreSQL PG:"dbname=tigertest" chicagotif.kml * chicago TIF kml file currently downloaded in tigertest with a table name of Layer0** Figure out how to change layer name while loading kml file** Instructions pulled from : http://wiki.wildsong.biz/index.php/Loading_data_into_PostGIS#Loading_data_from_KMZ_files 2017-11-28:* Created and edited [[ArcMap / ArcGIS Documentation]]* Plotted points for TIFS and Startups in Chicago in one map.** Location: E:\McNair\Projects\Agglomeration\TIF\Jeemin_Chicago_TIF_and_Startups_Attempt1* Used https://mygeodata.cloud/result to convert from KML file to CSV (which was then saved as txt file to be uploaded onto ArcMap)* Text files located in Local Disk (C:) Drive 2017-11-27:*Download Chicago TIF data 2017-11-13:* 2017-11-09: * Notes on data downloaded:** Year 2010-2012 data are based on total population, not 25 yrs or over (case for all other tables)*** Record appears five times total, with same exact column name***For exmaple: 'Total; Estimate; High school graduate (includes equivalency)' appears five times, with different values.* TODO:** Make Projects page for ACS Data***[[American Community Survey (ACS) Data]] 2017-11-07:*Yesterday, narrowed down columns of interest from ACS_S1501_educationattain_2016 table. Id Id2 Geography Total; Estimate; Population 25 years and over Total; Estimate; Population 25 years and over - High school graduate (includes equivalency) Total; Margin of Error; Population 25 years and over - High school graduate (includes equivalency) Total; Margin of Error; Population 25 years and over - High school graduate (includes equivalency) Percent; Margin of Error; Population 25 years and over - High school graduate (includes equivalency) Total; Estimate; Population 25 years and over - Associate's degree Total; Margin of Error; Population 25 years and over - Associate's degree Percent; Estimate; Population 25 years and over - Associate's degree Percent; Margin of Error; Population 25 years and over - Associate's degree Total; Estimate; Population 25 years and over - Bachelor's degree Total; Margin of Error; Population 25 years and over - Bachelor's degree Percent; Estimate; Population 25 years and over - Bachelor's degree Percent; Margin of Error; Population 25 years and over - Bachelor's degree Total; Estimate; Population 25 years and over - Graduate or professional degree Total; Margin of Error; Population 25 years and over - Graduate or professional degree Percent; Estimate; Population 25 years and over - Graduate or professional degree Percent; Margin of Error; Population 25 years and over - Graduate or professional degree Percent; Estimate; Percent high school graduate or higher Percent; Margin of Error; Percent high school graduate or higher Percent; Estimate; Percent bachelor's degree or higher Percent; Margin of Error; Percent bachelor's degree or higher *Complications:**For csv files corresponding to years 2015 & 2016, all of the above columns exist.**For csv files corresponding to years 2005 - 2014, no 'Percent' columns exist*** Instead their 'Total' columns are percentage values**For csv file corresponding to year 2005, columns regarding Graduate or professional degree are labeled differently.**2012 data doesn't correspond to Population 25 years and over. *Temporary Solution:**Since the above problems may be specific to this set of tables, will go through csv files and adjust columns. *Python script location: E:\McNair\Projects\Agglomeration\ACS_Downloaded_Data\pullCertainColumns.py 2017-10-31:* Finished doanloading files from ACS.* Started loading tables into tigertest.* Commands run could be found in E:\McNair\Projects\Agglomeration\ACS_Downloaded_Data\DataLoading_SQL_Commands.txt 2017-10-30:* Downloaded data from ACS, to be continued* File path: E:\McNair\Projects\Agglomeration\ACS_Downloaded_Data* Fields of interest: S1401 SCHOOL ENROLLMENT S1501 EDUCATIONAL ATTAINMENT S2301 EMPLOYMENT STATUS B01003 TOTAL POPULATION B02001 RACE B07201 GEOGRAPHICAL MOBILITY B08303 TRAVEL TIME TO WORK B19013 MEDIAN HOUSEHOLD INCOME B19053 SELF-EMPLOYMENT INCOME IN THE PAST 12 MONTHS FOR HOUSEHOLDS B19083 GINI INDEX OF INCOME INEQUALITY B25003 TENURE B25105 MEDIAN MONTHLY HOUSING COSTS B28011 INTERNET SUBSCRIPTIONS IN HOUSEHOLD G001 GEOGRAPHIC IDENTIFIERS 2017-10-23:* Talked to Ed with Peter & Oliver about upcoming tasks & projects.* Loaded acs_place table 2017 (does not contain population) on tigertest.** SQL commands used: DROP TABLE acs_place; CREATE TABLE acs_place ( USPS varchar(5), GEOID varchar(30), ANSICODE varchar(30), NAME varchar(100), LSAD varchar(30), FUNCSTAT varchar(10), ALAND varchar(30), AWATER varchar(30), ALAND_SQMI varchar(30), AWATER_SQMI varchar(30), INTPTLAT varchar(30), INTPTLONG varchar(30) ); \COPY acs_place FROM '/bulk/2017_Gaz_place_national.txt'; --COPY 29578 * TODO:** Find acs place 2016 data for population** Find larger acs files, ideally at the place level** Provide more documentation on POSTGIS & geocoding 2017-10-16:* Exported maps of points from the Bay Area each year. ** Map used location: E:\McNair\Projects\Agglomeration\HarrisonPeterWorkArcGIS\Jeemin_Bay_Area Points_Every_Year\BayAreaEveryYearMap** Zoom scale: 1:650.000* Location of Bay Area Points png files: E:\McNair\Projects\Agglomeration\HarrisonPeterWorkArcGIS\Jeemin_Bay_Area Points_Every_Year 2017-10-10:*Discoveries/ Struggles regarding.gdb to .shp file conversion:** Esri Production Mapping (costly)*** License needs to be purchasesd: http://www.esri.com/software/arcgis/extensions/production-mapping/pricing** Use ogr2ogr from gdal package*** https://gis.stackexchange.com/questions/14432/migrating-geodatabase-data-into-postgis-without-esri-apps*** Command: ogr2ogr -f "ESRI Shapefile" [Destination of shapefile] [path to gdb file]*** Problem installing gdal 2017-10-09:* TODO'S:** Downloading data onto tigertest*** Road*** Railway*** Coastline*** Instructions: http://www.edegan.com/wiki/PostGIS_Installation#Bulk_Download_TIGER_Shapefiles** Configure census data from American Community Survey (ACS)*** 1) Work out what data is of our interest (confirm ACS)*** 2) Determine appropriate shape file unit: **** census block vs. census block group vs. census track*** 3) Load into tigertest * Done:** Downloaded data from https://www.census.gov/cgi-bin/geo/shapefiles/6index.php tl_2017_us_coastline -- 4209 tl_2017_us_primaryroads -- 11574 tl_2017_us_rails -- 176237** Link found to potentially download ACS data: https://www.census.gov/geo/maps-data/data/tiger-data.html*** But most files on it come with .gdb extension and not .shp 2017 MONDAY -10-03:* Installed PostGIS & is now visible on pgAdmin III * ArcGIS (connect to postgis database):** 1) Open ArcMap** 2) Either open blank or open existing file/project** 3) Click on 'Add Data'2PMbutton with a cross and a yellow diamond (under Selection toolbar)** 4) Go to the top-most directory by pressing on the arrow that points left-then-6PMup (on the left of home button)** 5) Click on 'Database Connections'** 6) Click on 'Add Database Connection' (if Connection to localhost.sde) does not exist already)** 7) Fill in the following fields:*** Database Platform: PostgreSQL*** Instance: localhost*** User name: postgres*** Password: *** Database: tigertest** 8) Press 'OK'** 9) Now you'll have 'Connection to localhost.sde' in your Database Connections** 10) Double click on 'Connection to localhost.sde'** 11) Double click on the table of interest** 12) Click 'Finish'** 13) You'll see information populated on map, as one of the 'Layers'*** Tested with: tigertest.public.copointplacescontains * On running & altering Oliver's script: ** Location: E:\McNair\Projects\OliverLovesCircles\src\python\vc_circles.py** Ed manipulated file names so that underscores would replace dots (St.Louis --> St_Louis)** Takes in instances and sweep times as part of the argument, but not impactful as those variables are hardcoded in the script** Ran vc_circles.py with the following variables with changed values:*** SWEEP_CYCLE_SECONDS =10 (used to be 30)*** NUMBER_INSTANCES =16 (used to be 8)** New output to be found in: E:\McNair\Projects\OliverLovesCircles\out 2017-10-02:* Talked to Harrison & Peter regarding ArcGIS** Currently have points plotted on Houston** Trouble interpreting geometry type, as currently reads in from text file** Documents located in : E:\McNair\Projects\Agglomeration\HarrisonPeterWorkArcGIS* Attempted to install PostGIS spatial extention from PostgreSQL but getting 'spatial database creation failed' error message.** Referenced instructions: *** https://www.gpsfiledepot.com/tutorials/installing-and-setting-up-postgresql-with-postgis/*** http://www.bostongis.com/PrinterFriendly.aspx?content_name== postgis_tut01 2017-09-26:* Created a table that maps a state to the database name.** http://www.edegan.com/wiki/PostGIS_Installation#Translating_Table_names_to_corresponding_States* Set up wikiPage Added more GIS-information (functions, realm & remote desktopoutliers to consider)** http://www.edegan.com/wiki/Urban_Start-up_Agglomeration#GIS_Resources* Visualization in PostGIS or connecting to ArcGIS for visualization (import/export data)* Spatial indexing:** http://revenant.ca/www/postgis/workshop/indexing. html 2017-09-25:* Started working on python version Talked to Ed about GIS, Census data, and going about determining the correctness of reported 'place.' Currently script makes a cross product of each reported place and an existing place, outputting a column of boolean value to indicate whether the reported place's coordinates fell within a place's geometric boundaries. One other way of web crawlergoing about this which we discussed is to first check if the reported place does fall within that place's boundaries. So far If it successfully prints out a catchphraseisn't, we'll go about the cross product method. * To add documentation : ** http://www.edegan.com/wiki/PostGIS_Installation** http://www.edegan.com/wiki/ description Urban_Start-up_Agglomeration * Discussed the need to maintain venture capital database. *Relevant File paths:**E:\McNair\Projects\Agglomeration\TestGIS.sql**Z:\VentureCapitalData\SDCVCData\vcdb2\ProecssingCoLevelSimple.sql**Z:\VentureCapitalData\SDCVCData\vcdb2\CitiesWithGT10Active.txt 2017-09-21:* Functions for Linear Referencing: '''ST_LineInterpolatePoint(geometry A, double measure)''': Returns a point interpolated along a line. '''ST_LineLocatePoint(geometry A, geometry B)''': Returns a float between 0 and 1 representing the location of the closest point on LineString to the given Point. '''ST_Line_Substring(geometry A, double from, double to)''': Return a linestring being a substring of the input one starting and ending at the given fractions of total 2d length. '''ST_Locate_Along_Measure(geometry A, double measure)''': Return a derived geometry collection value with elements that match the specified measure. '''ST_Locate_Between_Measures(geometry A, double from, double to)''': Return a derived geometry collection value with elements that match the specified range of measures inclusively. '''ST_AddMeasure(geometry A, double from, double to)''': Return a derived geometry with measure elements linearly interpolated between the start and end points. If the geometry has no measure dimension, one is added. *3-D Functions: '''ST_3DClosestPoint''' — Returns the 3-dimensional point on g1 that is closest to g2. This is the first point of the 3D shortest line. '''ST_3DDistance''' — For geometry type Returns the 3-dimensional cartesian minimum distance (based on spatial ref) between two geometries in projected units. '''ST_3DDWithin''' — For 3d (z) geometry type Returns true if two geometries 3d distance is within number of units. '''ST_3DDFullyWithin''' — Returns true if all of the 3D geometries are within the specified distance of one websiteanother. '''ST_3DIntersects''' — Returns TRUE if the Geometries “spatially intersect” in 3d - only for points and linestrings '''ST_3DLongestLine''' — Returns the 3-dimensional longest line between two geometries '''ST_3DMaxDistance''' — For geometry type Returns the 3-dimensional cartesian maximum distance (based on spatial ref) between two geometries in projected units. To be worked '''ST_3DShortestLine''' — Returns the 3-dimensional shortest line between two geometries *Relevant PostgreSQL Commands: '''\dt *.*''' Show all tables '''\q''' Exit table *Specifities/ Outliers to consider: New York (decompose) Princeton area (keep Princeton unique) Reston, Virginia (keep) San Diego (include La Jolla) Silicon Valley (all distinct) * Continue reading from: https://postgis.net/docs/postgis_installation.html 2017-09-20:* Attended first intro to GIS course yesterday* Updated above notes onGIS 2017-09-19:* Useful functions for spatial joins: '''sum(expression)''': aggregate to return a sum for a set of records '''count(expression)''': aggregate to return the size of a set of records '''ST_Area(geometry)''' returns the area of the polygons '''ST_AsText(geometry)''' returns WKT text '''ST_Buffer(geometry, distance)''': For geometry: Returns a geometry that represents all points whose distance from this Geometry is less than or equal to distance. Calculations are in the Spatial Reference System of this Geometry. For geography: Uses a planar transform wrapper. '''ST_Contains(geometry A, geometry B)''' returns the true if geometry A contains geometry B '''ST_Distance(geometry A, geometry B)''' returns the minimum distance between geometry A and geometry B '''ST_DWithin(geometry A, geometry B, radius)''' returns the true if geometry A is radius distance or less from geometry B '''ST_GeomFromText(text)''' returns geometry '''ST_Intersection(geometry A, geometry B)''': Returns a geometry that represents the shared portion of geomA and geomB. The python geography implementation does a transform to geometry to do the intersection and then transform back to WGS84 '''ST_Intersects(geometry A, geometry B)''' returns the true if geometry A intersects geometry B '''ST_Length(linestring)''' returns the length of the linestring '''ST_Touches(geometry A, geometry B)''' returns the true if the boundary of geometry A touches geometry B '''ST_Within(geometry A, geometry B)''' returns the true if geometry A is within geometry B geometry_a '''&&''' geometry_b: Returns TRUE if A’s bounding box overlaps B’s. geometry_a '''=''' geometry_b: Returns TRUE if A’s bounding box is the same as B’s. '''ST_SetSRID(geometry, srid)''': Sets the SRID on a geometry to a particular integer value. '''ST_SRID(geometry)''': Returns the spatial reference identifier for the ST_Geometry as defined in spatial_ref_sys table. '''ST_Transform(geometry, srid)''': Returns a new geometry with its coordinates transformed to the SRID referenced by the integer parameter. '''ST_Union()''': Returns a geometry that represents the point set union of the Geometries. '''substring(string [from int] [for int])''': PostgreSQL string function to extract substring matching SQL regular expression. '''ST_Relate(geometry A, geometry B)''': Returns a text string representing the DE9IM relationship between the geometries. '''ST_GeoHash(geometry A)''': Returns a text string representing the GeoHash of the bounds of the object. *Native functions for geogrphy: '''ST_AsText(geography)''' returns text '''ST_GeographyFromText(text)''' returns geography '''ST_AsBinary(geography)''' returns bytea '''ST_GeogFromWKB(bytea)''' returns geography '''ST_AsSVG(geography)''' returns text '''ST_AsGML(geography)''' returns text '''ST_AsKML(geography)''' returns text '''ST_AsGeoJson(geography)''' returns text '''ST_Distance(geography, geography)''' returns double '''ST_DWithin(geography, geography, float8)''' returns boolean '''ST_Area(geography)''' returns double '''ST_Length(geography)''' returns double '''ST_Covers(geography, geography)''' returns boolean '''ST_CoveredBy(geography, geography)''' returns boolean '''ST_Intersects(geography, geography)''' returns boolean '''ST_Buffer(geography, float8)''' returns geography [1] '''ST_Intersection(geography, geography)''' returns geography [1]* Continue reading from: http://workshops.boundlessgeo.com/postgis-intro/geography.html 2017-09-18:* Read documentation on PostGIS and tiger geocoder* Continue reading from: http://workshops.boundlessgeo.com/postgis-intro/joins.html 2017-09-12:* Clarified University Matching output file can be found .* Helped Christy with pdf-reader, capturing keywords inreadable format. 2017-09-11:* Ensured that documentation exists for the projects worked on last semester. </onlyinclude> ===Spring 2017=== 2017-04-17:* To pull accelerators: Wrote simple python regex-based script that ran on organizations data. Code: E:\McNair\Projects\Accelerators\Python WebCrawlerCrunchbase Snapshot\webcrawlerpythonaccelerator keywords.py Matched output (885 mathces) : E:\McNair\Projects\Accelerators\Crunchbase Snapshot\Jeemin_885_accel_matches 2017-04-14:* Loaded Federal Grants Data into database 2017-04-12:* Finishing up cleaning the columns for Federal Grant Data - NIH. The output excel files can be accessed at: E:\McNair\Projects\Federal Grant Data\NIH\Grants Titled: Jeemin_combined_files 1986-2001.csv Jeemin_combined_files 2002-2012.csv Jeemin_combined_files 2013-2015.csv * psql table formula: CREATE TABLE all_grants ( APPLICATION_ID integer, ACTIVITY varchar(3), ADMINISTERING_IC varchar(2), APPLICATION_TYPE varchar(1), ARRA_FUNDED varchar(1), AWARD_NOTICE_DATE date, BUDGET_START date, BUDGET_END date, CFDA_CODE varchar(3), CORE_PROJECT_NUM varchar(11), ED_INST_TYPE varchar(30), FOA_NUMBER varchar(13), FULL_PROJECT_NUM varchar(35), FUNDING_ICs varchar(40), FUNDING_MECHANISM varchar(23), FY smallint, IC_NAME varchar(77), NIH_SPENDING_CATS varchar(295), ORG_CITY varchar(20), ORG_COUNTRY varchar(16), ORG_DEPT varchar(30), ORG_DISTRICT smallint, ORG_DUNS integer, ORG_FIPS varchar(2), ORG_NAME varchar(60), ORG_STATE varchar(2), ORG_ZIPCODE integer, PHR varchar(200), PI_IDS varchar(30), PI_NAMEs varchar(200), PROGRAM_OFFICER_NAME varchar(36), PROJECT_START date, PROJECT_END date, PROJECT_TERMS varchar(200), PROJECT_TITLE varchar(244), SERIAL_NUMBER smallint, STUDY_SECTION varchar(4), STUDY_SECTION_NAME varchar(100), SUBPROJECT_ID smallint, SUFFIX varchar(2), SUPPORT_YEAR smallint, DIRECT_COST_AMT integer, INDIRECT_COST_AMT integer, TOTAL_COST integer, TOTAL_COST_SUB_PROJECT integer ); \COPY all_grants FROM 'Jeemin_combined_files 1986-2001.csv' WITH DELIMITER AS E'\t' HEADER NULL AS ''CSV
* [[Installing python in a database]]
* Added building Python function section to [[Working with PostgreSQL]] at the bottom of the page.
* Talked to Sonia about pulling city, state, zipcode information, hence python was installed in a database. Will work with Sonia on Wednesday afternoon and see how best a regex function could be implemented
[[Category:Work Log]]
