scripting

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An Overview of Census Datasets and Census API Examples

This month’s post is a bit shorter, as I have just two announcements I wanted to share about some resources I’ve created.

First, I’ve written a short technical paper that’s just been published as part of the Weissman Center of International Business’ Occasional Papers Series. Exploring US Census Datsets: A Summary of Surveys and Sources provides an overview of several different datasets (decennial census, American Community Survey, Population Estimates Program, and County Business Patterns) and sources for accessing data. The paper illustrates basic themes that are part of all my census-related talks: the census isn’t just the thing that happens every ten years but is an ecosystem of datasets updated on an on-going basis, and there are many sources for accessing data which are suitable for different purposes and designed for users with varying levels of technical skill. In some respects this paper is a super-abridged version of my book, designed to serve as an introduction and brief reference.

Second, I’ve created a series of introductory notebooks on GitHub that illustrate how to use the Census Bureau’s API with Python and Jupyter Notebooks. I designed these for a demonstration I gave at NYU’s Love Data Week back on Feb 10 (the slides for the talk are also available in the repo). I structured the talk around three examples. Example A demonstrates the basics of how the API works along with some best practices, such as defining your variables at the top and progressively building links to retrieve data. It also illustrates the utility of using these technologies in concert, as you can pull data into your script and process and visualize it in one go. I also demonstrate how to retrieve lists of census variables and their corresponding metadata, which isn’t something that’s widely documented. Example B is a variation of A, extended by adding an API key and storing data in a file immediately after retrieval. Example C introduces more complexity, reading variables in from files and looping through lists of geographies to make multiple API calls.

Since I’ve written a few posts on the census API recently, I went back and added an api tag to group them together, so you can access them via a single link.

Define census API variables, build links, and retrieve data

County and ZIP Code Business Patterns 2017 and the Census API

The U.S. Census Bureau’s County and ZIP Code Business Patterns (CBP and ZBP) datasets are generated annually from the Business Register, a large administrative database updated by several federal agencies which contains every business establishment in the U.S. with paid employees. Business establishments are defined as single physical locations where business is conducted or where services or industrial operations are performed. Establishments are assigned to industries, which are groups of businesses that produce similar products or provide similar services, using the North American Industrial Classification System (NAICS). The ZBP contains tables with total establishments, employment, and wages by ZIP and counts of business establishments by NAICS and ZIP. The CBP has these tables plus a few others for counties.

The 2017 Business Patterns was recently released, and there are a few important changes to the dataset over previous iterations. I’ll summarize what they are and how they impact data retrieval using the Census Bureau’s ZBP API. I unwittingly discovered these issues this week as I was trying to use a Python / Pandas notebook I’d written for extracting ZBP data and aggregating the USPS ZIP codes to Zip Code Tabulation Areas (ZCTAs), which are used for publishing decennial and ACS census data. Everything went smoothly when I tested the scripts against the 2016 ZBP, but a few things went awry with 2017 and I was forced to make some revisions.

If you’re not familiar with the API, take a look at this earlier post for a basic introduction. The notebooks I’ll refer to are available on my github; zbp_to_zcta.ipynb works for the 2017 ZBP release, and I kept the earlier version that worked for 2016.

2017 NAICS Codes

NAICS codes are revised every five years in tandem with the Economic Census (conducted in years ending in 2 and 7), to effectively capture the changing nature of the economy. The CBP and ZBP employ the latest NAICS series in the year that it’s released, so beginning with 2012 the 2012 NAICS were used for categorizing establishments into industries. The 2012 definitions were used up through 2016, but now that we’re in 2017 we have a new NAICS 2017 series, and this was employed for the 2017 CBP and ZBP and will be used through 2021.

How different are the categories? If you’re working at the broad two-digit sector level nothing has changed. The more detailed the categories are (3 to 6 digit), the more likely it is that you’ll encounter changes: industries that were created, or removed (aggregated into a broader miscellaneous category), or modified. You can use the concordance tables to see how definitions have changed, and in some cases crosswalk data from one category to another.

If you’re using the API, you’ll need to modify your url to access the 2017 NAICS variables (&NAICS2017=) as opposed to the 2012 series (&NAICS2012= ).

New Privacy Regulations

For confidentiality purposes, the Census Bureau has always employed various methods to insure that the summary data produced for the CBP and ZBP can’t be used to identify characteristics of an individual business. If a geographic area or industrial category had fewer than 3 establishments in it, or if one establishment in an area or category constituted an overwhelming majority of the employment or wages, then those values were not disclosed or published. The only characteristic that was always published was the number of establishments.

Not any more – beginning with the 2017 CBP and ZBP, the following applies:

> Prior to reference year 2017, the number of establishments in a particular tabulation cell was not considered sensitive; therefore, counts of establishments were released without any disclosure avoidance methods applied. Beginning with reference year 2017, cells with fewer than 3 establishments have been omitted from the release.

So what does this mean? First, for any county or ZIP Code that has fewer than 3 business establishments in total, records for that county or ZIP Code will not appear in the dataset at all (although establishments in these areas will be counted in summaries of larger areas, like states or metro areas). In my script, about 30 ZIP Codes for NYC fell out of my results compared to last year; these were primarily non-residential ZIPs that represented a single business that processes lots of mail, and post office box ZIPs.

Second, for a given geographic area, if a given NAICS category has less than three business establishments, the number of establishments won’t be reported for that category, but they will be included in the sum total. Once again, in my case I’m working with two-digit sector codes. There is a 00 code that captures the sum of all establishments. When I was summing the values of all of the two-digit codes together, I discovered that these sums rarely matched the 00 total, like they did in the past, because of the new non-disclosure policy. To account for this, and to calculate percent totals correctly, I had to create a category that takes the difference between the total 00 category and the sum of all the others, to count how many businesses were not disclosed (see pic below). I could then treat that category like the others, and the sum of the parts would equal the whole again.

These data frames show counts of establishments by two digit NAICS sectors. In the top df, the totals column N00 does not equal the sum of the others columns. A column was added to the bottom df to get the difference between the two.

Subsequently, I replaced the zeros for any ZIP code that had businesses that weren’t disclosed with NULLs, as I can’t know for certain if the values are truly zero. The most likely categories (at the two digit level for ZIPs) where data was not disclosed were: 11 (agriculture), 21 (mining), 22 (utilities), and 99 (unclassified businesses).

Looping Through and Retrieving Geographies

The API allows you to select all geographies within another geography using the ‘in’ clause (visit the ZBP API to see a list of variables and examples). For example, you can select all the counties in a particular state – in the example below, values would be passed into the variables in braces, and you would pass ANSI FIPS codes into the geography variables:

base_url = f'https://api.census.gov/data/{year}/{dsource}'
edata_url=f'{base_url}?get={ecols}&for={county}:*&in=state:{state}&key={api_key}'

This option is only available for geographies that nest, according to the Census Bureau’s geographic hierarchy. ZIP Codes are not a census geography and don’t nest within anything, so we can’t use the ‘in’ clause. For the 2016 and prior versions of the ZBP API, there was a trick for getting around this; there was a state variable called ST, which you could use in a similar fashion to get all the ZIP Codes in a state in a ‘for’ clause:

edata_url = f'{base_url}?get={ecols}&for=zipcode:*&ST={state}&key={api_key}'

Not any more – the ST variable disappeared in the 2017 API for the ZBP. So what can you do instead? Option one is to loop through a list of ZIP codes, passing them to the API one by one. This is fine if you just need a few, but pretty slow if you need the 260 something that I needed. Option two is to pass in several ZIP codes into the URL at once, but there’s a catch: you’re only allowed to pass in 50 values at a time to any variable. To do this, you need to divide your list of ZIPs into chunks of no more than 50, loop through the sub-lists to insert them into the url, and append the results to a big list as you go along.

A function for breaking a list of ZIP Codes (or any list of variables) into chunks:

def chunks(l, n):
    for i in range(0, len(l), n):
        yield l[i:i+n]

Call the function to generate a list of lists with an equal number of values (in my case, my ZIP Codes are an index in a dataframe):

reqzips=list(chunks(zip2zcta.index.tolist(),48))

Then run the following to iterate through the list of ZIP code lists. I use enumerate so I can grab both the indices and values in the list. The ZIP codes values (v) have to be strung together and separated by commas before passing them into the url. The ecols variable is a list of columns I want to retrieve, which is also a single string with columns separated by commas. Once I receive the first chunk I append everything to a list (emp_data), but for every subsequent chunk I start reading from the second value [1:] and skip the first [0] because I only want to append the column headers once.

emp_data=[]
for i, v in enumerate (reqzips):
    batchzips=','.join(v)
    edata_url = f'{base_url}?get={ecols}&for=zipcode:{batchzips}&key={api_key}'
    response=requests.get(edata_url)
    if response.status_code==200:
        clear_output(wait=True)
        data=response.json()
        if i == 0:
            for record in data:
                emp_data.append(record)
        else:
            for record in data[1:]:
                emp_data.append(record)
        print('Retrieved data for chunk',i)
    else:
        print('***Problem with retrieval***, response code',response.status_code)
        break

The key here is to get the looping right, to insure that you end up with a list of lists where each list represents a row of data, in this case a ZIP code record with establishment data. I employed something similar (but a bit more complicated) with an ACS script that I wrote, but in that case I was looping through lists of columns / attributes instead of geographies.

If you’d like to learn more about the census business datasets and understand how to navigate NAICS, check out chapter 8 in my book. I don’t cover the APIs, but I do demonstrate how to use the new data.census.gov and I delve into the concepts behind these datasets in good detail.

Examples of using the Census Bureau’s API with Python

At the end of my book I briefly illustrate how the Census Bureau’s API works using Python. I’ll expand on that in this post; we’ll pull data from the Population Estimates Program, transform it, and create a chart using Python with Pandas in a Notebook. I’ll conclude with an additional example using the ZIP Code Business Patterns.

The Census Bureau has dedicated API pages for each dataset (decennial, acs, pop estimates, and more), and you need to familiarize yourself with the geographies and variables that are available for each. The API is a basic REST API, where you insert parameters into a base url and retrieve data based on the link you submit. Python has several modules you can use for interacting with APIs – the requests module is a popular choice.

The following pop estimates example is on github (but if github flops see the nbviewer example instead).

The top of the script contains basic stuff – import the modules you need, read in your key, and define the variables that you want to pull. You don’t have to use an API key, but if you don’t you’re limited to pulling in 500 records a day. Requesting a key is simple and free. A best practice is to store your key (a big integer) in a file that you read in, so you’re not exposing it in the script. Most of the census APIs require that you pass in a year and a dataset (dsource). Larger datasets may be divided into subsets (dname); for example the population estimates is divided into estimates, components of change, and characteristics (age, sex, race, etc.). Save the columns and geographies that you want to get in a comma-separated string. You have to consult the documentation and variable lists that are available for each dataset to build these, and the geography requires ANSI / FIPS codes.

%matplotlib inline
import requests,pandas as pd

with open('census_key.txt') as key:
    api_key=key.read().strip()

year='2018'
dsource='pep'
dname='components'
cols='GEONAME,NATURALINC,DOMESTICMIG,INTERNATIONALMIG'
state='42'
county='017,029,045,091,101'

Next, you can create the url. I’ve been doing this in two parts. The first part:

base_url = f'https://api.census.gov/data/{year}/{dsource}/{dname}'

Includes the base https://api.census.gov/data/ followed by parameters that you fill in. The year, data source, and dataset name are the standard pieces. The output looks like this:

'https://api.census.gov/data/2018/pep/components'

Then you take that base_url and add additional parameters that are going to vary within the script, in this case the columns and the geography, which all appear in the ‘get’ portion of the url. The ‘for’ and ‘in’ options allow you to select the type of geography within another geography, in this case counties within states, and you pass in the appropriate ANSI FIPS codes from the string you’ve created. The key appears at the end of the url, but if you opt not to use it you can omit that part. Once the link is fully constructed you use the requests module to fetch the data using that url. You can print the result out as text (assuming it’s not too long).

data_url = f'{base_url}?get={cols}&for=county:{county}&in=state:{state}&key={api_key}'
response=requests.get(data_url)
print(response.text)

The result looks like a nested list, but is actually a string that’s structured in a non-standard JSON format:

[["GEONAME","NATURALINC","DOMESTICMIG","INTERNATIONALMIG","state","county"],
["Bucks County, Pennsylvania","-178","-605","862","42","017"],
["Chester County, Pennsylvania","1829","-887","1374","42","029"],
["Delaware County, Pennsylvania","1374","-2513","1579","42","045"],
["Montgomery County, Pennsylvania","1230","-1987","2315","42","091"],
["Philadelphia County, Pennsylvania","8617","-11796","8904","42","101"]]

To do anything with it, convert it to JSON with response.json(). Then you can convert it into a list, dictionary, or in this example a Pandas dataframe. Here, I build the dataframe with everything from row one forward [1:]; row zero contains the column headers[0]. I rename some of the columns, build a unique ID by concatenating the state and county FIPS codes and set that as the new index, and drop the individual county and state FIPS columns. By default every object that’s returned is a string, so I convert the numeric columns to integers:

data=response.json()
df=pd.DataFrame(data[1:], columns=data[0]).\
    rename(columns={"NATURALINC": "Natural Increase", "DOMESTICMIG": "Net Domestic Mig", "INTERNATIONALMIG":"Net Foreign Mig"})
df['fips']=df.state+df.county
df.set_index('fips',inplace=True)
df.drop(columns=['state','county'],inplace=True)
df=df.astype(dtype={'Natural Increase':'int64','Net Domestic Mig':'int64','Net Foreign Mig':'int64'},inplace=True)
df

Then I can see the result:

Once the data is in good shape, you can begin to analyze and visualize it. Here’s the components of population change for Philadelphia and the surrounding suburban counties in Pennsylvania from 2017 to 2018 – natural increase is the difference between births and deaths, and there’s net migration within the US (domestic) and between the US and other countries (foreign):

labels=df['GEONAME'].str.split(' ',expand=True)[0]
ax=df.plot.bar(rot=0, title='Components of Population Change 2017-18')
ax.set_xticklabels(labels)
ax.set_xlabel('')

Each request is going to vary based on your specific needs and the construction of the particular dataset. Here’s another example where I pull data on business establishments, employees, and wages (in $1,000s of dollars) from the ZIP Code Business Patterns (ZBP). This dataset is smaller, so it doesn’t have a dataset name, just a data source. To get all the ZIP Codes in Delaware I use the asterisk * wildcard. Because ZIP Codes do not nest within states I can’t use the ‘in’ option, it’s simply not available. A state code is stored in a special field called ST, and I can use it as a general limiter with equals in the query:

year='2016'
dsource='zbp'
cols='ESTAB,EMP,PAYQTR1,PAYANN'
state='10'

base_url = f'https://api.census.gov/data/{year}/{dsource}'

data_url = f'{base_url}?get={cols}&for=zipcode:*&ST={state}&key={api_key}'
response=requests.get(data_url)
print(response.text)

[["ESTAB","EMP","PAYQTR1","PAYANN","ST","zipcode"],
["982","26841","448380","1629024","10","19713"],
["22","628","3828","15848","10","19716"],
["8","15","371","2030","10","19732"],
["7","0","0","0","10","19718"],
["738","9824","83844","353310","10","19709"]...

data=response.json()
zbp_data=pd.DataFrame(data[1:], columns=data[0]).set_index('zipcode')
zbp_data.drop(columns=['ST'],inplace=True)
for field in cols.split(','):
    zbp_data=zbp_data.astype(dtype={field:'int64'},inplace=True)
zbp_data.head()

One of the issues with the ZBP is that many variables are not disclosed due to privacy regulations; instead of returning nulls a zero is returned, but in this dataset they are not true zeros. Once you retrieve the data and set the types you can replace zeros with NaNs, which are numpy / Panda nulls – although there’s a quirk in that dataframe columns declared as integers cannot contain null values. Instead you can use a float, or a workaround that’s been implemented for new Pandas versions (for my specific use case this data will be inserted into a database, so I’ll use SQL to accomplish the zero to null conversion). ZBP data is also injected with noise to protect privacy, and you can retrieve special columns that contain noise flags.

The API is convenient for automating the data acquisition process, and allows you to cherry pick the variables you want. To avoid accessing the API over and over again as you build your scripts (which is prohibitive when requesting lots of data) you can pickle the data right after you retrieve it – a pickle is a python data object that efficiently stores data locally, and pandas has special functions for creating and accessing them. Once you pull your data and pickle it, you can comment out (or in a notebook, don’t rerun) the requests block, and subsequently pull the data from the pickle as you tweak your code (see caveat in the postscript – perhaps best to use json instead of pickle).

#Write to a pickle
zbp_data.to_pickle('insert path here.pickle')

#Read from a pickle to dataframe
zbp_new=pd.read_pickle('insert path here.pickle')

Take a look at the Census Data API User Guide to learn more. The guide focuses just on the REST API, and is not specific to a scripting language. Of course, you also need to familiarize yourself with the datasets and how they’re created and organized, and with census geography (which is why I wrote this book).

Postscript

Since I’ve finished this post I’ve created a notebook that pulls ZBP data from the API (alt nbviewer here) and have some additional thoughts I’d like to share:

I decided to dump the data I retrieved from the API to a json file and then pull data from it instead of using a pickle. Pickles come with serious security issues. If you don’t intend to share your code with anyone pickles are fine, otherwise consider an alternative.
My method for parsing the retrieved data into a dataframe worked fine because the census API uses non-standard JSON; essentially the string that’s returned resembles a nested Python list. If this was true JSON, we may need to employ a different method to account for the fact that the number of elements per record may vary.
Wildcards are not always available to build urls for certain data; for example to download the number of establishments classified by industry I wasn’t able to grab everything for one state using the method I illustrated in this post. Instead I had to loop through a list of ZIP and NAICS codes to retrieve what I wanted one at a time.
In the case of retrieving establishments classified by industry there were many cases when there was no data for a particular ZIP Code (i.e. no farms and mines in midtown Manhattan). Since I needed records that showed zero establishments, I had to insert them myself if the API returned no result. Even if you didn’t need records with zeros, it’s important to consider the potential impact of getting nothing back from the API on your subsequent code.
Given my experience thus far these APIs were pretty reliable, in that I haven’t had issues with time outs and partially returned data. If this was not the case and you had lots of data to retrieve, you would need to build in some try – except statements to handle exceptions, save data as you go along, and pick up where you left off if something breaks. Read about this geocoding script I wrote a few years back for examples.

Plotting Library GIS Services with Pandas

With the dawn of a new academic year I usually spend a little time looking back at the previous one. Since I began my position as Geospatial Data Librarian at Baruch College I’ve logged my questions, consultations, course visits, and workshops in a spreadsheet that I’ve used for creating summaries and charts. I spent a good chunk of this summer improving my Pandas skills, and put them to the test by summarizing and plotting my services data in a Jupyter Notebook instead.

Pandas is a data science module for Python that adds so many new components that it’s like a language all by itself. Its big selling point is that it adds a grid-like data structure to Python. In vanilla Python, you typically read data files into a list of lists where the big list represents the file, the individual lists represent rows, and the list elements represent values. There are no columns; to manipulate data you iterate through the sub-lists and elements by their position number. In well-structured datasets, elements in the same position in each sub-list represent attributes that would be stored in the same column.

In contrast, Pandas provides a true row and column structure called a dataframe, where you access each row by its index (a unique id) and columns by name or position. Furthermore, methods and functions that you apply to the data are automatically applied to entire rows and columns, and in some cases even to the entire dataframe, so that looping through data element by element is largely unnecessary. You’re able to treat a dataframe as if it were a spreadsheet or database table, in that you can concatenate dataframes together, merge them on their index numbers, and group records by values.

Using Pandas in concert with a Jupyter Notebook allows for an iterative approach to exploring and manipulating data, and is particularly conducive to creating plots and charts. You can use Python’s tried and true matplotlib module to build your chart bit by bit, or you can use Panda’s own plotting functions, which are wrappers around matplotlib that allow you to quickly create charts with fewer lines of code. Another plotting module called Seaborn offers a third approach.

This cheat sheet has become my indispensable reference for keeping track of the different Pandas functions and methods, and for helping me mentally navigate the different ways of doing things in Pandas versus regular Python. Plotting was a struggle at first, as I tried to figure out when to use Pandas versus matplotlib versus Seaborn. The fact that it’s possible to use all three at once to create the same plot added to my confusion! This visualization flowchart helped me sort things out. For simple stuff, I used the Pandas plot functions, but if the chart required additional customization I used matplotlib to generate the extra pieces, or the whole thing. In essence, use matplotlib for super detailed control over customization, and use Pandas plot functions as shortcuts for writing more concise code.

Preamble

I’ve stored my notebook and the data file on github (still a work in progress) if you’d like to take a closer look (the notebook is the ipynb file). I’m going to address a portion of what’s in the notebook in this post.

First and foremost you need to import pandas and matplotlib’s pyplot. The %matplotlib inline trick tells the notebook to display all charts that you generate with matplotlib; otherwise it just creates them without displaying them. The plt.style.use() lets you apply a global style (chart colors, background, grid lines etc) to all plots in your notebook. This convenient style sheet reference demonstrates what they all look like.

%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-muted')

Web Stats

I’ll start with the simplest example. My spreadsheet doesn’t contain web stats, so I needed to hard code these into the notebook. To create a dataframe you build it column by column, and add the index last. In a notebook you don’t need to use a print function to see the data, you simply enter the name of the object that you want to display:

geoportal=pd.DataFrame({'Page Views' : [29500, 37254, 40421, 33527],
'Unique Views' : [23052, 29285, 31668, 26418],
'Downloads' : [3561, 6807, 6682, 5208]},
index=['2015.16','2016.17','2017.18','2018.19'])
geoportal

The plot was pretty darn simple, using Panda’s DataFrame.plot you specify the type of chart (bar in this case), pass in a few arguments, and voila! Pandas automatically uses the index for the x axis (academic years in this case) and will attempt to plot all columns on the y axis. If this isn’t desirable you can set x and y in the arguments. The default legend placement isn’t ideal in this example, but we’ll see how to change it later. plt.savefig() saves the chart as an image file outside the notebook.

geoportal.plot.bar(rot=0, title='Baruch Geoportal')
plt.savefig('webfig.png')

Questions and Consultations

The rest of my data is stored in an Excel spreadsheet. You can quickly read spreadsheets into a dataframe by specifying the file and sheet, and the head() command previews the top records.

questions = pd.read_excel('RefLog.xlsx', sheet_name='Questions')
questions.head()

I used the groupby method to summarize the number of questions by semester, indicating what column to use for grouping, and how to aggregate. In this example I use .size() which counts all records (another method called count is similar except it does not count null values). Since this result returns just a single column, Pandas returns a data type called a sequence, which is a single-column dataframe with an index (similar to a dictionary key-value pair in vanilla Python). If I want a new dataframe, I can explicitly feed in the columns, reset the index and set it to the year. You can plot data from either type.

#Summarize as a series
questions_sem=questions.groupby(by='Semester').size()

#Summarize as a dataframe
questions_yr=questions[['Year','Question']].groupby(by='Year').size().reset_index(name='Questions').set_index('Year')
questions_yr

As before, the plot is pretty simple, but in this case when saving the figure I specify bounding box tight so the labels don’t get cut off (I rotated them 45 degrees for legibility).

questions_yr.plot.bar(rot=45, legend=None, title="GIS Questions")
plt.savefig('questions.png',bbox_inches='tight')

To create a stacked bar chart that shows the number of questions and the status of the person who asked them, I can create a new dataframe where I group by both year and status. One of the initial challenges in learning how to plot data is figuring out what structure is appropriate. After some experimentation, I figured out that each status needs to be as column in order to plot it. I used the following with the unstack method to pivot the data:

 questions_status2=questions[['Year','Question','Status']]\
.groupby(by=['Year','Status']).size().unstack() questions_status2

questions_status2[['Student','Faculty','Staff','CUNY','Public']]\
.plot.bar(stacked=True, rot=45, title="GIS Questions")
plt.savefig('questions_status.png',bbox_inches='tight')

Explicitly stating the columns isn’t necessary, but it allows you to specify the order in which they appear in the chart. I have another worksheet that lists my consultations, that I read in, transform, and plot using the same statements:

Questions represent emails or phone calls that I’ve received, while consultations are in- person, one-on-one sessions. Both the questions and consultations are specific to demographic, geospatial, or GIS-related topics. Students, faculty, and staff refer to people affiliated with my college (Baruch), while the CUNY category captures affiliates from all the other schools in the university regardless of their status. Public captures anyone outside the university.

The initial patterns are similar: the number of questions was low for my initial three years, and then began to take off in the 2010-11 academic year. This coincided with my movement out of the library’s Information Services Division and into the Graduate Services Division, where I was able to devote more time to providing my specialized services and less time providing general ones (i.e. the reference desk, visiting freshmen English composition classes). 2010-11 was also the year I introduced my day-long introductory GIS workshops which led to an increase in business, particularly from other CUNY campuses.

Another turning point was 2014-15 but the data diverges; the number of questions dips and hasn’t returned to to the peak I hit in 2013-14, while consultations remain consistently high. This is the year that I moved into the GIS Lab, and was able to provide better on-going in-person support. It was also the year I received tenure and promotion, which immediately resulted in a heavy increase in service commitments, i.e. serving on various college committees that took me away from my work (while I have graduate assistants that help with consultations, questions are sent directly to me). 2017-18 is a big divot on both charts as this was the year I was away on sabbatical to write my book (my grad assistant Janine held down the fort at the lab while I monitored questions from home), but there was a solid rebound in 2018-19.

Course Visits and Workshop Stats

I frequently visit public policy, journalism, and other courses to give lectures on census data and GIS, and for these charts I wanted to show the number of classes I visited and attendance on one chart. After loading my teaching data in, I excluded records that represented my GIS workshops by using the query method. Since I wanted to create two different aggregates – a count and a sum – I applied the .agg method after using groupby:

 classes_yr=classes[['Year','Class','Attendance']].groupby('Year').agg({'Class':'count', 'Attendance':'sum'})
classes_yr

As best as I could tell, the Pandas plot function couldn’t handle a line and bar on the same chart with a secondary Y axis, so I used matplotlib instead, building the chart one piece at a time:

plt.figure()

ax = classes_yr['Attendance'].plot(secondary_y=True, marker='o', color='orange')
ax = classes_yr['Class'].plot(kind='bar', title='Course Visits', rot=45)
ax.set_ylabel('Courses')
plt.ylabel('Attendance')

plt.savefig('courses.png',bbox_inches='tight')

The courses I visit are consistently mid-sized with about 20 students a piece, so visits and attendance track pretty closely. The pattern is similar to my questions and consultations, initially low, rising as I gained independence, dropping once I hit tenure and service commitments, then gradually rising until the 2017-18 sabbatical year.

For the GIS workshops (stored in greater detail in a separate worksheet) I wanted to create two charts: a summary of attendance for each year by status, and another showing the schools that participants came from. Since attendance will vary by the number of workshops, I also wanted to incorporate the number of sessions into the first chart. After loading in the data:

and creating a grouped summary:

I created an independent sequence for the labels using string methods:

and I used matplotlib so I could set different tick labels and move the legend, as the default placement blocked portions of the bars:

plt.figure()
ax=gis_yr[['Undergrad','Grad Stdt','Faculty','Staff','Other']].plot.bar\
(stacked=True, rot=25, title="GIS Workshops")

ax.set_xticklabels(gis_label)
ax.set_xlabel('Year (# Sessions)')
ax.set_ylabel('Attendance')
plt.legend(loc='upper center', bbox_to_anchor=(1, 1))

plt.savefig('workshops.png',bbox_inches='tight')

For the workshops, the status includes all CUNY members regardless of school, while Other is anyone not affiliated with CUNY. Graduate students have always comprised the largest share of participants. Once again, there is the tenure dip in 2014-15 (fewer sessions) and no sessions during 2017-18 sabbatical. 2016-17 was an exceptional year as one of our sessions was held at the FOSS4G conference, so there are lots of participants from the Other category. The latest year was disappointing, as bad weather impacted attendance at two of the sessions.

I wanted to create a pie chart to show participation by CUNY school, but to make it aesthetically pleasing I needed to remove schools with few participants and add them to an Other CUNY category. Otherwise there would be tiny wedges with unreadable labels. After creating a subset of the workshops dataframe that summed values only for the school columns, I iterated through the schools to sum attendance to a variable, dropped those schools, and added the sum to the other category (see the notebook for details). I used the Pandas plot function to create the pie chart, and used the autopct argument to display percentages in the wedges. I also specified a figure size, which you can do for any chart (and becomes important when you decide to embed them in documents):

gis_total=gis_schools.sum()

gis_schools.plot.pie(legend=False, figsize=(6,6), \
title='Workshop Participants by School \n ({} Participants in Total)'.format(gis_total), autopct='%i%%')
plt.ylabel("")
plt.savefig('schools.png',bbox_inches='tight')

One-third of participants were from my college, and one-fourth were from the Graduate Center, which is our nearest CUNY neighbor with a large population of master’s and PhD students who are keenly interested in learning GIS. The next biggest contributors are Hunter and Lehman Colleges, which are the two CUNY schools that have geography departments with GIS programs; Hunter is also close to Baruch, and we took a road trip to offer some sessions on Lehman’s campus.

Wrap Up

What I like about this approach is that you can summarize and reconfigure data without messing with the original source, and you can clearly see what your formulas are as they’re not hidden beneath the resulting values. These are both hazards when working directly within spreadsheets. While it takes time to learn these new functions and to grapple with finding work-arounds for exceptions, I don’t think it’s any less difficult than trying to accomplish the same things in a spreadsheet. I’ve always found spreadsheet charting to be rather clumsy, where you’re forced to cycle through numerous windows or to click on minuscule pieces of a chart to access hidden settings that you need. The Pandas / notebook approach makes a lot of sense for iterative data exploration, summation, and visualization, although I’ll continue to rely on regular Python for projects that fall outside this specific domain.

Recent Migration Trends for New York City and Metro

The Baruch GIS lab crew just published a paper: New Yorkers on the Move: Recent Migration Trends for the City and Metro Area. The paper (no. 15 Feb 2018) is part of the Weissman Center for International Business Occasional Paper Series, which focuses on New York City’s role in the international and domestic economy.

Findings

We analyzed recent population trends (2010 to 2016) in New York City and the greater metropolitan area using the US Census Bureau’s Population Estimates to study components of population change (births, deaths, domestic and international migration) and the IRS Statistics of Income division’s county to county migration data to study domestic migration flows.

Here are the main findings:

The population of New York City and the New York Metropolitan Area increased significantly between 2010 and 2016, but annually growth has slowed due to greater domestic out-migration.
Compared to other large US cities and metro areas, New York’s population growth depends heavily on foreign immigration and natural increase (the difference between births and deaths) to offset losses from domestic out-migration.
Between 2011 and 2015 the city had few relationships where it was a net receiver of migrants (receiving more migrants than it sends) from other large counties. The New York metro area had no net-receiver relationships with any major metropolitan area.
The city was a net sender (sending more migrants than it received) to all of its surrounding suburban counties and to a number of large urban counties across the US. The metro area was a net sender to metropolitan areas throughout the country.

For the domestic migration portion of the analysis we were interested in seeing the net flows between places. For example, the NYC metro area sends migrants to and receives migrants from the Miami metro. What is the net balance between the two – who receives more versus who sends more?

The answer is: the NYC metro is a net sender to most of the major metropolitan areas in the country, and has no significant net receiver relationships with any other major metropolitan area. For example, for the period from 2011 to 2015 the NYC metro’s largest net sender relationship was with the Miami metro. About 88,000 people left the NYC metro for metro Miami while 58,000 people moved in the opposite direction, resulting in a net gain of 30,000 people for Miami (or in other words, a net loss of 30k people for NYC). The chart below shows the top twenty metros where the NYC metro had a deficit in migration (sending more migrants to these areas than it received). A map of net out-migration from the NYC metro to other metros appears at the top of this post. In contrast, NYC’s largest net receiver relationship (where the NYC metro received more migrants than it sent) was with Ithaca, New York, which lost a mere 300 people to the NYC metro.

All of our summary data is available here.

Process

For the IRS data we used the county to county migration SQLite database that Janine meticulously constructed over the course of the last year, which is freely available on the Baruch Geoportal. Anastasia employed her Python and Pandas wizardry to create Jupyter notebooks that we used for doing our analysis and generating our charts, all of which are available on github. I used an alternate approach with Python and the SQLite and prettytable modules to generate estimates independently of Anastasia, so we could compare the two and verify our numbers (we were aggregating migration flows across years and geographies from several tables, and calculating net flows between places).

One of our goals for this project was to use modern tools and avoid the clunky use of email. With the Jupyter notebooks, git and github for storing and syncing our work, and ShareLaTeX for writing the paper, we avoided using email for constantly exchanging revised versions of scripts and papers. Ultimately I had to use latex2rtf to convert the paper to a word processing format that the publisher could use. This post helped me figure out which bibliography packages to choose (in order for latex2rtf to interpret citations and references, you need to use the older natbib & bibtex combo and not biblatex & biber).

If you are doing similar research, Zillow has an excellent post that dicusses the merits of the different datasets. There are also good case studies on Washington DC and Philadelphia that employ the same datasets.

Python Geocoding Take 2 – US Addresses

Python Geocoding Take 1 – International Addresses I discussed my recent adventures with geocoding addresses outside the US. In contrast, there are countless options for batch geocoding addresses within the United States. I’ll discuss a few of those options here, but will focus primarily on the US Census Geocoder and a Python script I’ve written to batch match addresses using their API. The code and documentation is available on my lab’s resources page.

A Few Different Options

ESRI’s geocoding services allow you (with an account) to access their geocoding servers through tools in the ArcToolbox, or you can write a script and access them through an API. QGIS has a third-party plugin for accessing Google’s services (2500 records a day free) or the Open Streetmap. You can still do things the old fashioned way, by downloading geocoded street files and creating a matching service.

Alternatively, you can subscribe to any number of commercial or academic services where you can upload a file, do the matching, and download results. For years I’ve used the geocoding services at Texas A&M that allow you to do just that. Their rates are reasonable, or if you’re an academic institution and partner with them (place some links to their service on their website) you can request free credits for doing matches in batches.

The Census Geocoder and API, and a Python Script for Batch Geocoding

The Census Bureau’s TIGER and address files are often used as the foundational layers for building these other services, to which the service providers add refinements and improvements. You can access the Census Bureau’s services directly through the Census Geocoder, where you can match an address one at a time, or you can upload a batch of 1000 records. It returns longitude and latitude coordinates in NAD 83, and you can get names and codes for all the census geographies where the address is located. The service is pretty picky about the structure of the upload file (must be plain text, csv, with an id column and then columns with the address components in a specific order – with no other attributes allowed) but the nice thing is it requires no login and no key. It’s also public domain, so you can do whatever you want with the data you’ve retrieved. A tutorial for using it is available on our lab’s census tutorials page.

They also have an API with some basic documentation. You can match parsed and unparsed addresses, and can even do reverse geocoding. So I took a stab at writing a script to batch process addresses in text-delimited files (csv or txt). Unfortnately, the Census Geocoding API is not one of the services covered by the Python Geocoder that I mentioned in my previous post, but I did find another third party module called censusgeocode which provides a thin wrapper you can use. I incorporated that module into my Python 3 script, which I wrote as a function that takes the following inputs:
census_geocode(datafile,delim,header,start,addcol) (str,str,str,int,list[int]) -> files

datafile – this is the name of the file you want to process (file name and extension). If you place the geocode_census_funct.py file in the same directory as your data file, then you just need to provide the name of the file. Otherwise, you need to provide the full path to the file.
delim – this is the delimiter or character that separates the values in your data file. Common delimiters includes commas ‘,’, tabs ‘t’, and pipes ‘|’.
header – here you specify whether your file has a header row, i.e. column names. Enter ‘y’ or ‘yes’ if it does, ‘n’ or ‘no’ if it doesn’t.
start – type 0 to specify that you want to start reading the file from the beginning. If you were previously running the script and it broke and exited for some reason, it provides an index number where it stopped reading; if that’s the case you can provide that index number here, to pick up where you left off.
addcol – provide a list that indicates the position number of the columns that contain the address components in your data file. For an unparsed address, you provide just one position number. For a parsed address, you provide 4 positions: address, city, state, and ZIP code. Whether you provide 1 or 4, the numbers must be supplied in brackets, as the function requires a Python list.

You can open the script in IDLE, run it to load it into memory, and then type the function with the necessary parameters in the shell to execute it. Some examples:

A tab-delimited, unparsed address file with a header that’s stored in the same folder as the script. Start from the beginning and the address is in the 2nd column: census_geocode('my_addresses.txt','t','y',0,[2])
A comma-delimited, parsed address file with no header that’s stored in the same folder as the script. Start from the beginning and the addresses are in the 2nd through 5th columns: census_geocode('addresses_to_match.csv',',','n',0,[2,3,4,5])
A comma-delimited, unparsed address file with a header that’s not in the same folder as the script. We ran the file before and it stopped at index 250, so restart there – the address is in the 3rd column: census_geocode('C:address_datadata1.csv',',','y',250,[3])

The beginning of the script “sets the table”: we read the address columns into variables, create the output files (one for matches, one for non-matches, and a summary report), and we handle whether or not there’s a header row. For reading the file I used Python’s CSV module. Typically I don’t use this module, as I find it’s much simpler to do the basic: read a line in, split it on a delimiter, strip whitespace, read it into a list, etc. But in this case the CSV module allows you to handle a wider array of input files; if the input data was a csv and there happened to be commas embedded in the values themselves, the CSV module easily takes care of it; if you ignore it, the parsing would get thrown off for that record.

Handling Exceptions and Server Errors

In terms of expanding my skills, the new things I had to learn were exception handling and control flows. Since the censusgeocoding module is a thin wrapper, it had no built in mechanism for retrying a match a certain number of times if the server timed out. This is an absolute necessity, because the census server often times out, is busy, or just hiccups, returning a generic error message. I had already learned how to handle crashes in my earlier geocoding experiments, where I would write the script to match and write a record one by one as it went along. It would try to do a match, but if any error was raised, it would exit that loop cleanly, write a report, and all would be saved and you could pick up where you left off. But in this case, if that server non-response error was returned I didn’t want to give up – I wanted to keep trying.

So on the outside there is a loop to try and do a match, unless any error happens, then exit the loop cleanly and wrap up. But inside there is another try loop, where we try to do a match but if we get that specific server error, continue: go back to the top of that for loop and try again. That loop begins with While True – if we successfully get to the end, then we start with the next record. If we get that server error we stay in that While loop and keep trying until we get a match, or we run out of tries (5) and write as a non-match.

In doing an actual match, the script does a parsed or unparsed match based on user input. But there was another sticking point; in some instances the API would return a matched result (we got coordinates!), but some of the objects that it returned were actually errors because of some java problem (failed to get the tract number or county name – here’s an error message instead!) To handle this, we have a for i in range loop. If we have a matched record and we don’t have a status message (that indicates an error) then we move along and grab all the info we need – the coordinates, and all the census geography where that coordinate falls, and write it out, and then that for loop ends with a break. But if we receive an error message we continue – go back to the top of that loop and try doing the match again. After 3 tries we give up and write no match.

Figuring all that out took a while – where do these loops go and what goes in them, how do I make sure that I retry a record rather than passing over it to the next one, etc. Stack Exchange to the rescue! Difference between continue, pass and break, returning to the beginning of a loop, breaking out of a nested loop, and retrying after an exception. The rest is pretty straightforward. Once the matching is done we close the files, and write out a little report that tells us how many matches we got versus fails. The Census Geocoder via the API is pretty unforgiving; it either finds a match, or it doesn’t. There is no match score or partial matching, and it doesn’t give you a ZIP Code or municipal centroid if it can’t find the address. It’s all or nothing; if you have partial or messy addresses or PO Boxes, it’s pretty much guaranteed that you won’t get matches.

There’s no limit on number of matches, but I’ve built in a number of pauses so I’m not hammering the server too hard – one second after each match, 5 seconds after every 1000 matches, a couple seconds before retrying after an error. Your mileage will vary, but the other day I did about 2500 matches in just under 2 hours. Their server can be balky at times – in some cases I’ve encountered only a couple problems for every 100 records, but on other occasions there were hang-ups on every other record. For diagnostic purposes the script prints every 100th record to the screen, as well as any problems it encountered (see pic below). If you launch a process and notice the server is hanging on every other record and repeatedly failing to get matches, it’s probably best to bail out and come back later. Recently, I’ve noticed fewer problems during off-peak times: evenings and weekends.

script_running

Wrap Up

The script and the documentation are posted on our labs resources page, for all to see and use – you just have to install the third party censusgeocode module before using it. When would you want to use this? Well, if you need something that’s free, this is a good choice. If you have batches in the 10ks to do, this would be a good solution. If you’re in the 100ks, it could be a feasible solution – one of my colleagues has confirmed that he’s used the script to match about 40k addresses, so the service is up to the task for doing larger jobs.

If you have less than a couple thousand records, you might as well use their website and upload files directly. If you’re pushing a million or more – well, you’ll probably want to set up something locally. PostGIS has a TIGER module that lets you do desktop matching if you need to go into the millions, or you simply have a lot to do on a consistent basis. The excellent book PostGIS in Action has a chapter dedicated to to this.

In some cases, large cities or counties may offer their own geocoding services, and if you know you’re just going to be doing matches for your local area those sources will probably have greater accuracy, if they’re adding value with local knowledge. For example, my results with NYC’s geocoding API for addresses in the five boroughs are better than the Census Bureau’s and is customized for local quirks; for example, I can pass in a borough name instead of a postal city and ZIP Code, and it’s able to handle those funky addresses in Queens that have dashes and similar names for multiple streets (35th st, 35th ave, 35th dr…). But for a free, public domain service that requires no registration, no keys, covers the entire country, and is the foundation for just about every US geocoding platform out there, the Census Geocoder is hard to beat.

At These Coordinates

Dispatches from the Geospatial Data World