• SR_spatial tweets

    • RT @dcvanriper: There it is. The official announcement that Census will cut short efforts to obtain a full and accurate count of the popula… 12 hours ago
    • RT @tribelaw: “The Trump administration is doing everything it can to sabotage the 2020 Census so it reflects an inaccurate and less divers… 13 hours ago
    • RT @vanitaguptaCR: NEW: Sharing my ⁦⁦@washingtonpost⁩ oped on how the Trump administration is once again trying to sabotage the census to… 13 hours ago

Who Represents You in NYC

UPDATE

NY1 – New York City’s 24-hour cable news channel – featured the maps in a segment they aired during the Thursday, June 20 segment of the “Road to City Hall”. We’ve posted a link to the video below:

NY1 Road to City Hall 6/20/13 segment on Who Represents Me

The Graduate Center also posted a news release about the project.

JUNE 10, 2013

Today our Center for Urban Research at the Graduate Center / CUNY joined with the League of Women Voters to launch an online service so anyone can identify their elected officials in New York City.

The idea behind this “Who Represents Me” service is not new (in fact, my old team at NYPIRG’s Community Mapping Assistance Project pioneered it more than a decade ago).  But now that redistricting has changed all the legislative boundaries in the city (and the City Council lines will all be new by January 2014) it seemed like the perfect time for a reprise of our Who Represents Me service from 2000, updated with new data and new technology.

WRM_mainscreen2

How it works

Anyone can enter a street address at the “Who Represents Me” website, or if they’re using a mobile device they can tap the Use My Current Location link. The site displays a list of all city, state, and federal elected representatives (as well as NYC Community Board), an interactive map of the district and all districts nearby, contact information for local offices, and links for more information such as email addresses, individual websites, Twitter feeds, and Facebook pages of elected officials.

Users can also link to candidate information using the League’s VOTE411.org interactive voter guide. And we provide district-specific links to DecideNYC.com’s candidate summaries.

According to Mary Lou Urban, Co-President of the League of Women Voters of the City of New York,

resources like MyGovNYC.org are what it takes to make participation in government appealingly simple and is a logical approach to increasing voter participation.

New data

We believe our Who Represents Me service will be even more popular and helpful than it was over a decade ago.

First, the League of Women Voters is providing up-to-date info for all elected officials across the city.  The League keeps this information current through ongoing contact with all officials at all levels of government.  Initially the League collected this data for its 2013 They Represent You brochure (which you can order here).  And they’ll be providing new info periodically for the online service.

We supplemented the League’s info with data from Sunlight Foundation, the Open States project, and local websites with contact information and photographs of City Council members, state legislators, congressional representatives, and executive branch officials.

WRM_councilexample

New features

One of the best features of the service is that Who Represents Me can be embedded in anyone’s website, blog, etc. So all the advocacy groups, elected officials, media outlets, and others who use the service can widely share it and make it their own.

Anyone can use the service, Tweet about it, post it to Facebook, and/or create and share a location-specific link to the list of representatives.  Just click the “LINK / EMBED” option at the top of the page and the link like the one below will automatically display the list of officials for that location:

http://mygovnyc.org/?levelofgovt=city&latlng=40.748724%2C-73.98420499999997

District maps

We used a combination of cartoDB, Google Maps API, and the Twitter Bootstrap framework to add a flexible and helpful interactive map overlay to the service.  Just click a thumbnail map of any district, and a new window is displayed that shows all the district boundaries for that location.  Hover over the list of districts and each one is highlighted on the map.  Double-click on a district in the list, and the map zooms to its extent.

WRM_mapexample

Most important, you can click anywhere on the map and new districts are highlighted for that location.  And the list of representatives is automatically updated when you close the map window.

So the maps — combined with the address search and current location feature — enable you to determine elected representatives literally for any and every location in the city.

Credits

“Who Represents Me: NYC” has been developed with the generous support of the New York Community Trust.

Geographic data sources for the service include:

The geographic data representing district boundaries is hosted at cartoDB. The overall site design relies on the Twitter Bootstrap framework. We use the Google Maps API for address matching, “typeahead” address search, and basemaps.

Interactive “Comparinator” maps launched for NYC Council districting

UPDATE Sept 7, 2012

The City Council Comparinator site is now embeddable for your website, blog, etc.

Here’s how to use it:

  • go to the site,
  • zoom to a district and click to highlight it (or enter a street address),
  • choose one of the tabs above the map (Side-by-Side vs Overlay, for example), and
  • pick which proposal you’re comparing with (Districting Commission or Unity Plan).
  • Then click “Link” in the upper right and you’ll see the embed code as well as the basic linking code.

Sample embed code:

We also added a feature: if you turn off the popup window before clicking “Link”, it’ll add a “popup=false” property to the URL, so the person viewing the link (or the starting image for your embedded map) won’t have the popup in the way but the district will still be highlighted.

Here’s an example:


ORIGINAL POST Sept 5, 2012:

NYC Council Districting and You

How existing City Council districts compare with proposed lines

councilcomparinatorscreenshot.PNG

Our Center for Urban Research (CUR) at the CUNY Graduate Center has launched an interactive map today to visualize proposed New York City Council districts compared with existing ones along with the demographic characteristics and patterns within the districts.

The Center hopes the map will help involve people in the NYC districting process simply by showing them how proposed or newly drawn lines looked in relation to their homes or workplaces. Our map is not for drawing districts; others such as the NYC Districting Commission are providing that service.   But CUR’s comparison maps are designed to be be engaging enough to visualize the impact of redistricting for everyone from local citizens to redistricting professionals, hopefully inspiring people to participate more actively in the process.

CUR’s map was designed and is being maintained independently from the NYC Districting Commission’s website. However, we hope that people who use CUR’s maps will then access the Districting Commission’s website for drawing maps online.

Using the map

The main features of the map are as follows:

  • Enter your address to find out what district currently represents you, and which proposed district you’d live in.
  • If you’re using the “Side-by-Side” view, the current districts are displayed on the left, and the proposed districts on the right.
  • If you’re using the “Overlay” view, you can move the transparency slider to the right to display proposed districts, or to the left to fade back to current districts.
  • Click anywhere on the map to highlight the current and proposed districts.
  • When you enter an address or click on the map, an info window pops up listing the current and proposed districts. You can click the link for the current district to go to that Councilmember’s website.
  • Click the “Link” in the upper right of the page to get a direct link to the area of the map you’re viewing. (This one zooms in on City Council District 8 in Manhattan.) You can share this on Twitter or Facebook, email it to friends and colleagues, or blog about it and include the link.

Credits

The mapping application was developed by the Center for Urban Research. David Burgoon, CUR’s application architect, constructed and designed the site, with data analysis support and overall conception from CUR’s Mapping Service director Steven Romalewski.

The application relies on geographic data hosting by cartoDB, open source mapping frameworks and services including OpenLayers and Bing maps, and ESRI’s ArcGIS software for cartography and data analysis.

Data sources

Current City Council district boundaries and proposed maps from the NYC Districting Commission are based on block assignment lists provided at the Districting Commission’s website.

Other proposed maps such as the Unity Map are provided by the advocacy organizations who developed those proposals.

Mapping NYC stop and frisks: some cartographic observations

WNYC’s map of stop and frisk data last week got a lot of attention by other media outlets, bloggers, and of course the Twittersphere.  (The social media editor at Fast Company even said it was “easily one of 2012’s most important visualizations“.)

I looked at the map with a critical eye, and it seemed like a good opportunity to highlight some issues with spatial data analysis, cartographic techniques, and map interpretation – hence this post. New York’s stop and frisk program is such a high profile and charged issue: maps could be helpful in illuminating the controversy, or they could further confuse things if not done right. In my view the WNYC map falls into the latter category, and I offer some critical perspectives below.

TL; DR

It’s a long post 🙂 . Here’s the summary:

  • WNYC’s map seems to show an inverse relationship between stop and frisks and gun recovery, and you can infer that perhaps the program is working (it’s acting as a deterrent to guns) or it’s not (as WNYC argues, “police aren’t finding guns where they’re looking the hardest”). But as a map, I don’t think it holds up well, and with a closer look at the data and a reworking of the map, the spatial patterns of gun recovery and stop and frisks appear to overlap.
  • That said, the data on gun recovery is so slim that it’s hard to develop a map that reveals meaningful relationships. Other visualizations make the point much better; the map just risks obscuring the issue. When we’re dealing with such an important — and controversial — issue, obscuring things is not what we want. Clarity is paramount.
  • I also make some other points about cartographic techniques (diverging vs. sequential color schemes, black light poster graphics vs. more traditional map displays). And I note that there’s so much more to the stop and frisk data that simply overlaying gun recovery locations compared with annual counts of stop and frisks seems like it will miss all sorts of interesting, and perhaps revealing, patterns.

As far as the map itself, here’s a visual summary comparing the WNYC map with other approaches.  I show three maps below (each one zoomed in on parts of Manhattan and the Bronx with stop and frisk hot spots):

  • the first reproduces WNYC’s map, with its arbitrary and narrow depiction of “hot spots” (I explain why I think it’s arbitrary and narrow later in the post);

WNYC map

  • the second map uses WNYC’s colors but the shading reflects the underlying data patterns (it uses a threshold that represents 10% of the city’s Census blocks and 70% of the stop and frisks); and

Modified hot spots (10% blocks representing 70% stop and frisks)

  • the third uses a density grid technique that ignores artificial Census block boundaries and highlights the general areas with concentrated stop and frisk activity, overlain with gun recoveries to show that the spatial patterns are similar.

Density grid


What WNYC’s map seems to show

The article accompanying the map says:

We located all the “hot spots” where stop and frisks are concentrated in the city, and found that most guns were recovered on people outside those hot spots—meaning police aren’t finding guns where they’re looking the hardest.

The map uses a fluorescent color scheme to show the pattern, by Census block, of the number of stop and frisk incidents in 2011 compared with point locations mapped in fluorescent green to show the number of stop and frisks that resulted in gun recovery.

The map is striking, no question. And at first glance it appears to support the article’s point that guns are being recovered in different locations from the “hot spots” of stop, question, and frisk incidents.

But let’s dig a bit deeper.

Do the data justify a map?

This is a situation where I don’t think I would’ve made a map in the first place. The overall point – that the number of guns recovered by stop and frisks in New York is infinitesimally small compared to the number of stop and frisk incidents, putting the whole program into question – is important. But precisely because the number of gun recovery incidents is so small (less than 800 in 2011 vs. more than 685,000 stop and frisks), it makes it unlikely that we’ll see a meaningful spatial pattern, especially at the very local level (in this case, Census blocks which form the basis of WNYC’s map).

And the point about extremely low levels of gun recovery compared with the overwhelming number of stop and frisk incidents has already been presented effectively with bar charts and simple numeric comparisons, or even infographics like this one from NYCLU’s latest report:

If we made a map, how would we represent the data?

For the point of this blog post, though, let’s assume the data is worth mapping.

WNYC’s map uses the choropleth technique (color shading varies in intensity corresponding to the intensity of the underlying data), and they use an “equal interval” approach to classify the data. They determined the number of stop and frisk incidents by Census block and assigned colors to the map by dividing the number of stop and frisks per block into equal categories: 1 to 100, 100 to 200, 200 to 300, and 400 and above.

(Later in this post I comment on the color pattern itself – diverging, rather than sequential – and also about the fluorescent colors on a black background.)

Although they don’t define “hot spot,” it appears that a hot spot on WNYC’s map is any block with more than either 200, 300, or 400 stop and frisks (the pink-to-hotpink blocks on their map).  If we take the middle value (300 stop and frisks per block), then the article’s conclusion that “most guns were recovered on people outside those hot spots” is correct:

  • there are a mere 260 Census blocks with a stop and frisk count above 300, and in these blocks there were only 81 stop and frisk incidents during which guns were recovered;
  • this accounts for only 10% of the 779 stop and frisks that resulted in gun recoveries in that year.

But you could argue that not only is the WNYC definition of a “hot spot” arbitrary, but it’s very narrow. Their “hot spot” blocks accounted for about 129,000 stop and frisks, or only 19% of the incidents that had location coordinates (665,377 stop and frisks in 2011). These blocks also represent less than 1% (just 0.66%) of the 39,148 Census blocks in the city, so these are extreme hot spots.

The underlying data do not show any obvious reason to use 300 (or 200 or 400) as the threshold for a hot spot – there’s no “natural break” in the data at 300 stop and frisks per block, for example, and choosing the top “0.66%” of blocks rather than just 1%, or 5%, or 10% of blocks doesn’t seem to fit any statistical rationale or spatial pattern.

If we think of hot spots as areas (not individual Census blocks) where most of the stop and frisk activity is taking place, while also being relatively concentrated geographically, a different picture emerges and WNYC’s conclusion doesn’t hold up.

[A note on my methodology: In order to replicate WNYC’s map and data analysis, I used the stop and frisk data directly from the NYPD, and used ArcGIS to create a shapefile of incidents based on the geographic coordinates in the NYPD file. I joined this with the Census Bureau’s shapefile of 2010 Census blocks. I determined the number of stop and frisks that resulted in gun recovery slightly different than WNYC: they only included stop and frisks that recovered a pistol, rifle, or machine gun. But the NYPD data also includes a variable for the recovery of an assault weapon; I included that in my totals.]

Choropleth maps: it’s all in the thresholds

Creating a meaningful choropleth map involves a balancing act of choosing thresholds, or range breaks, that follow breaks in the data and also reveal interesting spatial patterns (geographic concentration, dispersion, etc) while being easy to comprehend by your map readers.

If we look at the frequency distribution of stop and frisks in 2011 by Census block, we start to see the following data pattern (the excerpt below is the first 40 or so rows of the full spreadsheet, which is available here: sqf_2011_byblock_freq):

Click the image for a high-resolution version.

The frequency distribution shows that most blocks on a citywide basis have very few stop and frisks:

  • Almost a third have no incidents.
  • 70% of blocks have less than 9 incidents each while the remaining 30% of blocks account for almost 610,000 incidents (92%).
  • 80% of blocks have less than 17 stop and frisks each, while the remaining 20% account for 560,000 incidents (almost 85%).
  • 90% of the blocks have 38 or fewer incidents, while the remaining 10% account for 460,000 incidents (just under 70% of all stop and frisks).

It’s a very concentrated distribution. And it’s concentrated geographically as well. The following maps use WNYC’s color scheme, modified so that there’s one blue color band for the blocks with the smallest number of stop and frisks, and then pink-to-hot pink for the relatively few blocks with the greatest number of stop and frisks. The maps below vary based on the threshold values identified in the spreadsheet above:

30% of blocks are “hot”, accounting for 92% of stop and frisks

20% of blocks are “hot”, accounting for 84% of stop and frisks

10% of blocks are “hot”, accounting for 70% of stop and frisks

In the choropleth balancing act, I would say that a threshold of 9 or 17 stop and frisks per block is low, and results in too many blocks color-coded as “hot”. A threshold of 38 reveals the geographic concentrations, follows a natural break in the data, and uses an easily understood construct: 10% of the blocks accounting for 70% of the stop and frisks.

We could take this a step further and use the threshold corresponding to the top 5% of blocks, and it would look like the following — here’s an excerpt from the spreadsheet that identifies the number of stop and frisks per block that we would use for the range break (74):

Click the image for a high-resolution version.

And here’s the resulting map:

But this goes perhaps too far – the top 5% of blocks only account for half of the stop and frisks, and the geographic “footprint” of the highlighted blocks become too isolated – they lose some of the area around the bright pink blocks that represent areas of heightened stop and frisk activity. (Although even the 74 stop and frisks per block threshold is better than the arbitrary value of 300 in WNYC’s map.)

The two maps below compare WNYC’s map with this modified approach that uses 38 stop and frisks per block as the “hot spot” threshold (for map readability purposes I rounded up to 40). The maps are zoomed in on two areas of the city with substantial concentrations of stop and frisk activity – upper Manhattan and what would loosely be called the “South Bronx”:

WNYC map

Modified thresholds: 1-40, 41-100, 101-400, 400+

To me, the second map is more meaningful:

  • it’s based on a methodology that follows the data;
  • visually, it shows that the green dots are located generally within the pink-to-hot pink areas, which I think is probably more in line with how the Police Department views its policing techniques — they certainly focus on specific locations, but community policing is undertaken on an area-wide basis; and
  • quantitatively the second map reveals that most gun recoveries in 2011 were in Census blocks where most of the stop and frisks took place (the opposite of WNYC’s conclusion). The pink-to-hot pink blocks in the second map account for 433 recovered guns, or 56% of the total in 2011.

The following two maps show this overlap on a citywide basis, and zoomed in on the Brooklyn-Queens border:

Modified thresholds, citywide, with gun recovery incidents

Modified thresholds, along Brooklyn-Queens border, with gun recovery incidents

I’m not defending the NYPD’s use of stop and frisks; I’m simply noting that a change in the way a map is constructed (and in this case, changed to more closely reflect the underlying data patterns) can substantially alter the conclusion you would make based on the spatial relationships.

Hot spot rasters: removing artificial boundaries

If I wanted to compare the stop and frisk incidents to population density, then I’d use Census blocks. But that’s not necessarily relevant here (stop and frisks may have more to do with where people shop, work, or recreate than where they live).

It might be more appropriate to aggregate and map the number of stop and frisks by neighborhood (if your theory is to understand the neighborhood dynamics that may relate to this policing technique), or perhaps by Community Board (if there are land use planning issues at stake), or by Police Precinct (since that’s how the NYPD organizes their activities).

But each of these approaches runs into the problem of artificial boundaries constraining the analysis. If we are going to aggregate stop and frisks up to a geographic unit such as blocks, we need to know a few things that aren’t apparent in the data or the NYPD’s data dictionaries:

  • Were the stop and frisks organized geographically by Census block in the first place, or were they conducted along a street (which might be straddled by two Census blocks) or perhaps within a given neighborhood in a circular pattern over time around a specific location in the hopes of targeting suspects believed to be concealing weapons, that resulted in a single gun recovery preceded by many area-wide stop and frisks? In other words, I’m concerned that it’s arbitrary to argue that a gun recovery has to be located within a Census block to be related to only the stop and frisks within that same block.
  • Also, we need to know more about the NYPD’s geocoding process. For example, how were stop and frisks at street intersections assigned latitude/longitude coordinates? If the intersection is a common node for four Census blocks, were the stop and frisks allocated to one of those blocks, or dispersed among all four? If the non-gun recovery stop and frisks were assigned to one block but the gun recovery stop and frisk was assigned to an immediately adjacent block, is the gun recovery unrelated to the other incidents?

As I’ve noted above, the meager number of gun recoveries makes it challenging to develop meaningful spatial theories. But if I were mapping this data, I’d probably use a hot spot technique that ignored Census geography and followed the overall contours of the stop and frisk patterns.

A hot spot is really more than individual Census blocks with the highest stop and frisk incidents. It also makes sense to look at the Census blocks that are adjacent to, and perhaps nearby, the individual blocks with the most stop and frisks. That’s typically what a hot spot analysis is all about, as one of the commenters at the WNYC article pointed out (Brian Abelson). He referred to census tracts instead of blocks, but he noted that:

A census tract is a highly arbitrary and non-uniform boundary which has no administrative significance. If we are truly interested in where stops occur the most, we would not like those locations to be a product of an oddly shaped census tract (this is especially a problem because census tracts are drawn along major streets where stops tend to happen). So a hot spot is only a hot spot when the surrounding census tracts are also hot, or at least “warm.”

Census block boundaries are less arbitrary than tracts, but the principle applies to blocks as well. A hot spot covers an area not constrained by artificial administrative boundaries. The National Institute of Justice notes that “hot spot” maps often use a density grid to reveal a more organic view of concentrated activity:

Density maps, for example, show where crimes occur without dividing a map into regions or blocks; areas with high concentrations of crime stand out.

If we create a density grid and plot the general areas where a concentration of stop and frisks has taken place, using the “natural breaks” algorithm to determine category thresholds (modified slightly to add categories in the lower values to better filter out areas with low levels of incidence), we get a map that looks like this:

There were so many stop and frisks in 2011 that the density numbers are high. And of course, the density grid is an interpolation of the specific locations – so it shows a continuous surface instead of discrete points (in effect, predicting where stop and frisks would take place given the other incidents in the vicinity). But it highlights the areas where stop and frisk activity was the most prevalent – the hot spots – regardless of Census geography or any other boundaries.

Plotting the individual gun recovery locations against these hot spots produces the following map:

The spatial pattern of gun recoveries generally matches the hot spots.

Nonetheless, even this density map perhaps is too generalized. There are additional analyses we can do on the stop and frisk data that might result in a more precise mapping of the hot spots – techniques such as natural neighbor, kriging, and others; controlling the density surface by introducing boundaries between one concentration of incidents and others (such as highways, parks, etc); and filtering the stop and frisk data using other variables in the data set (more on that below). Lots of resources available online and off to explore. And many spatial analysts that are much more expert at these techniques than me.

Other map concerns

I replicated WNYC’s diverging color scheme for my modified maps above. But diverging isn’t really appropriate for data that go from low number of stop and frisks per Census block to high. A sequential color pattern is probably better, though I think that would’ve made it harder to use the fluorescent colors chosen by WNYC (a completely pink-to-hot pink map may have been overwhelming). As ColorBrewer notes, a diverging color scheme:

puts equal emphasis on mid-range critical values and extremes at both ends of the data range. The critical class or break in the middle of the legend is emphasized with light colors and low and high extremes are emphasized with dark colors that have contrasting hues.

With this data, there’s no need for a “critical break” in the middle, and the low and high values don’t need emphasis, just the high. The following example map offers an easier to read visualization of the patterns than the fluorescent colors, where the low value areas fade into the background and the high value “hot spots” are much more prominent:

This map might be a bit boring compared to the WNYC version 🙂 but to me it’s more analytically useful. I know that recently the terrific team at MapBox put together some maps using fluorescent colors on a black background that were highly praised on Twitter and in the blogs. To me, they look neat, but they’re less useful as maps. The WNYC fluorescent colors were jarring, and the hot pink plus dark blue on the black background made the map hard to read if you’re trying to find out where things are. It’s a powerful visual statement, but I don’t think it adds any explanatory value.

Other data considerations

The stop and frisk databases from NYPD include an incredible amount of information. All sorts of characteristics of each stop and frisk are included, the time each one took place, the date, etc. And the data go back to 2003. If you’d like to develop an in-depth analysis of the data – spatially, temporally – you’ve got a lot to work with. So I think a quick and not very thorough mapping of gun recovery compared with number of stop and frisks doesn’t really do justice to what’s possible with the information. I’m sure others are trying to mine the data for all sorts of patterns. I look forward to seeing the spatial relationships.

The takeaway

No question that a massive number of stop and frisks have been taking place in the last few years with very few resulting in gun recovery. But simply mapping the two data sets without accounting for underlying data patterns, temporal trends, and actual hot spots rather than artificial block boundaries risks jumping to conclusions that may be unwarranted. When you’re dealing with an issue as serious as individual civil rights and public safety, a simplified approach may not be enough.

The WNYC map leverages a recent fad in online maps: fluorescent colors on a black background. It’s quite striking, perhaps even pretty (and I’m sure it helped draw lots of eyeballs to WNYC’s website). I think experimenting with colors and visual displays is good. But in this case I think it clouds the picture.

Long Island special districts mapped online for the 1st time

In partnership with the Long Island Index, our team at the CUNY Graduate Center has mapped the complex and complicated special taxation districts and service provider areas throughout Nassau County.  We’ve updated this information at the Index’s mapping site, along with a new street address search feature.

I couldn’t say it any better than the Index’s news release on the project, so I’ve reproduced that verbatim below.


Nassau County’s Special Districts and Service Providers Mapped for the First Time

The Long Island Index, in collaboration with the Center for Urban Research at the CUNY Graduate Center, launched a new tool on its website that for the first time provides public access to maps representing the profusion of special districts that exist within Nassau County’s villages and towns. Visitors to the site are now able to search by street address or village to view any or all of the 240 fire, sanitation, water, library, parks, parking, police, school and sewer districts – as well as areas where local, county, or state government provides these services – and see clearly who provides what services and where. This new tool is the result of a comprehensive project to delineate all service provider boundaries using computer-mapping software, which integrates data on special districts from multiple sources. The maps are intended to give taxpayers and service providers a common and consistent basis for discussing special district issues.

map

The Long Island Index’s new mapping tool allows visitors to view any or all of the 240 fire, sanitation, water, library, parks, parking, police, school and sewer service providers in Nassau County.

Want to know how many different entities provide water services in Nassau County and their exact boundaries? Well, now you can see them. Want to know who provides water services in your community? Want to know how they are organized, which are special districts, which are town services? You can find that too. With the click of the mouse you can find the contact information and election data for all the service providers for your property. “This is the kind of tool we were looking for when we first started studying how services are provided on Long Island,” said Ann Golob, Director of the Long Island Index. “It didn’t exist so we took on the effort and have worked for over two years to collect, analyze and digitize this information. I think it will be a tremendous resource for the region.”

Also available on the site is a report by the Center for Governmental Research (CGR) in Rochester that explains the historical context surrounding the founding of special districts on Long Island along with the issues associated with so many providers. The CGR report, and the data found on the maps (provider names, URL, contact information, election data) can be downloaded from the either the Index Web site or the interactive maps site.

Steven Romalewski, director of the CUNY Mapping Service at the Center for Urban Research, said: “The maps bring a new level of information which will be a valuable resource for anyone trying to understand how special districts affect them. To create the service provider maps, we used raw data from the Nassau County assessor’s office, worked with the Index to validate the information independently, and reviewed additional data such as printed maps, historical metes and bounds, and some boundary maps in computer format from Nassau County. The online navigation is quick and easy with dynamic tools such as transparencies and map layers that combine seamlessly with the existing demographic, land use, and transportation data on the site.”

The site also features a detailed glossary of terms to help people understand the complex nature of different special districts across the county. For example, within the 54 library districts in Nassau County, there are four types: an Association Library, a School District Public Library, a Special District Public Library, and a Public Village Library. The glossary explains how each of these were established, how they are funded, bonding authority, if employees are subject to civil service law, and to what extent the community can be involved.

According to Long Island Association President Kevin Law, “These maps give Long Islanders a fantastic new tool for understanding the complexities that exist within our numerous special districts. It is really the first time that we can see who has what and where. It provides an opportunity to think out of the box about consolidation, which has the potential to improve efficiencies and stabilize taxes. People are only going to be supportive of this kind of initiative if they understand what is going on and these maps clearly show how multiple layers of government are a challenge for Long Island.”

“Civic organizations trying to research special districts in their own communities now have a powerful resource that never existed before,” said Nancy Douzinas, President of the Rauch Foundation and Publisher of the Long Island Index. “These maps will undoubtedly be of significant assistance to community groups, government agencies, private businesses and anyone else interested in Long Island’s communities.” In addition to the support from the Rauch Foundation, the Hagedorn Foundation helped support the initial mapping work leading to this project.

The Long Island Index plans to incorporate Suffolk County’s special districts in the coming months. The Long Island Index special district mapping feature is accessible at www.longislandindexmaps.org.

NYS Congressional districts & eligible voters mapped

Just three months after the US District Court approved the redistricted Congressional districts for New York, the state is holding primary elections for Republican and Democratic candidates for Congress and US Senate.

In an effort to help analysts understand the voting patterns for the primary and general elections for Congress in New York, our Center for Urban Research at the CUNY Graduate Center has updated a map of Congressional districts to highlight the differences between eligible voters by district and overall population counts.  The map is accessible at www.urbanresearchmaps.org/nycongress2012/map.html

Among other things, our new map displays the “citizen voting age population” (CVAP) estimates for each district as well as overall population counts.  When you visit the map, move your mouse over each district to display total population counts by race/ethnicity along with “citizen voting age population” (CVAP) estimates.   We also have a brief analysis of CVAP estimates compared with total population for each district.

In many Congressional districts, especially in New York City, news reports have noted the changing demographics, partly due to population shifts but also due to new boundaries that are the result of redistricting.  Districts that may once have had a predominantly Black population, for example, may now have a more mixed population.

But overall race and ethnicity population counts only tell part of the story.  Population data from the decennial 2010 Census include all residents — citizens as well as recent immigrants who may not yet be citizens, and people who are of voting age (18 or older) as well as children.  In some cases, the eligible voting population has a much different racial and ethnic profile than the overall population.

Coastal storm impact risk mapped on Long Island

UPDATED (Sunday, Oct. 28, 2012 11am)

In anticipation of Hurricane Sandy, you can use the Long Island Index mapping site to view the areas that would be at greatest risk from the storm.

You can also use Nassau County‘s or Suffolk County‘s websites for more information.


ORIGINAL POST (August 2011)

The latest tracking information for Hurricane Irene (as of Friday morning, 8/26) shows that the storm is likely going to pass east of New York City and make a head on collision with Long Island.  Newsday is reporting that it will hit western Suffolk County’s south shore on Saturday with “tropical-storm-force winds” and then ramp up to 110 mph winds by Sunday.  Yikes!

To help prepare for the storm, our team at the CUNY Graduate Center in collaboration with the Long Island Index has updated the Index’s mapping website with areas at greatest risk of hurricane-level storms.

Here’s the map:

This news release [PDF] provides more information.  The yellow-to-red shaded areas are “coastal storm impact zones.”  In 2005, the New York State Office of Emergency Management developed a map of areas that would be at greatest risk of hurricane impacts based on wind speed and other factors.

The shorthand for the map is a “SLOSH” map, because the zones of impact are based on NOAA’s “Sea, Lake and Overland Surge from Hurricanes” (SLOSH) model projections of vertical surge heights associated with category 1 – 4 storms.  In order to map this information at the Index site, we downloaded the SLOSH data from the NYS GIS Clearinghouse website (metadata here) and used the following color shading to represent the zones:

  • A Category 1 storm (impact areas shown on the map in yellow) means winds of 74-95 mph.
  • A Category 2 storm (impact areas shown on the map in light orange) means winds of 96-110 mph.
  • A Category 3 storm (impact areas shown on the map in dark orange) means winds of 111-130 mph.
  • A Category 4 storm (impact areas shown on the map in red) means winds of 131 mph or more.
If you’re in an area highlighted on the map, be sure to contact your local officials and follow media reports about the hurricane’s progress. Nassau County’s Office of Emergency Management has posted a map of evacuation routes if it comes to that:
Hopefully Irene passes us by, but if not: be prepared.  Better safe than sorry.

Coastal storm impact risk mapped in NYC

UPDATED (Monday, Oct. 29, 2012 1pm)

If you need to locate any of NYC’s 76 hurricane evacuation shelters, you can use the OASIS mapping site.

You can also use NYC.gov to find out the latest with Hurricane Sandy.


UPDATED (8/25/11 9am): We’ve added a temporary map layer on OASIS showing the locations of NYC’s hurricane evacuation centers. Here’s the link: http://bit.ly/oBsUY8 . It’s easy to use:

  • Hover your mouse over each one to highlight it (the site details will also be highlighted in the panel on the right).
  • Click on a map marker to bring the site details up to the top of the list.
  • Double-click on a site in the list and the map will zoom in right to that location.

You can type in your address above the map to see if you’re in an area that’s at risk of storm impacts, and how close you are to an evacuation center. The OASIS map automatically also shows any nearby subway stations. And you can add any other layers from the Legend list to the right of the map.

For more information about Hurricane Irene and what you should do to prepare, visit NYC’s website.

UPDATED (8/24/11 12 noon): We’ve expanded the map layer showing hurricane impact zones throughout the downstate region. It now shows potential impact zones on Long Island’s south shore, as well as some areas along the coast of the Long Island Sound.

ORIGINAL POST

For the past several years the www.OASISnyc.net mapping site has displayed a map of “coastal storm impact zones” in New York City (in addition to the wealth of other mapped data included with OASIS). This coming weekend, it seems like the coastal storm map may be especially useful with city officials bracing for the risk of Hurricane Irene bringing 72 mph winds or more to the NYC region by Sunday.

In 2005, the New York State Office of Emergency Management developed a map of areas that would be at greatest risk of hurricane impacts based on wind speed and other factors. The shorthand for the map is a “SLOSH” map, because the zones of impact are based on NOAA’s “Sea, Lake and Overland Surge from Hurricanes” (SLOSH) model projections of vertical surge heights associated with category 1 – 4 storms.

In 2006, we downloaded the SLOSH data from the NYS GIS Clearinghouse website (metadata here), and used the following color shading to represent the zones:

  • A Category 1 storm (impact areas shown on the map in yellow) means winds of 74-95 mph.
  • A Category 2 storm (impact areas shown on the map in light orange) means winds of 96-110 mph.
  • A Category 3 storm (impact areas shown on the map in dark orange) means winds of 111-130 mph.
  • A Category 4 storm (impact areas shown on the map in red) means winds of 131 mph or more.

Here’s the map:

Through the OASIS website, you can easily enter your address and find out if you’re in an area that might be at greatest risk of the hurricane, depending on how severe the winds are by the time Irene moves up the coast. But the real power of OASIS’s maps is that you can do much more. For example, you can:

  • customize the OASIS map to show transit routes, schools, public housing, and libraries in or near the zones;
  • use OASIS’s mapping tools to see areas near the zones that have been recently developed (using the 1996-2010 aerial image timeline tool);
  • zoom in to see individual property boundaries and click on each one to determine ownership, zoning, and land use characteristics; and
  • find out which elected officials represent the area, as well as the local Community Board (good sources for planning and safety resources).

In the images below, I’ve used the aerial timeline slider and the dynamic transparency tool to show recent housing development in an area at risk of impact from a Category 1 storm, along South Beach on Staten Island (at the OASIS website, you can click and drag the name of any map layer in the Legend to the “Transparency Control” box at the bottom of the legend and make it more or less transparent, so the layers underneath can shine through):

Sparse housing near the beach (1996)

Dense housing development (2010)

The zones mapped on OASIS closely mirror the city’s hurricane evacuation zones (mapped here [PDF] and described here). The city also provides a Hurricane Evacuation Zone Finder where you can enter an address and get useful safety tips depending on your zone.

The message: hopefully Irene passes us by, but if not: be prepared. Better safe than sorry.

Innovative map comparisons – Census change in 15 cities

Our team at the Center for Urban Research (at the CUNY Graduate Center) has updated our interactive maps showing race/ethnicity patterns from 2000 and 2010 in major cities across the US. We’ve enhanced the maps in several ways:

  1. Added more cities. We now have 15 major urban regions mapped across the US (Atlanta, Baltimore, Boston, Charlotte, Chicago, Detroit, Houston, Los Angeles, Miami, New York, Orlando, Philadelphia, Phoenix, San Francisco, and Washington D.C.).
  2. The maps now have three ways of comparing 2000 and 2010 racial patterns:
  3. We color-coded the population change data in the popup window. Population increase is shown in green; decrease is shown in red. See image below.

Here’s our news release with more info.

Btw, we’ve also updated our static maps to show New York City Council districts, to begin to get a sense of how demographic changes will shape upcoming redistricting efforts at the local level.  Here’s the link:www.urbanresearchmaps.org/plurality/nyccouncil.htm (For the static maps, you can view 2000-2010 demographic change with the vertical slider bar, but you can’t zoom in/out, etc.)

An initial version of the maps launched in June with the vertical bar technique, integrating it with interactive, online maps for the first time. Our Center crafted the maps so you could not only drag the bar left and right but also zoom in and out, click on the map to obtain detailed block-level population counts, and change the underlying basemap from a street view to an aerial image (via OpenLayers use of Microsoft’s Bing maps tiles), while also changing the transparency of the thematic Census patterns.

The latest iteration of CUNY’s Census maps continues to use the vertical slider but now incorporates this technique with two more comparison options. Each approach serves different purposes:

  1. The vertical slider bar provides a “before (2000) and after (2010)” visualization of change, either regionally or at the scale of a city neighborhood.
  2. The side-by-side comparison is ideal for lingering over a given area, especially at the local level, taking the time to absorb the differences in demographic patterns mapped with 2000 Census data on the left and 2010 on the right. We incorporated this approach specifically at the suggestion of the great interactive team at the Chicago Tribune, who have created some similar Census maps.
  3. The single-map 2010/2000 overlay is especially helpful for revealing the increase in diversity over a given area.

For example, you can zoom to Atlanta, GA on the single-map overlay and see the city’s predominantly Black population in 2000 surrounded by suburban Census blocks shaded dark blue, denoting a White population of 90% or more (see images below). As you transition the map from 2000 to 2010, the dark blue in the suburbs fades to a lighter shade (indicating a more mixed population demographically) coupled with more Census blocks shaded green, purple, and orange – each corresponding to communities that are now predominantly (even if only by a few percentage points) Hispanic, Asian, or Black respectively. This pattern is replicated in many of the urban regions featured at the website.

Atlanta & suburbs in 2000

Race/ethnicity change in Atlanta by 2010

Eventually we’ll be moving all this from pre-rendered tiles to vector tiles. CUR’s application architect Dave Burgoon contributed code he developed to TileStache to enable TileStache to produce AMF-based output for use in Flash-based interactive mapping applications. This will give us flexibility in mapping as many Census variables as needed, and also providing complete geographic coverage (hopefully down to the block level) nationwide. That’s the plan, anyway! Stay tuned.

Credits

Funding for much of the Center’s recent work on Census issues has been provided by the Building Resilient Regions Project of the John D. and Catherine T. MacArthur Foundation, the Hagedorn Foundation, as well as support from the CUNY Graduate Center and the City University of New York.

Several people provided feedback and helpful editorial suggestions on earlier versions of the maps and narrative. Though the materials at this site were prepared by the Center for Urban Research, those invdividuals improved our work. We greatly appreciate their contributions.

Slippy maps, meet before-and-after jQuery slider (introductions by OpenLayers)

Our team at the Center for Urban Research (at the CUNY Graduate Center) has launched a set of maps showing race/ethnicity patterns from 2000 and 2010 in major cities across the US.  The maps combine several mapping/web technologies that offer a new way of visualizing population change.  This post explains how we did it.

(And by popular demand, we’ve also included a map of Congressman Anthony Weiner’s district in relation to demographic change — you may have heard of him and his Twitter travails recently?)

Race/Ethnicity Change

Briefly, the maps show race/ethnicity change from 2000 to 2010 at the local level throughout major urban regions across the U.S.  So far we include New York City, Los Angeles, Boston, Chicago, Houston, and San Francisco.  (Others are coming soon.)

For our methodology and data analysis (and static maps), we provide that here.  For the mapping and web techniques, see below.

Reactions

So far we’ve received a pretty good response to our maps.  Here are some tweets posted recently:

  • @dancow (web journalist for ProPublica): Cool before/after map from CUNY’s urban research center showing NYC ethnic changes at the block level, from 2000-10.
  • @mericson (deputy graphics editor at NY Times): Nice block-level maps by @SR_spatial & CUNY Urban Research Center showing racial/ethnic change in NYC from 2000 to 2010.
  • @kelsosCorner (former Washington Post cartographer): Digging new 2010 Census plurality maps of NYC.
  • @albertsun (graphics editor at Wall St Journal): Coolest census map I’ve seen yet.
  • @PJoice (HUD employee; tweets are his own): This is the coolest map I have ever seen. Nice work by @SR_spatial and CUNY!
  • @MapLarge: I like how you can use the slider or move the map! Great Visualization!

Technical overview

The map uses the “before and after” technique that media websites have used for images of natural disasters.  We enhanced this technique by integrating it with interactive maps using OpenLayers, the open source mapping framework.  Now the slider works with two sets of overlapping, but perfectly aligned, maps from 2000 and 2010.

As it turns out, we didn’t set out to create an interactive version of these maps. In fact, we originally created static maps, but everyone we showed them to for feedback wanted the ability to zoom in/out and click on the map for more info.  So we developed the OpenLayers version. (And when I say “we”, that mainly means David Burgoon, CUR’s application architect, who I can’t say enough good things about.  I made the maps, and CUR’s Joe Pereira of the CUNY Data Service created the data sets, but Dave brought it all to life.)

OpenLayers enables us to introduce interactivity into the before-and-after images. Maps like these (to our knowledge) have not been available before — where you can move a slider back and forth while also zooming in/out and clicking on individual Census blocks for detailed information. You can also change the transparency of the thematic map layer, and switch between a street view and aerial view basemap.

It involved a good amount of work to integrate the slider technique with OpenLayers and also have two overlapping map instances working in tandem. The two maps need to appear as one, and this involves painstaking effort to ensure that the pixels on your screen are translated accurately to latitude/longitude coordinates in each of the separate but related interactive map instances, and the maps pan together seamlessly as you drag the slider left or right or move the map and it crosses the slider.

Mashup

In order to create the application, we used a mix of software applications, technologies, and techniques, summarized below:

  • We used the statistical software package SPSS to extract the Census block-level data for both years (see our methodology), allocate the 2000 data to 2010 blocks using the Census Bureau’s block equivalency files, and calculate the race/ethnicity plurality for each block.
  • We exported these SPSS files in DBF format and used ESRI’s ArcGIS Desktop to join the DBFs with 2010 TIGER Census block shapefiles.
  • ArcGIS Desktop was also used to create the choropleth maps (based on color schemes from ColorBrewer.org);
  • The map layouts were published as temporary web map services using ESRI’s ArcGIS Server. We used these to create pre-cached tiles (.PNG files) for the 2000 and 2010 maps, corresponding to zoom levels 4 through 10 using the now-standard Google-Microsoft map scales for online web maps. (Our application accesses the choropleth tiles as PNGs directly from the cache created by ArcGIS Server, rather than accessing the ArcGIS web map service in order to assemble the tiles. The latter approach would be too slow and would undermine the transition as you dragged the slider across the map.)
  • The slider technique was adapted from the jQuery plugin by www.catchmyfame.com.
  • OpenLayers provides all the map navigation and serving the maps themselves, modified with customized JavaScript code.
  • The basemap shown beneath the color-shaded map tiles is provided by Microsoft’s Bing map service. The street map and aerial image tiles from Bing are accessed directly via OpenLayers, rather than using the Bing API. This is a key reason we used Bing for these maps; if we used Google Maps as a basemap, we were limited to accessing Google Maps via Google’s API, which would have slowed map drawing times and undermined the slider effect.
  • For geocoding we use the Yahoo! Placefinder API.
  • Some browsers are not able to handle the before/after slider effect smoothly. In particular, Firefox and Safari perform poorly; the slider transition between one map to the other is not smooth. Microsoft’s Internet Explorer is adequate, but Google’s Chrome browser is best.

Data sources/issues

We used block-level data from the Census Bureau’s 100% population counts from the 2000 and 2010 decennial censuses (from Table P2 in the “PL-94-171” files for 2000 and 2010).

The Census Bureau’s block geography changed between 2000 and 2010 — new blocks were created, blocks were merged, and block boundaries were modified in many places. In order to compare population data from 2000 and 2010 using a common set of blocks, we used the Census Bureau’s block relationship file to allocate the 2000 population counts to 2010 geography.

When you’re viewing the map, it is best to use the maps and block-level data to understand trends over a larger area, even over several blocks. Be careful when viewing a specific block on its own. It covers a small area, and the Census Bureau may have made errors.

Credits

Funding for much of the Center’s recent work on Census issues has been provided by the Building Resilient Regions Project of the John D. and Catherine T. MacArthur Foundation, the Hagedorn Foundation, as well as support from the CUNY Graduate Center and the City University of New York.

Several people provided feedback and helpful editorial suggestions on earlier versions of the maps and narrative. Though the materials at this site were prepared by the Center for Urban Research, those invdividuals improved our work. We greatly appreciate their contributions.

@NYPLMaps & OASIS provide context for 18th century ship find

The www.OASISnyc.net mapping team has been working with the great folks at New York Public Library’s Map Division to integrate digitized historic maps aligned to the city’s current street grid.  But as we were working with Map Division staff to incorporate their maps, an amazing find at the World Trade Center construction site prompted us to speed up our work — earlier this month, construction workers unearthed an 18th century ship, largely intact, that likely hadn’t been disturbed for over 200 years.

Now you can display some key maps of lower Manhattan from the from the 18th and 19th centuries, view them in relation to the current street grid, and compare them to each other using OASIS’s dynamic transparency tool.  We added a brief tutorial at the OASIS wiki.

Now you can fade between current property maps …

… the 1775 Montresor map …

… the 1817 Poppleton map …

… and more.

We’ve also added the Viele map from 1874, and more are on their way.  This is all due to the groundbreaking NYPL “Map Rectifier” project.